![BILL OF MATERIALS – ROVER
No. Part Quantity Amount Links
1 RPi 4 1 [redacted]
2 Lidar 1 [redacted]
3 Chassis Build- Pipes, Wood X 4,000
4 RPi Camera 1 2,000
Review 2 PBL 3 6](https://image.slidesharecdn.com/pbllast-240928082024-c6dfdc1f/85/pbl-last-for-the-enginerreing-in-the-most-best-oc-6-320.jpg)
pbl last for the enginerreing in the most best oc
- 1. DEATHMACHINE AGRICULTURAL ROBOTIC PROCESS AUTOMATION Aditya Rane 20070123002 Pratham Talekar 20070123113 Satvik Sattenapalli 20070123089 Balajee Srinvasan 20070123059
- 2. AGENDA Introduction Scope Bill of Materials Implementation & Methodology Plan of work Summary Review 2 PBL3 2
- 3. INTRODUCTION The DeathMachine is a combination of Rover and the Drone whose main purpose is to spray pesticides. • Rover – A remotely controlled rover upon which a landing pad will be present for the drone to launch/land. A secondary tank will also be available on the rover to spray pesticides and/or to refill the pesticides for the drone. • Drone – An efficient drone that can spray pesticide over a large area easily that can be used by the farmers. It can be used for surveillance of the field as well. Review 2 PBL 3 3
- 4. SCOPE • There has been numerous technological development in the world to provide sufficient food as per the demand of society but in one place there has been no vast technological advancements i.e. safety of the crops (external and biological factors). • With the help of the drone, farmers can easily monitor their field and can act upon the immediate external dangers. • The Roadbot will act as a multipurpose vehicle as it will be used as the launch pad for the drone and it will also have a secondary tank for refilling the tank of drone or for spraying in places where the drone will be unable to. • With the help of this project, the farmer can easily take care of their field and protect the crops from external and biological dangers. Review 2 PBL 3 4
- 5. LITERATURE SURVEY Agriculture is a vital sector for human sustenance and economic growth. However, with the increasing global population and climate change, the demand for sustainable and efficient agricultural practices has never been higher. In recent years, the use of rovers and drones in agriculture has emerged as a potential solution to meet these challenges. Rovers and drones equipped with advanced sensors and imaging technologies can help farmers monitor crops, identify problems, and optimize agricultural operations. This literature survey aims to provide an overview of the current state-of-the-art in the use of rovers and drones in agriculture Rovers in Agriculture Rovers are unmanned ground vehicles that can be used to collect data and perform various tasks in agriculture. One of the main advantages of rovers is their ability to navigate through rough terrain and collect data from hard-to-reach areas. For example, a study by Yang et al. (2020) used a rover equipped with a multispectral camera and lidar sensors to collect data on soil moisture, temperature, and plant growth in a cotton field. The data collected by the rover was used to generate a 3D map of the field, which helped the farmers to identify areas with poor soil conditions and take appropriate actions. Another application of rovers in agriculture is precision weed management. A study by Wulfsohn et al. (2019) used a rover equipped with a camera and machine learning algorithms to detect and classify weeds in a soybean field. The rover was able to achieve an accuracy of 95% in detecting weeds, and the results were used to guide the application of herbicides only where necessary, reducing the amount of herbicide used and minimizing the environmental impact. Drones in Agriculture Drones are unmanned aerial vehicles that can be used to collect high-resolution data and images of crops and fields. One of the main advantages of drones is their ability to cover large areas quickly and efficiently. For example, a study by Zhang et al. (2021) used drones equipped with multispectral cameras to collect data on crop growth and plant health in a rice field. The data collected by the drones was used to create a map of the field, which helped the farmers to identify areas with nutrient deficiencies and adjust their fertilization practices. Another application of drones in agriculture is crop mapping and yield estimation. A study by Liu et al. (2020) used drones equipped with RGB and thermal cameras to collect data on crop growth and yield in a maize field. The data collected by the drones was used to create a map of the field, which helped the farmers to identify areas with low yield and take appropriate actions. Review 2
- 6. BILL OF MATERIALS – ROVER No. Part Quantity Amount Links 1 RPi 4 1 [redacted] 2 Lidar 1 [redacted] 3 Chassis Build- Pipes, Wood X 4,000 4 RPi Camera 1 2,000 Review 2 PBL 3 6
- 7. PRIMARY GOALS Rover Implementing the Autonomous Path Planning
- 8. METHODOLOGY ROBOT OPERATING SYSTEM (ROS) Review 2 PBL 3 8 Robot Operating System (ROS) unlike its name, is a framework used to build robot software. It provides a set of libraries that helps one to create complex robotic systems. It is an open-source project maintained by Open Robotics. It provides a set of communication tools that enable the exchange of data between different components, such as sensors, actuators, and controllers. ROS also provides a set of standard message types and services that help developers create interoperable components. ROS supports multiple programming languages such as C++, Python, and Java, and has libraries for common tasks such as 3D visualization, navigation, and perception. It also provides a set of tools for debugging, monitoring, and simulation, which makes it easier to test and deploy robotic systems. ROS is widely used in academia and industry for a variety of applications such as autonomous driving, robotic exploration, and industrial automation. Its popularity is due to its flexibility, modularity, and the large community of developers that support it.
- 9. PBL 3 9 HOW WORKS Review 2 ROSMASTER ROS Node1 ROS Node2 ROS Node3 ROS Message ROS Message ROS Message ROS Node1 ROS Node2 ROS Message ROS Topic: /cmd_vel Message Type: geometry_msgs/Twist ROS programs are called nodes. A node is a program that performs a specific task in a robot system, such as reading sensor data. Nodes communicate with each other using ROS topics. A topic is a named bus over which nodes can publish and subscribe to messages. For example, a camera node might publish images to a topic, and a computer vision node might subscribe to that topic to process the images. Messages are the data that is passed between nodes over ROS topics. ROS provides a standard message format, but you can also define your own message types. The ROS master is a program that manages the network of nodes, topics, and services. Nodes register with the master when they start up, and the master keeps track of all the nodes and their communication channels.
- 10. METHODOLOGY SIMULTANEOUS LOCALIZATION AND MAPPING (SLAM) Review 2 PBL 3 10 Simultaneous Localization and Mapping (SLAM) is a technique used in robotics to build a map of an unknown environment while simultaneously locating the robot in that environment. SLAM is important because it enables robots to operate in unknown or changing environments without prior knowledge of the environment. The SLAM process involves collecting sensor data from the robot, processing the data to estimate the robot's location and the map of the environment, and using this information to control the robot's movement. There are various SLAM algorithms available such as Extended Kalman Filter (EKF) SLAM, FastSLAM, GraphSLAM, and Hector SLAM. Each algorithm has its own strengths and weaknesses, and the choice of algorithm depends on the application requirements and the available computational resources. SLAM is a complex technique and requires a deep understanding of robotics, control theory, and sensor data processing. However, it is an important area of research in robotics and has many real-world applications such as autonomous driving, robotic exploration, and search and rescue operations.
- 11. IMPLEMENTATION ROS & HECTOR SLAM Review 2 PBL 3 11 Raspberry Pi is a powerful device in comparison to any microcontroller, like an Arduino, however, being an ARM device, it has its own limitations, such as the lack of 64-bit operating systems like Ubuntu. However, it can run the Ubuntu Server which runs the ROSCore. The ROSNodes are initialized and run on the Raspberry Pi. A workstation, connected to the RPi over the network can access all ROSMessages sent over ROSTopics. The IP Address of the machine running the ROSCore (RPi) is imported on the workstation. Once, the connection is acknowledged, the data transmission is started, and the data can now be visualized with softwares like Gazebo and RViz. In case of a LIDAR, RViz can be setup to subscribe to the ROSTopic /scan. After starting the rplidar and hector_slam packages, the data can be visualized on RViz. The following slides include pictures of the same.
- 12. PBL 3 12 Review 2 LIDAR > Range : 8m > 400 RPM > Data: 8,000 points mapped/second
- 13. Review 2 PBL 3 13 Hector SLAM Simulated environment – LIDAR Heat Map Localization Failure Hector SLAM
- 14. Autonomous Path Planning in Simulated Environment
- 15. PBL 3 15 Review 2
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- 20. PBL 3 20 Review 2
- 21. PBL 3 21 SOURCES Review 2 •Yang, Y., Wang, J., Zhang, J., Zhang, B., Hu, J., & Yang, G. (2020). A robotic system for crop field data acquisition and analysis. Journal of Field Robotics, 37(3), 453-469. •Wulfsohn, D., Almeida, G., Dornhoff, G., & Weis, M. (2019). Development of a robotic weed detection system. Computers and Electronics in Agriculture, 161, 184-192. •Zhang, L., Chen, C., He, X., Xiong, Y., & Wang, Y. (2021). Rice phenotyping using unmanned aerial vehicle multispectral imagery. Remote Sensing, 13(1), 118. •Liu, J., Liu, Y., Zhang, L., & Wang, J. (2020). A maize yield estimation method based on UAV remote sensing data. Remote Sensing, 12(11), 1853
- 22. THANK YOU Aditya Rane Pratham Talekar Satvik Sattenapalli Balajee Srinivasan Review 2 PBL 3 22