Mobile Pick and Place

Project Overview

The Mobile Pick and Place project involves a robotic arm mounted on an Autonomous Mobile Robot (AMR). The system performs pick-and-place operations autonomously by navigating to a target location, detecting and estimating the pose of objects using a YOLO-based object detection module, and executing robotic arm movements. The navigation stack and Robotics arm software is implemented using the ROS 2 Humble framework.

System Workflow

1. Navigation : The AMR navigates from its current location to the designated pick and drop location using the Nav2 stack.
2. Object Detection and 3D/6D Pose Estimation( pose_estimation_pkg ): A YOLO-based AI module identifies the object and uses it for estimating the pose of the object relative to the camera by utilizing Hyco compiler.
3. Pose Transformation: The rcar_communication package transforms the pose from the camera frame to the base frame of the robotic arm.
4. Pick-and-Place Operation: Joint angles are calculated, and the robotic arm executes the pick-and-place task.

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Setup Instructions

Prerequisites

• ROS 2 Humble installed on your system.
• Correct hardware connections for the AMR and robotic arm.
• A YOLO v8-based object detection model.

1.Clone Repository

git clone https://github.com/Ignitarium-Renesas/R-Car_Mobile_Pick_Place_V2.git

2. Install Requirements

cd R-Car_Mobile_Pick_Place_V2/pick_n_place_ws/
pip install -r src/pose_estimation_pkg/requirements.txt 
pip install pymycobot --upgrade 

cd R-Car_Mobile_Pick_Place_V2/pick_n_place_ws/src/Project-Rover-Robot
npm i 

3. Build the Workspace

cd R-Car_Mobile_Pick_Place_V2/pick_n_place_ws
colcon build

4. Flash the AMR code into Arduino

Flash the code provided in this folder in to arduino board.

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Run Application

Step 1: Verify Connections

Ensure all hardware connections are correctly set up.

• Turn on the Wi-Fi Router.
• Turn on the robotic arm & mobile base.

Step 2: Connect to MechArm Robotic Arm Controller

Connect to the Wi-Fi access point:

SSID: IGN-Robo_5G
Pass: IGN_Robo  

Log in to the Mecharm using SSH:

ssh er@192.168.0.146
# Password: Elephant (default password for Elephant Robotics Hardware)

Step 3: Launch the RealSense Camera Node

Open a terminal in the Mecharm and launch the camera node:

ros2 launch realsense2_camera rs_launch.py

Step 4: Launch the Arm Server Script

Open a terminal in the mecharm and launch the camera node:

python3 Server_280.py

Step 5: Run Rcar demo launch file

ssh root@192.168.0.217

docker start rcar
docker exec -it rcar bash
ros2 launch rcar_demo run_demo.launch.py

Step 6: Run HyCo Application (Websocket)

# copy app_temp in to rcar board
scp -r HyCo_Infer_App/app_temp/ root@192.168.0.217:~/

ssh root@192.168.0.217
cd app_temp
export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/lib:/home/root/app_temp/onnxruntime-linux-aarch64-1.17.1/lib:/home/root/app_temp

./rcar_app_ws yolo_v5s_ign-app/exec_config.json

Step 7: Start and enter the Docker container

ssh root@192.168.0.217 

docker start rcar 

docker exec -it rcar bash 

#run navigation modules:Bringup 

ros2 launch rcar_robot base_bringup.launch.py 

Step 8: Launch Navigation

ssh root@192.168.0.217 

docker start rcar 

docker exec -it rcar bash 

#run navigation modules:Navigation 

Ros2 launch rcar_robot rcar_navigation.launch.py 

Step 9: Launch GUI & Start Demo

cd R-Car_Mobile_Pick_Place_V2/pick_n_place_ws/src/mycobot280/Project-Rover-Robot/ 
node index.js 
 
#Open a Browser and in search tab type: 
http://localhost:5000/ 

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Notes:

• Refer the PCB_Files folder for Design related files
• Refer this README for further instruction related to pose estimation.
• Refer this README for further instructions related to HyCo Inference.
• Refer this README for further instructions related to Pose Transformation and services.

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