An Efficient Planner for Dual-Arm Massage Robots in Constrained State

Dual-arm massage robots necessitate reaching the massage pose along an optimal collision-free path in constrained state, where traditional path planning methods often exhibit inefficiency. This paper proposes a sampling-based planning algorithm that integrates lazy evaluation, transition testing, and bidirectional Rapidly-exploring Random Tree to reduce computational load, enhance planning efficiency, and improve the planner's ability to effectively explore the state space, avoid local minima, and converge to high-quality solutions. Comparative experiments conducted on a simulation platform across multiple algorithms have demonstrated the efficacy of the proposed planner, achieving a 95 % planning success rate within 1 second. The paths generated by the algorithm exhibit superior quality and enhanced consistency. In summary, the proposed algorithm is capable of producing asymptotically optimal, collision-free paths for a dual-arm massage robot.