Jalayan Vikram Robot

Overview

Jalayan Vikram Robot is an autonomous amphibious robot designed for post-flood relief operations. This project leverages ROS2, Gazebo, OpenCV, and the Raspberry Pi 4 to create a powerful system capable of navigating land and water environments for disaster recovery. The robot's core feature is its ability to geo-tag locations, which helps optimize resource allocation and significantly reduces response time in flood-affected areas.

Features

• Amphibious Design: Designed to operate both on land and in water, suitable for flood rescue missions.
• Autonomous Navigation: ROS2-based control for real-time movement and decision-making.
• Geo-tagging System: Optimizes resource allocation by tagging locations during the rescue operation.
• Obstacle Detection: Uses sensors and OpenCV for object detection and obstacle avoidance.
• Simulated Testing: Developed and tested in Gazebo before real-world implementation.
• Raspberry Pi 4: Low-cost, high-performance computing for real-time operations.

Technologies Used

• ROS2 (Robot Operating System 2)
• Gazebo Simulator
• OpenCV (Computer Vision Library)
• Raspberry Pi 4
• Fusion 360 (3D Modeling and Design)

Development Process

1. Design: 3D design using Fusion 360 for an amphibious chassis.
2. Simulation: Testing in a virtual flood environment using Gazebo.
3. Autonomous Navigation: ROS2 nodes for movement and obstacle detection.
4. Object Detection: Integrated OpenCV for real-time object detection.
5. Geo-tagging: Implemented location-based tagging to enhance disaster relief coordination.

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How to Build and Run the Package

Prerequisites

• ROS2 installed on your system. You can find instructions to install ROS2 here.
• Gazebo installed for simulation.
• A basic understanding of ROS2 nodes and packages.

1. Create a ROS2 Workspace

To begin, create a workspace and initialize your project package.

mkdir -p ~/jalayan_ws/src
cd ~/jalayan_ws/src
ros2 pkg create --build-type ament_python flood_robot

2. Clone the Project Repository

Clone the repository into your workspace:

git clone https://github.com/jaswanth-coder/Flood_robo.git

3. Implement the Movement and Obstacle Detection

In the flood_robot directory, create two Python files: move_robot.py and obstacle_detection.py. These files will handle the robot’s movement and obstacle detection, respectively.

move_robot.py

obstacle_detection.py

Human_detection

this folder contains the object detection model file and dataset related details .

4. Create a Launch File

In the launch directory, create a launch file flood_robot_launch.py to launch both the movement and obstacle detection nodes.

5. Build the Package

After writing the code, build the ROS2 package:

cd ~/jalayan_ws
colcon build

Source the workspace:

source install/setup.bash

6. Run the Launch File

Finally, launch the package:

ros2 launch flood_robot flood_robot_launch.py

This will start the robot movement and obstacle detection system.

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Simulation in Gazebo

You can test the robot’s behavior in a simulated environment using Gazebo. Modify the package to include a simulation world and robot model, and test the movement and obstacle avoidance in a virtual flood scenario.

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Conclusion

Jalayan Vikram Robot is an ideal beginner project that introduces you to core robotics concepts such as autonomous navigation, obstacle detection, and sensor integration using ROS2. Its application in post-flood relief scenarios demonstrates the real-world utility of robotics in disaster management. As you continue to develop the project, consider incorporating more advanced AI algorithms for better performance.

Special Thanks to and Reference from https://www.pcbway.com/project/shareproject/3D_Printed_Screw_propelled_Robot_With_Video_Feed_8bf6a5c6.html Contributions are welcome. Check out the CONTRIBUTING.md for more details.

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For further inquiries or support, please contact the project maintainers.