The optimization of trajectories for multiple robotic arms in a shared workspace is critical for industrial automation but presents significant challenges, including data sharing, communication overhead, and adaptability in dynamic environments. Traditional centralized control methods require sharing raw sensor data, raising concerns and creating computational bottlenecks. This paper proposes a novel Federated Learning (FL) framework for distributed multi-robotic arm trajectory optimization. Our method enables collaborative learning where robots train a shared model locally and only exchange gradient updates, preserving data privacy. The framework integrates an adaptive Rapidly exploring Random Tree (RRT) algorithm enhanced with a dynamic pruning strategy to reduce computational overhead and ensure collision-free paths. Real-time synchronization is achieved via EtherCAT, ensuring precise coordination. Experimental results demonstrate that our approach achieves a 17% reduction in average path length, a 22% decrease in collision rate, and a 31% improvement in planning speed compared to a centralized RRT baseline, while reducing inter-robot communication overhead by 45%. This work provides a scalable and efficient solution for collaborative manipulation in applications ranging from assembly lines to warehouse automation.
Khan et al. (Mon,) studied this question.