A Novel Versatile Architecture For Autonomous Underwater Vehicle S Motion Planning And Task Assignment

Abstract Expansion of today’s underwater scenarios and missions necessitates the requestion for robust decision making of
the Autonomous Underwater Vehicle (AUV); hence, design an efficient decision making framework is essential for
maximizing the mission productivity in a restricted time. This paper focuses on developing a deliberative conflict-free-task
assignment architecture encompassing a Global Route Planner (GRP) and a Local Path Planner (LPP) to provide consistent
motion planning encountering both environmental dynamic changes and a priori knowledge of the terrain, so that the AUV
is reactively guided to the target of interest in the context of an uncertain underwater environment. The architecture
involves three main modules: The GRP module at the top level deals with the task priority assignment, mission time
management, and determination of a feasible route between start and destination point in a large scale environment. The
LPP module at the lower level deals with safety considerations and generates collision-free optimal trajectory between each
specific pair of waypoints listed in obtained global route. Re-planning module tends to promote robustness and reactive
ability of the AUV with respect to the environmental changes. The experimental results for different simulated missions,
demonstrate the inherent robustness and drastic efficiency of the proposed scheme in enhancement of the vehicles
autonomy in terms of mission productivity, mission time management, and vehicle safety.
Keywords Autonomous underwater vehicles, Autonomy, Decision making, Motion planning, Task assignment, Time
management, Mission management
1 Introduction
Autonomous Underwater Vehicles (AUVs) generally are capable of spending long periods of time carrying out underwater
missions at lower costs comparing manned vessels (Blidberg et al. 2001). An AUV needs to have a certain degree of
autonomy to carry out mission objectives successfully and ensure safety in all stages of the mission, as failure is not
acceptable due to expensive maintenance. Autonomous operation of AUV in a vast, unfamiliar and dynamic underwater
environment is a complicated process, specifically when the AUV is obligated to react promptly to environmental changes.
On the other hand, diversity of underwater scenarios and missions necessitates the requestion for robust decision making
based on proper awareness of the situation. Hence, an advanced degree of autonomy at the same level as human operator is
an essential prerequisite to trade-off within the problem constraints and mission productivity while manage the risks and
available time. At the lower level, it again must autonomously carry out the collision avoidance and similar challenges. To
this end, a hybrid architecture encompassing a Global Route Planner (GRP) and Local Path Planner (LPP), so that the AUV
is reactively guided to the target of interest in the context of uncertain underwater environment. With respect to the
combinatorial nature AUV’s routing and task allocation, which is analogous to both Traveling Salesman Problem (TSP)
and Knapsack problem, there should be a trade-off within the maximizing number of highest priority tasks with minimum
risk percentage in a restricted time and guaranteeing reaching to the predefined destination, which is combination of a
discrete and a continuous optimization problem at the same time. To provide a higher level of autonomy, deliberative
hybrid architecture has been developed to promote vehicles capabilities in decision-making and situational awareness. To
this end, the GRP module at the top level, simultaneously tends to determine the optimum route in terrain network cluttered
with several waypoints and prioritize the available tasks. The proposed time optimum global route may have several
alternatives, each of which consists of the proper sequence of tasks and waypoints. Another important issue that should be
taken into consideration at all stages of the mission is vehicles safety, which is extremely critical and complicated issue in a
vast and uncertain environment. The LPP module at lower level tends to generate the safe collision-free optimum path
between pairs of waypoints included in the global route encountering dynamicity of the terrain; hence the LPP operates in
context of the GRP module. Traversing the distance between two specific waypoints may take more time than expected due
to dealing with dynamic unexpected changes of the environment.
A Novel Versatile Architecture for Autonomous Underwater Vehicle’s
Motion Planning and Task Assignment
Somaiyeh Mahmoud.Zadeh, David M.W Powers,KarlSammut, Amir Mehdi Yazdani
S.M. Zadeh, D.M.W Powers, K.Sammut, A.M. Yazdani
School of Computer Science, Engineering and Mathematics, Flinders University,
Adelaide, SA, Australia
e-mail: somaiyeh.mahmoudzadeh@flinders.edu.au
e-mail: david.powers@flinders.edu.au
e-mail: karl.sammut@flinders.edu.au
e-mail: amirmehdi.yazdani@flinders.edu.au
S.M. Zadeh
School of Computer Science, Engineering and Mathematics, Flinders University,
Adelaide, SA, Australia

The loss of time in dealing with associated problem leads to requirement for a proper re-planning scheme. Hence, a
“Synchro-module” is provided to manage the lost time within the LPP process and improve the robustness and reactive
ability of the AUV with respect to the environmental changes. A variety of investigations have been carried out on
autonomous unmanned vehicle motion planning and task allocation discussed in the next section.
The paper is organized in the following subsections. The related works to this research is provided in section 2. In section
3, the problem is formulated formally. An overview of the genetic algorithm and global route planner paradigm are
presented in section 4. The particle swarm optimization and the local path planner are introduced in section 5. The
architectures evaluation is discussed in section 6. The discussion on simulation results is provided in Section 7. And, the
section 8 concludes the paper.
2 Related Works
Majority of AUV’s motion planning approaches are categorized into two groups that first group attempt to find a trajectory
that allows an AUV to transit safely from one location to another, while second group mostly concentrate on task allocation
and vehicles routing problem (VRP). Respectively, the previous attempts in this scope are divided into two main categories
as follows.
2.1 Vehicle Task Assignment-Routing
Effective routing has a great impact on vehicle time management as well as mission performance due to take selection and
proper arrangement of the tasks sequence. Various attempts have been carried out in scope of single or multiple vehicle
routing and task assignment based on different strategies. Karimanzira et al.(2014) presented a behaviour based controller
coupled with waypoint tracking scheme for an AUV guidance in large scale underwater environment. Iori and Ledesma
(2015) modelled AUVs routing problem with a Double Traveling Salesman Problem with Multiple Stacks (DTSPMS) for a
single-vehicle pickup-and-delivery problem by minimizing the total routing cost. Other methods also studied on efficient
task assignment for single/multiple vehicle moving toward the destination such as graph matching (Kwok et al. 2002),
Tabu search (Higgins 2005), partitioning (Liu and Shell 2012), simulated annealing (Chiang and Russell 1996), branch and
cut (Lysgaard et al. 2004), and evolutionary algorithms (Gehring and Homberger 2001). Martinhon et al.(2004) proposed
stronger K-tree approach for the vehicle routing problem. Zhu and Yang (2010) applied an improved SOM-based approach
for multi-robots dynamic task assignment. Alvarez et al.(2004) outlined a discrete method to grid the search space, then
applied Genetic Algorithm (GA) on the grids to generate an energy optimal route. Also some popular graph search
algorithms like A* (Al-Hasan and Vachtsevanos 2002; Pereira et al 2013) and Dijkstra (Eichhorn 2015) have been applied
to determine a grid or cell-based route from the start to the destination point. Liu and Bucknall (2015) proposed a three-
layer structure to facilitate multiple unmanned surface vehicles to accomplish task management and formation path
planning in a maritime environment, in which the mission is divided between vehicles according to general mission
requirement. Eichhorn (2015) implemented graph-based methods for the AUV ‘‘SLOCUM Glider’’ motion planning in a
dynamic environment. The author employed modified Dijkstra Algorithm where the applied modification and conducted
time variant cost function simplifies the determination of a time-optimal trajectory in the geometrical graph. Wang et al.
(2005) introduced an adaptive genetic algorithm to determine real-time obstacle-free route for AUV in a large-scale terrain
in presence of few waypoints. An energy efficient fuzzy based route planning using priori known wind information in a
graph-like terrain is presented by Kladis et al. (2011) for UAVs motion planning. M.Zadeh et al. (2015) investigated a
large-scale AUV routing and task assignment joint problem by transforming the problem space into a NP-hard graph
context, in which the heuristic search nature of GA and PSO employed to find the best series of waypoints. This work is
extended to semi dynamic networks while two biogeography-based optimization (BBO) and PSO meta-heuristic algorithms
are adopted to provide real-time optimal solutions (M.Zadeh et al. 2016-a). The traditional algorithms used for graph
routing problem have major shortcomings (notably high computational complexity) for real-time applications. Majority of
the discussed research, in particular, focus on task and target assignment and time scheduling problems without considering
requirements for vehicle safe deployment toward the destination.
2.2 Path/Trajectory Planning Approaches
Many strategies have been provided and applied to the AUV path-planning problem in recent years encountering
dynamicity of the terrain. Methods like D* or A* algorithms have been employed for AUV optimum path generation
(Carsten et al. 2006; Likhachev et al.2005). Another approach to solve this problem is the Fast Marching (FM) algorithm,
which uses a first order numerical approximation of the nonlinear Eikonal equation. Petres et al. (2005) provided FM-based
path planning to deal with a dynamic environment. This method is accurate but also computationally expensive than A*.
Later on, an upgraded version of FM known as FM* or heuristically guided FM is investigated on path planning problem
(Petres et al. 2007) that preserves the efficiency of the FM and accuracy of the A* algorithm, while apparently it is
restricted to use linear anisotropic cost to attain computational efficiency. In particular, the main drawback of these
methods is that their time complexity increases exponentially with increasing the problem space. Generally, the heuristic
grid-search based methods are criticized because their discrete state transitions, which restrict the vehicle’s motion to
limited directions.

Another solution for path planning is using the evolution based algorithms. Evolutionary algorithms are population based
optimization methods that can be implemented on a parallel machine with multiple processors to speed up computation
(Roberge et al. 2013). Relatively, they are efficient methods for dealing with path planning as a Non-deterministic
Polynomial-time (NP) hard problem (M.Zadeh et al. 2016-c; Ataei and Yousefi-Koma 2015), and fast enough to satisfy the
time restrictions of real-time applications. The Particle Swarm Optimization (Zheng et al. 2005) and Genetic Algorithm
(Nikolos et al.2003; Zheng et al. 2005; Kumar and Kumar 2010) are two popular types of optimization algorithms applied
successfully in path planning application. Fu et al. (2012) employed Quantum-based PSO (QPSO) for unmanned aerial
vehicle path planning, but implemented only off-line path planning in a static and known environment, which is far from
reality. Subsequently, this algorithm was employed by Zeng et al., (2014-a; 2014-b) for on-line AUV path planning in a
dynamic marine environment. A Differential Evolution based path planning is proposed by (M.Zadeh et al. 2016-b) for the
AUV operation in three-dimensional complex turbulent realistic underwater environment.
Although various path-planning techniques have been suggested for autonomous vehicles, AUV-oriented applications still
have several difficulties when operating across a large-scale geographical area. The computational complexity grows
exponentially with enlargement of search space dimensions. To cope with this difficulty, speed up the path planning
process and reduce memory requirement, the majority of conventional path planning approaches transmuted the 3D
environment to 2D space. However, a 2D representation of a marine environment doesn’t sufficiently embody all the
information of a 3D ocean environment and vehicle motion with six degrees of freedom. In large-scale operations it is hard
to estimate all probable changes of the terrain (obstacles/current behavior) and tracking the behavior of a dynamic terrain
beyond the vehicles sensor coverage is impractical and unreliable. A further problem is then; a huge amount of data about
the update of entire terrain condition must be computed repeatedly. This huge data load from environment should be
analyzed continuously every time path replanting is required, which is computationally inefficient and unnecessary as only
awareness of environment in vicinity of the vehicle such that the vehicle can be able to perform reaction to environmental
changes is enough. On the other hand, when the terrain is cluttered with multiple waypoints and the vehicle is requested to
carry out a specific sequence of prioritized tasks assigned to trajectories between waypoints, path planning is not able to
facilitate the vehicle to carry out the task assignment considering graph routing restrictions; thus, a routing strategy is
required to handle graph search constraints and facilitate the task assignment. The routing strategies are not as flexible as
path planning in terms coping with environmental prompt changes, but they give a general overview of the area that AUV
should fly through (general route), which means cut off the operation area to smaller beneficial zones for deployment. To
summarize above discussion, existing approaches mainly cover only a part of the AUV routing task assignment problem, or
path planning along with obstacle avoidance as a safety consideration.
2.3 Research Contribution
To carry out the underwater missions in large scale environment in presence of severe environmental disturbances, a hybrid
architecture with re-planning capability is being developed to cover shortcomings and to take advantages of both path and
route planning strategies, which is a significant change to accelerate the computational runtime. The proposed system is
designed in separate modules running concurrently including a global route planner (GRP) at top level with higher level of
decision autonomy, and the local path planner (LPP) at lower level, to autonomously carry out the collision avoidance. A
constant interaction is flowing between these two modules by back feeding the situational awareness of the surrounding
operating filed form the LPP to the GRP for making a decision on requisitions of re-planning. Hence, the third module
“Synchro-module” is performed to manage the lost time within the LPP/GRP process and reactively adapt the system to the
last update of environment and decision parameters (e.g. remaining time).This process continues iteratively until the AUV
reaches the end point. A significant benefit of such detached design is that different methods employed by main modules
and even sub modules can be easily replaced with new methods or get upgraded without requiring any change in whole
structure of the system. This issue specifically increases the reusability of the control architecture and specifically eases
updating/upgrading AUV’s maneuverability at all times.
Obtaining the exact optimum solution is only possible for the specific case where the environment is completely known and
no uncertainty exists and the environment modelled by this research corresponds to a dynamic environment with high
uncertainty. Moreover, the task and route planning problem is a generalization of both the knapsack and TSP problems and
meta–heuristics are the fastest approach introduced for solving NP-hard complexity of these problems and have been
shown to produce solutions close to the optimum with high probability (Iori and Ledesma, 2015; Besada-Portas, et al.,
2010). On the other hand, precise and concurrent synchronization of the higher and lower level modules is the primary
requirement for preserving the consistency, stability and cohesion of this real-time system in meeting the specified
objectives. The most critical factor for both GRP and LPP operation is having a short computational time to provide fast
concurrent synchronization between modules while balancing the constraints. Maintaining comparably fast operation for
each component of the main architecture is necessary to prevent any of them from dropping behind the others. Any such a
delay disrupts the routine flow and concurrency of the entire system, and adding NP computational time into the equation
would itself render a solution suboptimal. While the solutions proposed by any meta–heuristic algorithm do not necessarily
correspond to the optimal solution, it is more important to control the time, and thus we rely on the previously mentioned
ability of meta-heuristic algorithms, including GA and PSO algorithms as employed in the GRP and LPP modules, to find
correct and near optimal solutions in competitive time (real and CPU).

3 Problem Formulation
The main goal of AUV operation is to complete mission objectives while ensuring the vehicle’s safety at all times.
Appropriate vehicle routing and path planning strategy along with efficient synchronization between models maximizes the
achievements of a mission. A mathematical formulation of the problem is provided in the following subsections.
3.1 Mathematical Representation of the Operation Terrain
The ocean environment is modelled as a three-dimensional terrain Γ3D covered by uncertain, static and moving obstacles
comprising several fixed waypoints. An underwater mission is commenced at a specified starting point WP1
:(x1,y1,z1) and it
is terminated when the AUV reaches to a predefined destination point (dock for example) at WPD
:(xD,yD,zD). The
waypoints’ location are randomized according to a uniform distribution of ~U(0,10000) for WPi
x,y and ~U(0,100) for WPi
z.
Waypoints in the terrain are connected with edge like qi from a set of q={q1,…,qm}, where m is the number of edges in the
graph.
Each edge of the network like qi is assigned with a specific
task from a set of Task={Task1,…,Taskk} k∈ m in advance.
Each task has a value like ρi from a limited set of
ρ={ρ1,…,ρk} that represents its priority comparing other
tasks, and completion time of δT regardless of required
time for passing the relevant edge. Each task also has a risk
percentage of ξT regardless of terrain hazards and risks.
Exploiting a priori knowledge of the underwater terrain,
the initial step is to transform the problem space into a
graph problem as depicted in Fig.1; then the GRP module
tends to find the best fitted route to the available time,
involving the best sequence of waypoints.








ij
ij
ij
ij
T
T
T
q
ij
ij
ij
Task
t
d
q
ξ
δ
ρ
:
: (1)
The underwater variable environment poses several challenges for AUV deployment, such as dealing with static, dynamic
and uncertain obstacles and ocean current. Ocean current is considered as static current that affect floating obstacles.
3.2 Mathematical Representation of Static/Dynamic Obstacle
In terms of collision avoidance, obstacle’s velocity vectors and coordinates can be measured by the sonar sensors with a
certain uncertainty modelled with a Gaussian distributions. The state of obstacle(s) continuously measured and sent to state
predictor to provide the estimation of the future states of the obstacles for the LPP. The state predictor estimates the
obstacles behaviour during the vehicles deployment in specified operation window. Four different type of obstacles are
conducted in this study to evaluate the performance of the proposed path planner, in which an obstacle is presented by three
components of position, radius and uncertainty Θ(i):(Θp,Θr,,ΘUr). The obstacle position Θp initialized using normal
distribution of ~𝒩(0,σ2
) bounded to position of start waypoint WPa
x,y,z and position of target waypoint WPb
x,y,z , where
σ2
≈Θr. Therefore the obstacles position Θp on WPa
x,y,z<Θp<WPb
x,y,z has a truncated normal distribution, where its probability
density function defined as follows:
  i
r
b
z
y
x
a
z
y
x
i
p WP
WP 


 ,
,
,
, , (2)
  


















 i
r
i
p
a
z
y
x
i
r
i
p
b
z
y
x
i
r
i
p
b
z
y
x
a
z
y
x
i
r
i
p
WP
WP
WP
WP
f
,
,
,
,
2
,
,
,
,
)
,
,
,
0
;
( (3)
The obstacle radius initialized using a Gaussian normal distribution of ~(0,100). Different type of considered obstacles in
this research explained in the rest.
[1] Static Known Obstacles: The location of these obstacles is known in advance and their position can be obtained
from offline map. No uncertainty growth considered for position of these obstacles (e.g. known rocks in the terrain).
[2] Static Obstacles with Certain Growth of Uncertainty: These obstacles classified as the Quasi-static obstacles that
usually known as no flying zones. The obstacles in this category has an uncertain radius varied in a specified bound
with a Gaussian normal distribution ~(Θp,σ0), where the value of Θr in each iteration is independent of its previous
value.
[3] Self-Motivated Moving Obstacle: Self-motivated moving obstacle, is the third type that has a motivated velocity that
shift it from position A to position B. Therefore, its position changes to a random direction with an uncertainty rate
proportional to time, given in (4).
)
,
(
)
1
(
)
( 0

p
p
p U
t
t 




 (4)
[4] Moving Obstacles with Propagated Uncertainty in Position and Radius: Another type of considered obstacle is
self-motivated moving obstacle that affected by current force and moving with a self-motivated velocity to a random
WP-1
Start
WP-2
WP-3
WP-4
WP-5
WP-6
WP-7
WP-8
WP-9
WP-10
WP-11
WP-D
Target
WP-12
WP-13
WP-14
WP-15
WP-16
WP-17
WP-18
q3
q2
q1
q5
q6
q7
qm
q4
Fig. 1. A graph representation of operating area covered by waypoints

direction, denoted by (4) and (5). Here, the effect of current presented by uncertainty propagation proportional to
current magnitude UR
C
=|VC|~(0,0.3) that radiating out from the center of the obstacle in a circular format.

































 
)
(
0
0
,
1
1
0
,
1
0
0
0
1
0
0
)
(
1
)
1
(
)
(
3
2
1
3
)
1
(
2
1
t
U
B
B
t
U
B
B
X
B
t
B
t
C
R
C
R
Ur
t
r
r
(5)
where ΘUr ~σ is the rate of change in objects position, and X(t-1)~𝒩(0,σ0) is the Gaussian normal distribution that
assigned to each obstacle and gets updated in each iteration t.
3.3 Mathematical Representation of the AUV Routing Problem
The mathematical representation of the AUVs’ routing problem should be simple enough to avoid unnecessarily expensive
computations. The generated route should be applicable and logically feasibleaccording to feasibility criteria’s (given in
section 4), and represented by Ri=(x1,y1,z1,…,xi,yi,zi,…,xD,yD,zD), where (xi,yi,zi)is the coordinate of any arbitrary waypoint in
geographical frame. The goal is to find the optimum route covering the maximum number of highest priority tasks with
smallest risk percentage in a time interval that battery’s capacity allows. The problem involves multiple objectives that
should be satisfied during the optimization process. In the preceding discussion, the mathematical representation of the
AUV route planning problem in Γ3Dterrain is described as follows:
2
2
2
)
(
)
(
)
( i
j
i
j
i
j
ij
z
z
y
y
x
x
d 




 (6)
ij
T
AUV
ij
ij
V
d
t δ

 (7)
}
1
,
0
{
,
δ
0
0











 





l
V
d
lq
t
lq
T
n
i
j
i
ij
T
AUV
ij
ij
n
i
j
i
ij
ij
Route (8)
}
1
,
0
{
,
max
ξ
ρ
0
0
0











 








l
w
lq
w
lq
lq
W
n
i
j
i
ij
ij
n
i
j
i
ij
ij
n
i
j
i ij
ij
ij
R (9)
 
  Available
Route
Available
n
i
j
i
ij
T
AUV
ij
ij
Available
Route
T
T
t
s
T
V
d
lq
T
T




















 


max
.
.
δ
min
min
0
(10)
where TRoute is the required time to pass the route, TAvailable is the total mission time, l is the selection variable, tij is the
required time to pass the distance dij between two waypoint of WPi
and WPj
along with task completion time δTij. ρTij and
ξTij are the priority value and risk percentage of the task, respectively. The next step is generating time/distance optimum
trajectory in smaller scale between each pair of waypoints in optimum global route.
3.4 Path Planning Problem Formulation
The path planner should generate time optimum collision-free local path ℘i (shortest path) between specific pair of
waypoints through a spatiotemporal underwater environment in the presence different types of uncertain obstacles. The
resultant path should be safe and flyable (feasible). The operation terrain modelled as a time varying environment covered
by uncertain, static and moving obstacles Θ mentioned above. The dimension of the operating window depends on distance
between two nominated waypoints. The proposed path planner in this study, generates the potential trajectories using B-
Spline curves captured from a set of control points like ϑ= {ϑ1, ϑ2,…,ϑi,…,ϑn} in the problem space with coordinates of
ϑ1:(x1,y1,z1),…,ϑn:(xn,yn,zn), where n is the number of corresponding control points. These control points play a substantial
role in determining the optimal path. The mathematical
description of the B-Spline coordinates is given by:









n
i
K
i
i
n
i
K
i
i
n
i
K
i
i
t
B
z
t
Z
t
B
y
t
Y
t
B
x
t
X
1
,
1
,
1
,
)
(
)
(
)
(
)
(
)
(
)
(
(11)
where Bi,K(t) is the curve’s blending functions, t is the time step,
and K is the order of the curve and shows the smoothness of the
curve, where bigger K correspond to smoother curves represented
in Fig.2. For further information refer to [24].
ϑ1
ϑ2
ϑ3
ϑ4
ϑ5
ϑ6
ϑ7
ϑ8
ϑ9
ϑ1
ϑ2
ϑ3
ϑ4
ϑ5
ϑ6
ϑ7
ϑ8
ϑ9
A B
Fig. 2. Quadratic B-Spline curve by control points, where in (A),
K = 3.5 and in (B), K = 6.

The path-travelled time Tpath-flight between two waypoints for ℘i should be minimized. The ocean current velocity is assumed
to be constant. The path planner is applied in a small-scale area, and the AUV considered to have constant thrust power;
therefore, the battery usage for a path is a constant multiple of the time and distance travelled. Performance of the generated
trajectory is evaluated based on overall collision avoidance capability and time consumption, which is proportional to
energy consumption and travelled distance. The path planner’s cost function is detailed in section 6.2.
To cope with the probable challenges of the dynamic environment, the LPP repeatedly calculates the trajectory between
vehicles current position and its specified target location. The path absolute time tij is calculated at the end of the trajectory.
Then tij is added to corresponding task completion time δTask and the computation time Tcompute. Total value of this
summation Tpath-flight gets compared to expected time TExpected for passing the distance between specified pair of waypoints.
If Tpath-flight gets smaller value than TExpected, it means no unexpected difficulty is occurred and vehicle can continue its travel
along the current global route. However, if Tpath-flight exceeds the TExpected, it means AUV faced a challenge during its
deployment. Obviously, a specific amount of battery and time TAvaliable is wasted for handling collision avoidance, so the
TAvaliable should be updated. In such case, the current route cannot be optimum anymore due to loss of time and re-planning
is required according to mission updates.




 compute
Task
ij
flight
path T
t
T ij
 (12)
if Tpath-flight ≤ TExpected
Continue the current optimum route Rj
else if Tpath-flight > TExpected
Update TAvaliable and operation network
Re-plan a new route according to mission updates
It would be computation and time dissipation for an AUV to pass a specific edge (distance) for more than ones that means
repeating a task for several times. Hence, if re-routing is required at any situation, the TAvaliable gets updated; the passed
edges get eliminated from the operation network (so the search space shrinks); and the location of the present waypoint is
considered as the new start position for both LPP and GRP. Afterward, the GRP tends to find the optimum route based on
new information and updated network topology. The process of combinational GRP, LPP, re-planning process and
schematic representation of proposed control architecture is provided in Fig.3 and Fig.4, respectively.
2
10
S
1
3
4
7
8
9 D
5
11
12
13
14
15
16
17
19
18
6
2
10
S
1
3
4
7
8
9 D
5
11
12
13
14
15
16
17
19
18
6
2
10
S
3
4
7
8
9 D
5
11
12
13
14
15
16
17
19
18
6
Fig.3. Graph representation of operating area covered by waypoints and route/ trajectory planning, re-planning process

Given a candidate route in a sequence of
waypoints (e.g. initial optimum route: {S-1-
7-3-4-5-6-9-10-13-17-19-D} in Fig.3) along
with environment information, the LPP
module provides a trajectory to safely guide
the vehicle through the waypoints. During
deployment between two waypoints, the
LPP can incorporate any dynamic changes
of the environment. The provided trajectory
is then sent to the guidance controller to
generate the guidance commands for the
vehicle. After visiting each waypoint, the re-
planning criteria (given in equation (12)) is
investigated. If re-planning is required,
the“Synchro-module” updates the operation
graph and mission available time; and the
controller recalls the GRP to provide new
optimum route based on mission
updates(e.g. new optimum route: { 9-8-10-
16-D}). This process continues until mission
ends and vehicle reaches the required
waypoint.
The trade-off between available mission time and mission objectives is critical issue that can be adaptability carried out by
GRP. Hence, the main synchronous architecture should be fast enough to track environmental changes, cope with dynamic
changes, and carry out prompt re-planning. To handle the complexity of NP-hard graph routing and task allocation problem,
the GRP takes the advantages of genetic algorithm to find an optimum global route for the underwater mission. In the LPR
module, the particle swarm optimization algorithm carries out path planning between each pair of the waypoints, which is
efficient and fast enough in generating collision-free optimum trajectory in smaller scale.
4 Overview of Genetic Algorithm and Global Routing Process
The Genetic Algorithm (GA) is a particular type of stochastic
optimization search algorithm represents problem solving technique
based on biological evolution. GA has been extensively studied and
widely used on many fields of engineering. It searches in a population
space that each individual of this population is known as chromosome.
Its process starts with randomly selecting a number of feasible solutions
from the initial population. A fitness function should be defined to
evaluate quality of solutions during the evolution process. New
population is generated from initial population using the GA operators
like selection, crossover and mutation. Chromosomes with the best
fitness value are transferred to next generation and the rest get
eliminated. This process continues until the chromosomes get the best
fitted solution to the given problem (Sivanandam and Deepa 2008). The
average fitness of the population gets improved at each iteration by
adaptive heuristic search nature of the GA. The GRP module deals with
finding the optimal route through the operating graph, where the input
to this module is a group of feasible generated routes involving a
sequence of nodes with same starting and ending points that are encoded
as chromosomes. The operation is terminated when a fixed number of
iterations get completed, or when no dramatic change observed in
population evolution. The process of the GA algorithm is proposed by
the flowchart given in Fig.5.
Developing a suitable coding scheme and chromosome representation is
the most critical step of formulating the problem in GA framework.
Hence, efficient representation of the routes and encoding them
correctly into the chromosomes has direct impact on overall
performance of the algorithm and optimality of the solutions. The
process of The GA-based algorithm for route planning and task priority
assignment is summarized in following steps.
4.1 Chromosome Encoding (Initialize Chromosome/Route Population)
A chromosome in the proposed GA corresponds to a feasible route including a sequence of nodes. The first and last gene of
the chromosomes always corresponds to the start and destination node with respect to the topological information of the
Mission
Route
Planner
Local
Path
Planner
Task Priority
Assignment
Time
Management
Optimum Route
Determination
Task Risk/Cost
Assesment
Sensory Data
Situation Awareness
Environmental
Estimator
Dynamic/Static
Obstacle Detected
Current Disturbance
Detected
Action Selection
Collision Avoidance
Coping with Current
Disturbance
Trajectory
Vehicle
Controller(s)
Actuators
Route:{Sequence of nodes}
Mission Update &
Replanning
Fig.4.Proposed control architecture including AUV’s cooperative GRP and LPP scheme
Start
Feasible Route
Generation
Initialization of Routes Using
Chromosomes
Selection
(The most fitted chromosomes selected)
Mutation
Crossover
Feasible?
End
Evaluate Route Feasibility
No
Yes
Fitness Evaluation
Delete from
Population
Add to Next Generation
Yes
No
Termination
Criterion Is
Attained
Fig.5. Process of the GA algorithm

graph. Chromosomes take variable length, but limited to
maximum number of nodes included in the graph, since it is
never required for a route to include nodes more than whole
number of nodes in the graph. The resultant solution from
both GA should be feasible and valid according to criteria’s
given in Fig.6. A priority based strategy is used in this
research to generate feasible routes [34]. For this purpose,
some guiding information is added to each node at the
initial phase. The priority vector initialized randomly. The
nodes are selected based on their corresponding value in
priority vector and Adjacency relations. Using Adjacency
matrix prevents appearance of non-existed edges of the
graph. To prevent generating infeasible routes some
modifications are applied as follows:
 Each node take positive or negative priority values in the specified range of [-100,100]. The selected node in a route
sequence gets a large negative priority value that prevents repeated visits to a node. Then, the visited edges get
eliminated from the Adjacency matrix. So that, the selected edge will not be a candidate for future selection. This
issue reduces the memory usage and time complexity for large and complex graphs.
 To satisfy the termination criteria of a feasible route, if the route ends with a non-destination node and/or the length
of the route exceeds the number of existed nodes in the graph, the last node of the sequence get replaced by index of
the destination node. This process keeps the generated route in feasible (valid) space.
Figure 7 presents an example of the route generating process according to a sample Adjacency matrix(Ad) of a graph and a
random priority array (Ui). To generate a feasible route in a graph with 18 nodes based on topological information, the first
node is selected as the start position. Then from Adjacency matrix the connected nodes to node-1 are considered. In graph
shown in Fig.7, this sequence is {2,3,4,5}. The node with the highest priority in this sequence is selected and added to the
route sequence as the next visited node. This procedure continue until a legitimate route is built (destination visited).
Ad Example of adjacency matrix for a graph with 18 nodes
n Node index where n=1 is the start and n=18 is the destination point
Rk
Ui Partial route corresponding to the priority vector of a route including k nodes.
Ui Priority array (random no repeated vector in range of [-100,100])
1
2
3
4
5
6
7
8
9
10
11
12 13
14
15
16
17
18














































0
1
0
1
0
1
0
1
0
0
0
0
0
0
0
0
0
0
1
0
0
0
1
0
0
1
0
1
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Ui:{44,-38,76,-78,18,47,42,61,66,-69,-25,-93,58,-15,11,-43,81,97}
Fig.7. Sample of feasible route generation process based on topological information (priority vector Ui and Adjacency matrix Ad) ref p1
4.2 Selection
Selecting the parents for crossover and mutation operations is another step of the GA algorithm that plays an important role
in improving the average quality of the population in the next generation. Several selection methods exist for this purpose
such as roulette wheel, ranks selection, elitist selection, scaling selection, tournament selection, etc. The roulette wheel
selection has been conducted by current research, wherein the next generation is selected based on corresponding cost
value, then the wheel divided into a number of slices and the chromosomes with the best cost take larger slice of the wheel.
4.3 Crossover Operation
Crossover is a GA operator that shuffles sub parts of two parent chromosomes and generate offspring that includes some
part of both parent chromosomes. Many types of crossover techniques have been suggested since now. Generally, they can
be categorized in to two main types of single point and multipoint crossover methods. In a single point crossover, only one
crossing site exists, while in multipoint crossover, multiple sites of a pair of parents are selected randomly to get shuffled.
The single point crossover method is simple, but it has some drawbacks like formation of loop (cycles) when applied for
Fig.6. Route feasibility criteria

routing problem. Therefore, to prevent such an issue it is required to use more advanced type of multipoint crossover
method like Order crossover (OX), Cycle crossover (CX), Partially Matched (PMX), Uniform crossover (UX) and so on
(Sivanandam and Deepa 2008). Discussion over which crossover method is more appropriate still is an open area for
research. Current research took advantages of uniform crossover, which uses a fixed mixing ratio among pair of parents.
The gens are swapped with a fixed probability that usually is considered as 0.5. This method is extremely useful in
problems with a very large search space in those where recombination order is important. An example of uniform crossover
is given below:
Parent-1: WPS WP3 WP14 WP18 WP8 WP4 WP7 WP17 WPD
Parent-2: WPS WP5 WP9 WP6 WP11 WP16 WP13 WP10 WP12 WP19 WPD
Offspring-1: WPS WP5 WP14 WP6 WP11 WP4 WP13 WP10 WPD
Offspring-2: WPS WP3 WP9 WP18 WP8 WP16 WP7 WP17 WP12 WP19 WPD
Fig.8. Example of uniform crossover
If the length of the chromosome is smaller than four, the crossover operator gets in to trouble of finding crossing site and
swapping. So the chromosomes with length less than four gens get discarded from the crossover operation. The offspring
gets eliminated if it does not correspond to a feasible route.
4.4 Mutation Operation
Mutation is another GA operator for generating the new population. This operator provides bit flipping, insertion,
inversion, reciprocal exchange, etc., for altering parents (Sivanandam and Deepa 2008). Current research applies a
combination of three inversion, insertion, and swapping type of mutation methods, explained in Fig.9. All these three
methods preserve most adjacency information. In order to keep the new generation in feasible space, the mutation is
applied on gens between the first and last gens of the parent chromosome that correspond to start and destination point.
Both of the mutation and crossover operations enhance the rate of convergence.
WPS WP6 WP11 WP19 WP5 WP9 WP13 WP8 WPD → WPS WP11 WP6 WP19 WP5 WP9 WP13 WP8 WPD
Fig.9. Respectively the insertion, swap, and inversion mutations
4.5 Termination Criteria
The termination of the GA process is defined by completion of the maximum number of iterations, appearance of no
change in population fitness after several iterations, and approaching to a stall generation.
4.6 Route Optimality Evaluation
The most important step in finding an optimum route by GA is forming an efficient cost function, so that the algorithm
tends to compute best fitted solution with minimum cost value. The problem involves multiple objectives that should be
satisfied during the optimization process. One approach in solving multi-objective problems is using multi-objective
optimization algorithms. Another alternative is to transform a multi-objective problem into a constrained single-objective
problem. The cost function for the route planner is defined as particular combination of weighted factors that are required
to be maximized or minimized (given in section 6).
5 Overview of PSO and its Process on Path
Planning
The PSO is one of the fastest optimization
methods for solving variety of the complex
problems and widely used in past decades.
The argument for using PSO in path planning
problem is strong enough due to its superior
capability in scaling well with complex and
multi-objective problems. The process of PSO
is initialized with a population of particles.
Each particle involves a position and velocity
in the search space that get updated iteratively
using equation (13). Each particle has
memory to preserve the previous state values
of best position PP-best
, as and the global best
position P G-best
. The current state value of the
particle is compared to the PP-best
and P G-best
in each iteration. More detail about the
algorithm can be found in related references
(Kennedy and Eberhart 1995). Fig .10. PSO optimal path planning pseudo code

   
)
(
)
1
(
)
(
)
1
(
)
1
(
)
1
(
)
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(
)
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(
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( 2
2
1
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t
t
t
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P
r
c
t
t
P
r
c
t
t
i
i
i
i
best
G
i
i
best
P
i
i
i



















 

(13)
where c1and c2 are acceleration coefficients, χi and υi are particle position and velocity at iteration t. Pi
P-best
is the personal
best position and Pi
G-best
is the global best position. r1 and r2 are two independent random numbers in[0,1]. ω exposes the
inertia weight and balances the PSO algorithm between the local and global search. Each particle in the swarm assigned by
a potential path. The position and velocity parameters of the particles correspond to the coordinates of the B-spline control-
points ϑi that utilises in path generation. The path planning is an optimization problem that aims to minimize the travel
distance/time and avoid colliding obstacle(s). As the PSO algorithm iterates, every particle is attracted towards its
respective local attractor based on the outcome of the particle’s individual and swarm search results. The fitness of each
generated path (particle) gets evaluated according to the fitness/cost functions discussed in section 6. All control points
ϑ={ϑ1, ϑ2,…, ϑi,…, ϑn} should be located in respective search region constraint to predefined bounds of βi
ϑ=[Ui
ϑ,Li
ϑ]. If
ϑi
:(xi
,yi
,zi
) represent one control point in Cartesian coordinates in tth
path iteration, Li
ϑ is the lower bound; and Ui
ϑ is the
upper bound of all control points at (x-y-z) coordinates given by (14):
],
,...,
,...,
,
[
],
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[
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[
],
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,
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[
],
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[
2
1
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(
2
1
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(
1
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(
1
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)
(
1
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(
n
i
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n
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n
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i
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z
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U
y
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y
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U
x
x
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x
U
z
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L
y
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y
L
x
x
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L

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
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(14)
With respect to given relations (14), each control point is generated from (15):
)
(
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(
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(
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t
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t
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U
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t
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
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





(15)
where (x0, y0, z0) and (xn, yn, zn) are the position of the start and target points in the LPP, respectively. The pseudo code of
the PSO algorithm and its mechanism on path planning process is provided in Fig.10.
6 Architecture Evaluation
AUV starts its mission from start point and should serve sufficient number of tasks to reach the destination on-time. Given
a candidate route in a sequence of waypoints along with environmental information, the LPP module provides a trajectory
to safely guide the vehicle through the waypoints. The resultant local path should be time optimum, safe and flyable
(feasible). It shouldn’t cross the forbidden area covered by obstacles Θ (defined using eq 2-5). If the ϑ={ϑ1, ϑ2,…,ϑi,…, ϑn}
is the sequence of control points along each arbitrary local path from set of ℘={℘1,℘2,…}, the path ℘i gets evaluated by a
cost function Cost℘ defined based on travel time ti≈Tpath-flight required to pass the path segments. The route cost has direct
relation to the passing distance among each pair of selected waypoints. Hence, the path cost Cost℘ for any optimum local
path get used in the context of the GRP. The model is seeking an optimal solution in the sense of the best combination of
path, route, and task cost. The route function CostRoute gets penalty when the TRoute for a particular route Ri exceeds the
TAvailable. Thus, the provided route and path is evaluated as follows.







 

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s
s
s z
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,
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2
)
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,
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 (16)
  

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
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1
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AUV
t
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t
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D
n
i
flight
path
V
t
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i
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

 (17)


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N
Ur
r
p
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j
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j
flight
path
j
and
t
j
t
s
T
Cost
i
)
,
,
(
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}
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0
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.
.
)
min(
3

(18)
  Available
Route
i
Available
n
i
j
i
ij
T
AUV
ij
ij
Route
Available
Route
Route
T
T
R
t
s
l
T
V
Cost
lq
Cost
T
T
Cost











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
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

max
.
.
}
1
,
0
{
,
0
 (19)
}
1
,
0
{
,
0







 


l
lq
Cost
n
i
j
i Route
Route
ij
Task


(20)
After visiting each waypoint, the re-planning criterion is investigated. A computation cost encountered any time that re-
planning is required. Thus, the total cost for the model defined as:
  


 
r
compute
Route
Task
Total T
Cost
Cost
Cost
Cost
1
2
1 
 (21)

where Tcompute is the time required for checking the re-planning criteria and computing the new optimum route, and r is the
number of repeating the re-planning procedure. NΘ is number of obstacles. Θp, Θr, and ΘUr are obstacle position, radius and
uncertainty, respectively. φ1 and φ2 are two positive numbers that determine amount of participation of CostTask and CostRoute
on calculation of total cost CostTotal. Giving the appropriate value for coefficients of engaged factors in the cost function has
a significant effect on performance of the model.
7 Results and Discussion
The main purpose of this research is evaluating the performance of entire architecture in terms of increasing mission
productivity (task assignment and time management), while guaranteeing vehicles safety during the mission. To verify the
efficiency of the proposed architecture, the performance of each module is investigated individually from top to bottom
layer and explained in following subsections.
8 Simulation Results and Discussion
The main purpose of this research is evaluating the performance of entire architecture in terms of increasing mission
productivity (task assignment and time management), while guaranteeing vehicles safety during the mission. To verify the
efficiency of the proposed architecture, the performance of each module is investigated individually from top to bottom
layer and explained in following subsections.
8.1 Simulation Results for Methods Used in GRP Module
At the top level of the architecture, a configurable GRP module is developed in order to find the most productive optimum
global route between start and destination points. Two different algorithms are adapted and tested by module to evaluate
the optimality of the global route. Several different criteria are embedded to keep the generated routes concentrated to the
feasible solution space, which comprehensively reduces the memory usage and time complexity of the searching process.
The global route gives a general overview of the area that AUV should fly through by cutting off the operation area to
smaller beneficent zone for vehicle’s deployment. The GRP operates based on offline map information and does not deal
with dynamic changes of terrain. Assumptions for GRP module are given below.
i) In this study, it is assumed that vehicle is moving in a 3D environment covered by multiple fixed waypoints that one of
them is the start point which vehicle starts its mission from that and one of them is destination point (dock for example)
that vehicle should reach to that point within mission available time. This information represented in a graph form
terrain.
ii) Tasks assigned to edges of the graph in advance. Each task involves three parameters of priority, risk percentage and
required completion time. AUV is moving with static velocity and is requested to serve maximum number of tasks in
mission time.
To evaluate efficiency the GRP module for a single vehicle routing problem, its performance in task allocation, time
management, productivity of the mission, real-time performance, and other factors are tested using two different
evolutionary strategies of GA and Imperialist Competitive Algorithm (ICA), which both are popular meta-heuristic
optimization methods in solving NP hard problems. More detail about ICA optimization algorithm can be found in
(Movahed et al. 2011; Soltanpoor et al. 2013). A number of performance metrics have been investigated to evaluate the
quality/optimality of the proposed solutions in different network topologies. One of these metrics is the reliability
percentage of the route representing chance of mission success, which is combination of route validity to time restriction
and feasibility criteria. Other metrics involve the number of completed tasks, total obtained weight, total cost, and the time
optimality of the generated route with respect to complexity of the graph. These metrics altogether perform single vehicles
mission productivity in a specific time interval. The ICA and GA configured with the same initial conditions of 150
iterations and 100 populations. The performance of both algorithms is tested on two graphs with the same complexities, one
with 50 nodes and another one with 100 nodes, presented in Table 1 and Fig.11.
Table 1. Statistical analyzing of route evaluation for two different graph complexity for both ICA and GA
Performance Metrics Topology 1 Topology 2
Number of Nodes 50 Nodes 100 Nodes
Number of Edges 1197 4886
Algorithm ICA GA ICA GA
CPU Time(sec) 18.4 9.5 20.2 17.53
Best Cost 0.056 0.034 0.047 0.029
Available Time(sec) 25200 (7h) 25200 (7h) 25200 (7h) 25200 (7h)
Route Time(sec) 22218 23981 25212 23875
Total Distance 55329 61812 669857 63417
Total Weight 45 54 47 57
N-Tasks 16 19 18 23
Reliability
Violation 0.00 0.00 0.0043 0.00
Feasibility Yes Yes Slightly late Yes

Fig.11. (a) GA and ICA cost variations in 150 iterations, (b) GA and ICA total computational time in 150 iterations
From simulation results in Table.1, it is noted that in all cases route travelling time obtained by GA is smaller than total
available time and violation value for all solutions is equal to zero that confirms feasibility of the produced route, which
means GA acts according to defined constraints. It is clear from Table.1 and Fig.11 that GA acts more efficiently in terms
of minimizing cost value and computation time comparing to ICA. The provided results also confirms the superior
performance of the GA based route planner in terms of increasing mission productivity by maximizing total obtained
weight and number of covered tasks by taking maximum use of the available time (as TRoute considerably approaches the
TAvailable). Indeed it is evident from Table.1 and Fig.11 that the performance of both algorithms is relatively independent of
both size and complexity of the graph, as this is a challenging problem for other deterministic algorithms. Hence, the
evolutionary algorithms are suitable to produce optimal solutions quickly for real-time applications.
To evaluate the stability and reliability of the employed algorithms in terms of total route time, CPU time, distance, and
total obtained weight, 100 execution runs are performed in a Monte Carlo simulation, presented by Fig.12.
Fig.12. Comparison of stability of GA and ICA in terms of satisfying given performance metrics based on Monte Carlo simulation
The number of graph nodes is fixed on 20 waypoints for all Monte Carlo runs, but the topology of the graph was changed
randomly based on a Gaussian distribution on the problem search space. The time threshold (TAvailable) also fixed on
2.52×104
(sec). Fig.12 compares the functionality of GA and ICA in dealing with problem’s space deformation and
quantitative measurement of four significant mission’s metrics of travel time, CPU time, total weight, and total traveled
distance. As indicated in Fig.12, GA has superior performance and shows more consistency in its distribution comparing to
the generated solutions by ICA algorithm. The GA reveals robust behavior to the variations and meet the specified
constraint.
8.2 Simulation Results for PSO-based planner Used in LPP Module
The path planning is an optimization problem in which the main goal is to minimize the travel distance and time Tpath-flight,
and avoiding colliding obstacle(s). The following assumptions are considered in generation local optimum path.
a) The ocean current velocity is assumed to be constant. As the path planner is applied in a small scale area, the
water current has effect on both floating and moving obstacles, where moving obstacles have self-motivated
velocity additional to current velocity. The floating obstacles considered with a growing uncertainty rate ΘUr
proportional to current velocity (UR
C
(t)~|VC|).
b) The AUV considered to have constant thrust power, and therefore, the battery usage for a path is a constant
multiple of the distance travelled. Therefore, it is assumed the AUV travelling with constant velocity of VAUV.

These assumptions play important role in efficient path planning and copping with terrain dynamic changes. In path
planning simulation the obstacles are generated randomly from different categories and configured individually based on
given relations in section 3. Encountering different type of obstacles, this research investigates four different scenario in
terms of the dynamicity of the environment.
Scenario-1: The AUV starts its deployment in a pure static operating filed covered by random combination of the known
static and uncertain static obstacles, in which obstacles are under the exposure of varying levels of position
uncertainty propagating from the center of the obstacle. The vehicle is required to pass the shortest collision
free distance to reach to the specified target waypoint.
Scenario-2: Making the AUV’s deployment more challenging, in the second scenario, the robustness of the method is
tested in a dynamic environment with moving obstacles, in which obstacle position changes to a random
direction by uncertainty rate proportional to time, where the number of obstacles increases by time.
Scenario-3: In the third scenario, the mission becomes more complicated by encountering the current force on moving
obstacles with uncertain position, in which the obstacle has self-motivated velocity to a random direction and
affected by current force that presented with a growing uncertainty proportional to the current velocity
UR
C
~|VC| radiating out from centre of the object.
Scenario-4: The last case, an irregularly shaped terrains including all static, floating, and moving obstacles encountered in
computing optimum trajectory.
All four scenarios simulated for varying number of 3 to 6 obstacles in corresponding operation window. The purpose of this
simulation is evaluating the ability of the proposed method in balancing between searching unexplored environment and
safely swimming toward the target waypoint. For this purpose, a distinctive number of runs are performed to analyze the
performance of the method in satisfying the problem constrains for all mentioned scenarios.The PSO optimization
configuration set by 150 particles (candidate paths) and 100 iterations. The expansion-contraction coefficients also set on
2.0 to 2.5. The maximum number of control points for each B-spline is fixed on 8. The vehicles water-referenced velocity
considered 3 m/s. Figure.13 represent the produced optimum trajectory in first scenario encountering 3 to 6 obstacles. The
gradual increment of collision boundary is presented by circle(s) around the obstacles, in which the uncertainty propagation
is assumed to be linear with iteration/time. The performance of the algorithm in minimizing the cost and eliminating the
violation is for all scenarios represented by Fig.13(b-c) to Fig.15(b-c). The purpose of increasing the number of obstacles is
to evaluate sustainability of the path planning performance to complexity of the terrain.
(a) (b) (c)
Fig.13. (a) 2-D representation of generated optimum 3-D trajectory in scenario-1, including random combination 3 to 6 static known and static
uncertain obstacles. (b) Cost variation of path population in each iteration. (c) Violation variation of path population in each iteration as collision
penalty.
The performance of the algorithm investigated for the second scenario and presented by Fig.14, while number of obstacles
is increased to 6. The obstacles movement also occurs in a specified rate of uncertainty proportional to time in a random
direction.

(a) (b) (c)
Fig.14. (a) 2-D representation of generated optimum 3-D trajectory in scenario-2, including 3 to 6 moving obstacles. (b) Cost variation of path
population in each iteration. (c) Violation variation of path population in each iteration as collision penalty.
Figure 15 represents the produced optimum trajectory for 3 to 6 obstacles in the third scenario, respectively. The
uncertainty around the obstacles propagates from the centre of the object in all directions with a growth rate proportional to
current velocity. Additionally, the obstacles move with a self-motivated velocity in a random direction.
(a) (b) (c)
Fig.15. (a) 2-D representation of generated optimum 3-D trajectory in scenario-3, including 3 to 6 obstacles. (b) Cost variation of path
population in each iteration. (c) Violation variation of path population in each iteration as collision penalty.
Referring to Fig.13(b), 14(b) and 15(b), it is evident that the path population converges to the minimum cost by passing
iterations. The cost variation range decreases in each iteration which means algorithm accurately converges the solution
space to the optimum solution. The red crosses in the middle of the bar charts represent the mean cost of path population in
each iteration. Tracking the variation of the mean cost and mean violation in Fig.13(b,c), 14(b,c) and 15(b,c) declares that
algorithm accurately pushes the solutions to approach the optimum solution with minimum cost and efficiently manages the
trajectory to eliminate the collision penalty within 100 iterations.

Fig.16. The generated trajectory in scenario-4 with random composite of all four types of obstacles
The simulation result for last scenario is provided in Fig.16 in which the performance of the proposed method in generating
collision free shortest trajectory is investigated for a random combination of all types of obstacles. The trajectory is plotted
in 3-d format for clear graphical representation of its collision avoidance capability. The simulation results represented in
Fig.13 to Fig.16 shows that the proposed path planning method accurately generates collision free time optimal trajectories
and dynamically adapts to environmental changes encountering uncertain, static, floating, and moving obstacles. Increasing
the number of obstacles, increases the problems complexity, however, it is derived from results that the performance of the
algorithm is almost stable against increasing the complexity of the terrain and the algorithm tends to minimize the travel
distance and time, which furnishes the expectation of the architecture at lower level of the autonomy. Any time that LPP is
recalled from the main model, it dynamically computes optimum path based on observed change in the environment and
new obtained information. The AUV travels through the listed waypoints in optimum global route with 3m/s water-
referenced velocity, and passes waypoints one by one, in a way that a target waypoint for LPP, would be a new start
position in next run. This process repeats until vehicle reaches to the final destination. Therefore, the initial and destination
waypoints and operation field for LPP changes as vehicle passes through the waypoints in global route sequence. The next
step is evaluation of the entire model in terms of appropriate decision making and providing efficient interaction and
cooperation between the high and low level modules. Additional to addressed common performance indexes discussed
above, two other factors are highlighted for the purpose of this research that are investigated along the evaluation of the
architectures performance. The first critical factor for the LPP is the computational time. The second important factor
considered for the purpose of this research is the existence of reasonable and close correlation between generated path time
(Tpath-flight) the expected time (TExpected), which is investigated meantime the evaluation of the architecture along the checking
process for requisition of re-planning.
8.3 Architecture Performance Evaluation
In this section, the simulation result of the proposed configurable architecture for AUV’s mission management is presented.
The main architecture aims to take the maximum use of the mission available time, to increase the mission productivity by
optimum routing, and guarantee on-time termination of the mission; and concurrently ensuring the vehicles safety by
copping dynamic unexpected challenges during deployment toward the final destination. Accurate synchronization of the
inputs and outputs to the main model and concurrent cooperation of the engaged modules are the most important
requirements in stability of the architecture toward the main objectives addressed above. To this end, the robustness of the
model in enhancement of the vehicles autonomy is evaluated by testing 10 missions’ through 10 individual experiments
presented in Fig.17 to Fig.19.
The initial configuration of the operation network has been set on 50 waypoints and 1470 edges involving a fixed sequence
of tasks with specified characteristics (priority, risk percentage, completion time) in 10 km2
(x-y), 1000 m(z) space. The
waypoints location are randomized according to ~U(0,10000) for WPi
x,y and ~U(0,100) for WPi
z. The mission available time
for all experiments is fixed on TAvailable=10800(sec)=3 (hours). The vehicle starts its mission at initial location WP1
and
ends its mission at WP50
. The operating field is modeled as a realistic underwater environment that randomly covered by
different uncertain static, floating and moving obstacles, where the floating obstacles is affected by current force varied
according to |VC|~N(0,0.3). For the purposes of this study, the optimization problem was performed on a desktop PC with
an Intel i7 3.40 GHz quad-core processor in MATLAB® R2014a. The LPP as an inner component operates in context of
the GRP module and output of each module concurrently feeds to another one. One mission progress has been provided in
Table.2 (A-B) to clarify the process of the architecture in different stages of a specific mission toward carrying out the
mentioned objectives.

Table.2. Process of the architecture in one mission scenario
A. Global Route Planning (GRP) Module
Call NO Start Dest Task NO Weight Cost CPU TAvailable TRoute Validity Route Sequence
1 1 50 8 22 0.048 17.3 10800 10262 Yes 1-39-7-16-48-33-40-38-50
2 7 50 10 34 0.030 23.9 7710.8 7805 Yes 7-41-14-12-36-48-22-15-47-40-50
3 12 50 7 38 0.024 21.9 5375.3 5211 Yes 12-4-39-44-30-28-11-50
4 4 50 5 44 0.040 19.8 4234.7 3998 Yes 4-41-12-44-11-50
5 41 50 5 36 0.029 18.4 3474.7 3468 Yes 41-46-3-44-17-50
6 3 50 4 38 0.078 20.7 2117.3 2008 Yes 3-29-30-42-50
7 30 50 2 35 0.477 21.8 1054.5 1051 Yes 30-42-50
B. Local Path Planning (LPP) Module
Route ID PP Call Edges Violation(Collision) Cost CPU Tpath-flight TExpected TAvailable Replan Flag LPP Flag
Route-1
1 1-39 0.000000 0.2260 47.3 2333.1 2535.3 8466.7 0 1
2 39-7 0.000043 0.7010 39.8 755.8 666.6 7710.8 1 0
Route-2
1 7-41 0.000000 0.1460 42.4 501.4 508.3 7209.4 0 1
2 41-14 0.000000 0.3170 40.0 1078 1179 6131.4 0 1
3 14-12 0.000000 0.2260 42.3 756.1 686.6 5375.3 1 0
Route-3 1 12-4 0.000000 0.2790 40.9 1140.6 528.3 4234.7 1 0
Route-4 1 4-41 0.000000 0.2020 37.4 760.02 696.8 3474.7 1 0
Route-5
1 41-46 0.000000 0.2017 40.6 674.5 820.6 2800.2 0 1
2 46-3 0.000000 0.2037 44.1 682.8 647.8 2117.3 1 0
Route-6
1 3-29 0.000000 0.1600 39.8 567.2 857.6 1550.1 0 1
2 29-30 0.000000 0.1460 43.4 495.5 334.8 1054.5 1 0
Route-7
1 30-42 0.000000 0.1420 39.7 479.04 482.1 575.4 0 0
2 42-50 0.000000 0.1370 40.1 563.7 569.3 11.7 0 0
The mission starts with calling the GRP for the first time. The GRP produces a valid optimum route to take maximum use
of available time (valid route TRoute≤ TAvailable). Referring Table.2(A), the initial optimum route covers number of 8 tasks
with total weight of 22 and cost of 0.048 with estimated completion time of TRoute=10262(sec). In the second step, the LPP
is recalled to generate optimum collision free trajectory through the listed sequence of waypoint included in the initial
route. Referring to Table.2(B), the LPP module got the first pair of waypoints (1-39) and generated optimum trajectory
between location of WP1
to location of WP39
with total cost of the 0.2260, and travel time of Tpath-flight=2333.1 which is
smaller than TExpected=2535.3. The TExpected for the LPP is calculated based on estimated travel time for the generated route
TRoute. In cases that Tpath-flight is smaller than TExpected the re-planning flag is zero which means the initial optimum route is
still valid and optimum, so the vehicle is allowed the follow the next pair of waypoints included in initial optimum route.
After each run of the LPP, the Tpath-flight is reduced from the total available time TAvailable. The second pair of waypoints (39-
7) is shifted to the LPP and the same process is repeated. However, if Tpath-flight exceeds the TExpected re-planning flag gets
one, which means some of the available time is wasted in passing the distance between WP39
and WP7
due to copping
collision avoidance. In such a case also the TAvailable gets updated and visited edges (1-39, and 39-7) get eliminated from the
graph. Afterward, instead of LPP, the GRP is recalled to generate new optimum route from the current waypoint WP7
to the
predefined destination WP50
according to updated operation network and TAvailable. In experimental results presented in
Table.2, the GRP is recalled for 7 times and the LPP called for 13 times within 7 optimum routes. This synchronization
among the modules continues until vehicle reaches to the destination (success) or TAvailable gets a minus value (failure:
vehicle runs out of battery). The final route passed by the vehicle in this mission through the 7 route re-planning and 13
path planning is the sequence {1-39-7-41-14-12-4-41-46-3-29-30-42-50} with total cost of 0.038, total weight of 38, and
total time of 10788.3.
The most appropriate outcome for a mission is completion of the mission with the minimum remained time, which means
maximizing the use of mission available time. Referring Table.2(B), the remaining time is 11.7 out of the whole mission
available time of TAvailable=10800(sec)=3(h), which is considerably approached to zero. Therefore the architectures
performance can be represented by mission time (or remained time) along with productivity of the mission by completing
the maximum number of highest priority tasks with minimum risk percentage.
Considering the fact that reaching to the
destination, as a big concern for vehicles
safety, is more important than maximizing
the vehicles productivity, a big penalty
value is assigned to GRP to strictly prevent
generating routes with TRoute bigger than
TAvailable. To measure the performance of the
proposed dynamic architecture in a
quantitative manner, the robustness and
stability of the model in enhancement of the
vehicles autonomy in terms of mission time
management and vehicles safety is
evaluated through testing 10 individual
missions with the same initial condition that
closely matches actual underwater mission
scenarios that presented in Fig.17 to Fig.19.
Fig.17. Architecture performance in maximizing mission’s productivity by maximizing the
mission time constraint to available time threshold and its computational stability

The stability of the architecture in time management is the most critical factor representing robustness of the method. It is
derived from simulation results in Fig.17 that the proposed architecture is capable of taking maximum use of mission
available time as apparently the mission time in all experiments approach the TAvailable and meet the above constraints
denoted by the upper bound of 10800 sec=3 hours (is shown by red line). Respectively, the value of the remaining time
that has a linear relation to TAvailable, should be minimized but it should not be equal to zero which is accurately satisfied
considering variations of remaining time for 10 experiments in Fig.17. In other words, minimizing the remaining time
maximizes the mission productivity. To establish appropriate cooperation between the high and low level modules (GRP
and LLP), the correlation between path time (Tpath-flight) the expected time (TExpected) is another important performance index
investigated and presented in Fig.18.

Fig.18. Stability of architecture in managing correlation of Tpath-flight and TExpected in multiple recall of LPP in 10 experiments
Figure 18 presents relation between value of Tpath-flight and TExpected in multiple recall of LPP in 10 different experiments.
Existence of a reasonable difference between Tpath-flight and TExpected values in each LPP operation is critical to total
performance of the architecture. In other words, there shouldn’t be a big difference between these two parameters to
prevent interruption in cohesion of the whole system. As discussed earlier, route replanning is required when the Tpath-flight
exceeds TExpected; hence, according to Fig.18, the Synchro-module is recalled for six times in mission 1, not recalled in
mission 2, two times in mission 3, not recalled in mission 4 and 5, six times in mission 6, ones in mission 7, three times in
mission 8, four times in mission 9, and two times in mission 10 in order to apply mission updates and carry out the
replanning process. Another critical factor is the computational time for both LPP and GRP operations. The LPP must
operate concurrently and synchronous to the GRP, thus a large computational time causes the LPP drop behind the
operation of the GRP, which flaws the routine flow and cohesion of the whole system. Figure 19 presents the
computational time for both LPP and GRP operations in multiple recalls through the 10 mission executions in boxplot
format.
It is noteworthy to mention from analyze
of results in Fig.19 that the proposed
methodology takes a very short
computational (CPU) time for all
experiments that makes it highly suitable
for real-time application. Besides,
referring to Fig.19 it can be inferred that
the variation of computational time is
settled in a narrow bound (approximately
in range of second for all experiments) for
both GRP and LPP modules that confirm
applicability the model for real-time
implementation. Considering the indexs of
the total mission time, remaining time, and
variations of Tpath-flight and TExpected, the
results obtained from analyze of 10
different missions are quantitatively very
similar that proves the inherent stability of
the model. More importantly, the violation
percentage in both GRP and LPP
simulations presented in Table.2, reveal
that both planners’ are robust to the
variations of the operation network
parameters and environmental conditions.
9 Conclusion
In this paper, a novel approach for enhancement of an underwater vehicle’s autonomy for large-scale underwater mission
was provided. This included a two-layer architecture, route planner in top level and path planner in low level, working
interactively with each other and made vehicle capable of robust decision-making. Indeed, this research is an extension of
previous study (M.Zadeh et al. 2016-d, 2016-e) in which the high and low level motion planner are designed in a separate
modular format, so that the employed algorithms by each module can be easily replaced or upgraded. The main advantage
of the proposed framework is having a modular and flexible structure that is compatible with a broad range of
computational methods. The underwater mission, which conceptually is a kind of task assignment problem, was specified
by accomplishing the maximum number of assigned tasks regarding the mission available time. By doing so, a series of
diverse scenarios were designed to evaluate the performance and reliability of the proposed model. Simulation results
showed that the proposed model is able to generate real-time near-optimal solutions that are relatively independent of both
size and complexity of operation network. Therefore, the main objective of mission that was maximizing the mission
Fig.19. Stability of LPP and GRP computational time variation for different recall in 10
individual experiments

productivity while keeping the vehicle safety was perfectly satisfied. Besides, the results indicated that the proposed model
is good choice for operating in dynamic environment as it can excellently handle the influence of uncertainties through the
mission.
As prospect for future research, we will plan to improve the level of vehicle’s overall situation awareness by using the
estimation of one step forward of mission operating filed changes and then feeding those to the model to generate the
solutions for such a highly dynamic and uncertain missions. Besides, the functionality of the model will be investigated on
a sea test trials. The modules will be upgraded with online replanning capability operating in a more realistic environment.
Acknowledgment
Somaiyeh M.Zadeh and Amirmehdi Yazdani are funded by Flinders International Postgraduate Research Scholarship
(FIPRS) program, Flinders University of South Australia. This research is also supported through a FIPRS scheme from
Flinders University. The authors declare that there is no conflict of interest.
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