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RRT Algorithm for Motion Planning in Robotic Arms
Project type
Robotics, Dynamics & Control
Date
Sep 2023 - Oct 2023
Repository
YouTube Video 1
YouTube Video 2
Skills
Rviz · Linux/UNIX · Python (Programming Language) · Robot Operating System (ROS2) · MOVEit
This project focused on implementing the Rapidly-Exploring Random Tree (RRT) algorithm for sampling-based motion planning to control a UR5 robotic arm. Utilizing the MoveIt! library in a ROS2 environment, the goal was to develop a robust motion planning solution capable of navigating the robotic arm through various environments, avoiding obstacles, and reaching specified end-effector poses.
Key Features:
- RRT Algorithm Implementation: Developed the RRT algorithm to compute joint trajectories, ensuring the arm's end-effector could reach desired positions while avoiding obstacles.
- Collision Avoidance: Incorporated collision detection and avoidance using data from an /obstacles topic, ensuring all planned paths were free from collisions.
- Inverse Kinematics (IK): Utilized IK functions to convert end-effector goals into joint space configurations, essential for effective motion planning.
- Path Optimization: Implemented path shortcutting and resampling techniques to refine the planned trajectories, improving execution efficiency and ensuring smooth movement.
