Author: Rahul Roy
Rescue missions in disaster-stricken areas pose significant challenges due to their dangerous and unpredictable nature. The intricacies of these environments make it difficult to send humans for exploration, necessitating the utilization of ground robots or aerial robots. While conventional robots encounter limitations such as maneuvering through tight spaces and overcoming obstacles, quadrupeds offer promising solutions with their superior mobility.
To address this challenge, I have programmed a Unitree Go1 robot to autonomously explore unknown environments and search for survivors in disaster zones. Equipped with facial recognition capabilities, the robot can efficiently navigate through confined spaces and hazardous terrain, enhancing the effectiveness of rescue operations.
Final Project Video:
finalvideo.mp4
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Setup your workspace:
mkdir -p ws/src
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Go into your source directory:
cd ws/src
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Download the
dependencies.repos
file:wget <(https://github.com/roy2909/QuadrupedSearchandRescue/blob/main/dependencies.repos)>
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Use the VCS tool to import the dependencies:
vcs import < dependencies.repos
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Download the
rs_driver
submodule inLidar_ROS_2_SDK
:cd rslidar_sdk_ros2/ git submodule init git submodule update
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Build the workspace:
colcon build
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Source the workspace:
source install/setup.bash
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Launch the Intel RealSense camera node:
ros2 launch realsense2_camera rs_launch.py enable_gyro:=true enable_accel:=true unite_imu_method:=1 enable_infra1:=true enable_infra2:=true enable_sync:=true
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Launch everything on the robot for mapping with the LiDAR only:
ros2 launch unitree_nav unitree_nav.launch.py
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Launch the robot with the RealSense camera and the LiDAR:
ros2 launch unitree_nav unitree_nav_camera.launch.py
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Launch the exploration node only:
ros2 launch frontier frontier.launch.xml
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Launch the exploration node and YOLO node for person detection:
ros2 launch frontier frontier_detect.launch.xml
Feel free to reach out if you have any questions or need further assistance with setting up the project.