Cooperative Dynamic Motion Planning for Dual Manipulator Arms Based on RRT*Smart-AD Algorithm.

Area of Science:

• Robotics and Intelligent Systems
• Artificial Intelligence
• Manufacturing Engineering

Background:

• Dual-arm cooperative operation is essential for intelligent manufacturing, requiring advanced motion planning.
• Existing algorithms like RRT and RRT*Smart have limitations in handling dynamic environments and cooperative tasks for dual manipulators.
• There is a need for improved motion planning that addresses sampling, optimization, and dynamic adaptability for dual-arm systems.

Purpose of the Study:

• To propose a novel motion planner, RRT*Smart-AD, for dual-arm robots operating in dynamic environments.
• To enhance cooperative motion planning by addressing sampling methods, path optimization, and dynamic adaptability.
• To ensure generated trajectories satisfy obstacle avoidance, differential, and physical collision constraints.

Main Methods:

• Introduced a dynamic A* cost function sampling method combined with intelligent beacon sampling.
• Implemented a path-pruning strategy for improved computational efficiency.
• Developed strategies for dynamic region path repair and regrowth for enhanced adaptability.
• Utilized Particle Swarm Optimization (PSO) to optimize quintic non-uniform rational B-splines (NURBS) trajectories, considering constraints like velocity and acceleration.

Main Results:

• The RRT*Smart-AD algorithm demonstrated superior performance in cooperative dynamic path planning for dual robotic arms compared to biased RRT* and RRT*Smart.
• Generated smooth motion trajectories that comply with differential and physical collision constraints in dynamic scenarios.
• Successfully addressed obstacle avoidance and dynamic characteristics for dual-arm robots.

Conclusions:

• RRT*Smart-AD offers a robust solution for cooperative motion planning of dual-arm robots in intelligent factories.
• The proposed method provides significant practical engineering value for obstacle avoidance and dynamic path planning.
• This research offers a theoretical reference for path planning in other robotic systems operating in complex environments.