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8000 GitHub - taozhang1004/rrt_ik_6dof_arm: Step-by-step tutorial: RRT planing and Inverse Kinematics for 6DOF robot arm
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Path Planning for GalaXea A1 Robotic Arm Based on RRT Algorithm

After completing this tutorial, you will:

  • Understand how the RRT path planning algorithm is implemented on a 6DOF robotic arm.
  • Learn about the shortcomings of the original RRT algorithm and how to improve it.
  • Learn how to use Inverse Kinematics (IK) to solve for joint angles.
  • Learn how to use RRT to perform object grasping in MuJoCo simulation and generate data.

Your Task

  • Use RRT (and its potential enhancements) to implement object grasping with the A1 robotic arm and generate data.
  • Task Outline:
    • Initial robotic arm position start
    • Target robotic arm position end
    • Without path planning, the arm will collide with obstacles no_plan
    • After applying RRT path planning with_plan

Task Steps

Step 1: Understand the RRT Algorithm and Run the Code

  • Read through rrt_a1.py and understand how the algorithm is implemented.
  • Start the conda environment set up in Project 1: conda activate act_a1
  • Run RRT until a successful path is found: python3 rrt_a1.py
  • View the path generated: rrt_robot_motion.mp4

Step 2: Improve the RRT Algorithm

  • Problem: Currently, the probability of RRT finding a valid path is quite low, or it takes too long to find one.
  • Solution: Enhance the existing RRT algorithm to improve planning efficiency, such as using RRT* or RRT-Connect algorithms.

Step 3: Implement Object Grasping and Record Data

  • Set up a scene in MuJoCo for the robotic arm to grasp an object.
  • The current target position is defined in the robotic arm's 6D joint space. In other words, the goal is to find a joint state, but what we want is to provide the object’s pose and have the arm directly grasp it. You need to refer to the code in ik.py to solve for the joint angles based on the target end-effector pose using IK. Install the additional dependencies: pip install ikpy transformations, then run python3 ik.py to generate a sample IK video. The coarse coordinates represent the actual end-effector pose, while the fine coordinates are the target pose. If the IK solution is successful, both poses will perfectly overlap, as shown in the image: ik
  • Combine the previous steps to build an automated path planning and object grasping data recording pipeline for future training use.

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Step-by-step tutorial: RRT planing and Inverse Kinematics for 6DOF robot arm

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