Federated Learning for Distributed Multi-Robotic Arm Trajectory Optimization

Manipulator path and trajectory planning is a significant aspect of robotics that involves defining an optimal trajectory for the manipulator to move from a starting position to a target position while avoiding obstacles and minimizing latency factors such as time, energy, or jerk. This process typically involves algorithms that analyze the manipulator’s kinematic and dynamic constraints, including workspace object geometry and environmental obstacles, to generate a collision-free and efficient trajectory. Common techniques include sampling-based methods like Rapidly-exploring Random Trees (RRT) or Probabilistic Roadmaps (PRM), optimization-based approaches, and artificial potential fields that treat obstacles as repulsive forces and goals as attractive forces. Advanced path planning may also incorporate machine learning for adaptive navigation in dynamic environments. The goal is to ensure smooth, precise, and safe motion. Efficient path planning enhances performance, reduces wear and tear, and ensures operational reliability.