Apresentação ICTAI: A Multi-Population Genetic Algorithm for UAV Path Re-Planning under Critical Situation
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The document presents a multi-population genetic algorithm designed for UAV path re-planning in critical situations such as motor and battery failures. It outlines the problem description, various methods including a greedy heuristic, and computational results demonstrating the algorithm's effectiveness in navigating different regions and handling failures. The findings suggest improved performance of the multi-population genetic algorithm over traditional methods in various experimental scenarios.
![Computational Results
Parameters and Settings used in the Experiments
Model Parameters Value
Map
Dimension X [m] 1000
Dimension Y [m] 1000
UAV
Initial Position (px
0 , py
0 ) [m] (0; 0)
Initial Velocity (v0) [m/s] 24
Initial Angle (α0) [o
] 90
Linear Velocity (vmin; vmax ) [m/s] [11; 30]
Angular Variation (εmin; εmax ) [o
/s] [−3; 3]
Acceleration (amin; amax ) [m/s2
] [0; 2]
Number of time steps to land (T) [s] 60
Time Discretization (∆T) [s] 1
Probability of failure (∆) 0.001
MPGA
Populations 3
Individuals/Pop 13
Individuals Total 39
Mutation Rate 0.5
Crossover Rate 0.75
Stop Criterion 10000
Jesimar S. Arantes (USP) IEEE ICTAI 2015 November – 2015 32 / 41](https://image.slidesharecdn.com/ieee-ictai-2015-170321034024/85/Apresentacao-ICTAI-A-Multi-Population-Genetic-Algorithm-for-UAV-Path-Re-Planning-under-Critical-Situation-35-320.jpg)
Apresentação ICTAI: A Multi-Population Genetic Algorithm for UAV Path Re-Planning under Critical Situation
- 1. A Multi-Population Genetic Algorithm for UAV Path Re-Planning under Critical Situation Jesimar S. Arantes (USP) Márcio S. Arantes (USP) Claudio F. M. Toledo (USP) Brian C. Williams (MIT) São Carlos, SP November – 2015 Jesimar S. Arantes (USP)Márcio S. Arantes (USP)Claudio F. M. Toledo (USP)Brian C. Williams (MIT) (USP)IEEE ICTAI 2015 November – 2015 1 / 41
- 2. Outline 1 Introduction 2 Problem Description 3 Methods 4 Computational Results Jesimar S. Arantes (USP) IEEE ICTAI 2015 November – 2015 2 / 41
- 3. Introduction Overview Figure 1: Illustrative scenario for mission planning. Jesimar S. Arantes (USP) IEEE ICTAI 2015 November – 2015 3 / 41
- 4. Introduction Overview Figure 2: Illustrative scenario for mission planning. Jesimar S. Arantes (USP) IEEE ICTAI 2015 November – 2015 4 / 41
- 5. Introduction Overview Figure 3: Illustrative scenario for mission planning. Jesimar S. Arantes (USP) IEEE ICTAI 2015 November – 2015 5 / 41
- 6. Introduction Overview Figure 4: Illustrative scenario for mission planning. Jesimar S. Arantes (USP) IEEE ICTAI 2015 November – 2015 6 / 41
- 7. Introduction Overview Figure 5: Illustrative scenario for mission planning. Jesimar S. Arantes (USP) IEEE ICTAI 2015 November – 2015 7 / 41
- 8. Introduction Overview Figure 6: Illustrative scenario for mission planning. Jesimar S. Arantes (USP) IEEE ICTAI 2015 November – 2015 8 / 41
- 9. Problem Description Types of Regions and Critical Situation Regions 1 No-Fly Zone (φn) 2 Penalty Region (φp) 3 Bonus Region (φb) 4 Remainder Region (φr ) Critical Situation 1 Motor Failure (ψm) 2 Battery Failure (ψb) 3 Aerodynamic Surfaces Failure type 1 (ψs1 ) 4 Aerodynamic Surfaces Failure type 2 (ψs2 ) Jesimar S. Arantes (USP) IEEE ICTAI 2015 November – 2015 9 / 41
- 10. Problem Description Types of Regions and Critical Situation Regions 1 No-Fly Zone (φn) 2 Penalty Region (φp) 3 Bonus Region (φb) 4 Remainder Region (φr ) Critical Situation 1 Motor Failure (ψm) 2 Battery Failure (ψb) 3 Aerodynamic Surfaces Failure type 1 (ψs1 ) 4 Aerodynamic Surfaces Failure type 2 (ψs2 ) Jesimar S. Arantes (USP) IEEE ICTAI 2015 November – 2015 9 / 41
- 11. Methods Codification, Decodification and Solution Codification ut: Jesimar S. Arantes (USP) IEEE ICTAI 2015 November – 2015 10 / 41
- 12. Methods Codification, Decodification and Solution Codification ut: Decodification FΨ: xt+1 = FΨ(xt , ut ) px t+1 py t+1 vt+1 αt+1 = px t + vt · cos(αt ) · ∆T + at · cos(αt ) · (∆T)2/2 py t + vt · sen(αt ) · ∆T + at · sen(αt ) · (∆T)2/2 vt + at · ∆T − Fd t αt + εt · ∆T Jesimar S. Arantes (USP) IEEE ICTAI 2015 November – 2015 10 / 41
- 13. Methods Codification, Decodification and Solution Codification ut: Decodification FΨ: xt+1 = FΨ(xt , ut ) px t+1 py t+1 vt+1 αt+1 = px t + vt · cos(αt ) · ∆T + at · cos(αt ) · (∆T)2/2 py t + vt · sen(αt ) · ∆T + at · sen(αt ) · (∆T)2/2 vt + at · ∆T − Fd t αt + εt · ∆T Solution xt: Jesimar S. Arantes (USP) IEEE ICTAI 2015 November – 2015 10 / 41
- 14. Methods Greedy Heuristic Jesimar S. Arantes (USP) IEEE ICTAI 2015 November – 2015 11 / 41
- 15. Methods Greedy Heuristic Jesimar S. Arantes (USP) IEEE ICTAI 2015 November – 2015 12 / 41
- 16. Methods Greedy Heuristic Jesimar S. Arantes (USP) IEEE ICTAI 2015 November – 2015 13 / 41
- 17. Methods Greedy Heuristic Jesimar S. Arantes (USP) IEEE ICTAI 2015 November – 2015 14 / 41
- 18. Methods Greedy Heuristic Jesimar S. Arantes (USP) IEEE ICTAI 2015 November – 2015 15 / 41
- 19. Methods Greedy Heuristic Jesimar S. Arantes (USP) IEEE ICTAI 2015 November – 2015 16 / 41
- 20. Methods Multi-Population Genetic Algorithm Jesimar S. Arantes (USP) IEEE ICTAI 2015 November – 2015 17 / 41
- 21. Methods Multi-Population Genetic Algorithm Jesimar S. Arantes (USP) IEEE ICTAI 2015 November – 2015 18 / 41
- 22. Methods Multi-Population Genetic Algorithm Jesimar S. Arantes (USP) IEEE ICTAI 2015 November – 2015 19 / 41
- 23. Methods Multi-Population Genetic Algorithm Jesimar S. Arantes (USP) IEEE ICTAI 2015 November – 2015 20 / 41
- 24. Methods Multi-Population Genetic Algorithm Jesimar S. Arantes (USP) IEEE ICTAI 2015 November – 2015 21 / 41
- 25. Methods Multi-Population Genetic Algorithm Jesimar S. Arantes (USP) IEEE ICTAI 2015 November – 2015 22 / 41
- 26. Methods Multi-Population Genetic Algorithm Jesimar S. Arantes (USP) IEEE ICTAI 2015 November – 2015 23 / 41
- 27. Methods Multi-Population Genetic Algorithm Jesimar S. Arantes (USP) IEEE ICTAI 2015 November – 2015 24 / 41
- 28. Methods Multi-Population Genetic Algorithm Jesimar S. Arantes (USP) IEEE ICTAI 2015 November – 2015 25 / 41
- 29. Methods Multi-Population Genetic Algorithm Jesimar S. Arantes (USP) IEEE ICTAI 2015 November – 2015 26 / 41
- 30. Methods Multi-Population Genetic Algorithm Jesimar S. Arantes (USP) IEEE ICTAI 2015 November – 2015 27 / 41
- 31. Methods Multi-Population Genetic Algorithm Jesimar S. Arantes (USP) IEEE ICTAI 2015 November – 2015 28 / 41
- 32. Methods Objective Function minimize fitness = −Cφb · |φb| i=1 (P(xK ∈ Zi φb )) + Cφp · |φp| i=1 (P(xK ∈ Zi φp )) + Cφn · max(0, 1 − ∆ − P K t=0 |φn| i=1 xt /∈ Zi φn ) + 1 |εmax | · K t=0 ut · |εt | + shortestDist(xK , Zφb ) + Cφb , vK − vmin > 0 0 , otherwise + Cφb · 2 (K−T) 10 , ψ = ψb 0 , otherwise Landing on φb Landing on φp Landing and fly on φn Curves of the UAV Distance to φb Time violation Battery failure Jesimar S. Arantes (USP) IEEE ICTAI 2015 November – 2015 29 / 41
- 33. Methods Methods Used In this work, the following methods were used. GH: Greedy Heuristic MPGA1(–GH): Multi-Population Genetic Algorithm 1 Without greedy operator MPGA2(+GH): Multi-Population Genetic Algorithm 2 With greedy operator Jesimar S. Arantes (USP) IEEE ICTAI 2015 November – 2015 30 / 41
- 34. Computational Results Automatically Generated Maps Level of Difficulty 1 ME : a), b) 2 MN: c), d) 3 MH: e), f) Level of Coverage 1 C25%: a), c), e) 2 C50%: b), d), f) Legend Colors 1 φb 2 φp 3 φn 4 φr (a) (b) (c) (d) (e) (f) Jesimar S. Arantes (USP) IEEE ICTAI 2015 November – 2015 31 / 41
- 35. Computational Results Parameters and Settings used in the Experiments Model Parameters Value Map Dimension X [m] 1000 Dimension Y [m] 1000 UAV Initial Position (px 0 , py 0 ) [m] (0; 0) Initial Velocity (v0) [m/s] 24 Initial Angle (α0) [o ] 90 Linear Velocity (vmin; vmax ) [m/s] [11; 30] Angular Variation (εmin; εmax ) [o /s] [−3; 3] Acceleration (amin; amax ) [m/s2 ] [0; 2] Number of time steps to land (T) [s] 60 Time Discretization (∆T) [s] 1 Probability of failure (∆) 0.001 MPGA Populations 3 Individuals/Pop 13 Individuals Total 39 Mutation Rate 0.5 Crossover Rate 0.75 Stop Criterion 10000 Jesimar S. Arantes (USP) IEEE ICTAI 2015 November – 2015 32 / 41
- 36. Computational Results Experiments: Result obtained for the GH, MPGA1(–GH) and MPGA2(+GH) GH MPGA1(–GH) MPGA2(+GH) Ψ Instance φb φr Inf. φb φr Inf. φb φr Inf. ψm ME and C25% 79 21 0 81 19 0 90 10 0 ME and C50% 92 6 2 92 7 1 96 3 1 MN and C25% 58 39 3 60 39 1 71 28 1 MN and C50% 86 12 2 84 16 0 96 4 0 MH and C25% 30 52 18 36 64 0 40 60 0 MH and C50% 62 28 10 60 33 7 82 15 3 Avg 67.8 26.3 5.8 68.8 29.7 1.5 79.2 20.00 0.83 Jesimar S. Arantes (USP) IEEE ICTAI 2015 November – 2015 33 / 41
- 37. Computational Results Experiments: Result obtained for the GH, MPGA1(–GH) and MPGA2(+GH) GH MPGA1(–GH) MPGA2(+GH) Ψ Instance φb φr Inf. φb φr Inf. φb φr Inf. ψb ME and C25% 99 0 1 100 0 0 100 0 0 ME and C50% 97 0 3 99 0 1 99 0 1 MN and C25% 93 3 4 94 5 1 99 0 1 MN and C50% 98 0 2 99 0 1 100 0 0 MH and C25% 67 5 28 73 27 0 94 6 0 MH and C50% 83 0 17 68 17 15 95 2 3 Avg 89.5 1.3 9.2 88.8 8.2 3.0 97.8 1.3 0.8 Jesimar S. Arantes (USP) IEEE ICTAI 2015 November – 2015 34 / 41
- 38. Computational Results Experiments: Result obtained for the GH, MPGA1(–GH) and MPGA2(+GH) GH MPGA1(–GH) MPGA2(+GH) Ψ Instance φb φr Inf. φb φr Inf. φb φr Inf. ψs1 ME and C25% 81 8 11 90 8 2 91 7 2 ME and C50% 88 0 12 89 0 11 93 0 7 MN and C25% 68 16 16 76 18 6 86 8 6 MN and C50% 82 1 17 84 3 13 89 0 11 MH and C25% 41 23 36 49 46 5 67 28 5 MH and C50% 56 0 44 46 23 31 78 4 18 Avg 69.3 8.0 22.7 72.3 16.3 11.3 84.0 7.8 8.2 Jesimar S. Arantes (USP) IEEE ICTAI 2015 November – 2015 35 / 41
- 39. Computational Results Experiments: Result obtained for the GH, MPGA1(–GH) and MPGA2(+GH) GH MPGA1(–GH) MPGA2(+GH) Ψ Instance φb φr Inf. φb φr Inf. φb φr Inf. ψs2 ME and C25% 90 4 6 94 4 2 99 0 1 ME and C50% 90 0 10 95 1 4 95 1 4 MN and C25% 70 20 10 79 16 5 92 5 3 MN and C50% 87 1 12 83 8 9 94 0 6 MH and C25% 40 17 43 62 35 3 74 24 2 MH and C50% 61 3 36 57 13 30 76 4 20 Avg 73.0 7.5 19.5 78.3 12.8 8.8 88.3 5.7 6.0 Avg Final 74.9 10.8 14.3 77.1 16.7 6.2 87.3 8.7 4.0 Time (Sec) GH MPGA1 MPGA2 0.07 1.017 0.874 Jesimar S. Arantes (USP) IEEE ICTAI 2015 November – 2015 36 / 41
- 40. Computational Results Experiments: Example of Routes S E (a) S E (b) Figure 7: Routes determined by the planner MPGA2(+GH) in a map MN with coverage C25%: (a) ψm. (b) ψb. Jesimar S. Arantes (USP) IEEE ICTAI 2015 November – 2015 37 / 41
- 41. Computational Results Experiments: Example of Routes S E (c) S E (d) Figure 8: Routes determined by the planner MPGA2(+GH) in a map MN with coverage C25%: (c) ψs1 . (d) ψs2 . Jesimar S. Arantes (USP) IEEE ICTAI 2015 November – 2015 38 / 41
- 42. Computational Results Experiments: Example of Routes (a) Wind Velocity 10 Knots - MN with C25% (b) Wind Velocity 50 Knots - MN with C25% Figure 9: (a), (b) FG simulation with winds 10 and 50 knots. Wind direction: west. Jesimar S. Arantes (USP) IEEE ICTAI 2015 November – 2015 39 / 41
- 43. Computational Results Video FlightGear Simulator Figure 10: Video FlightGear Simulator. Jesimar S. Arantes (USP) IEEE ICTAI 2015 November – 2015 40 / 41
- 44. Acknowledgements Questions send email to: jesimar.arantes@usp.br marcio, claudio@icmc.usp.br williams@mit.edu Thank You! Jesimar S. Arantes (USP) IEEE ICTAI 2015 November – 2015 41 / 41