Improving the scalability of asymptotically optimal motion planning for humanoid dual-arm manipulators

Abstract

Due to high-dimensionality, many motion planners for dual-arm systems follow a decoupled approach, which does not provide guarantees. Asymptotically optimal sampling-based planners provide guarantees but in practice face scalability challenges. This work improves the computational scalability of the latter methods in this domain. It builds on top of recent advances in multi-robot motion planning, which provide guarantees without having to explicitly construct a roadmap in the composite space of all robots. The proposed framework builds roadmaps for components of a humanoid robot's kinematic chain. Then, the tensor product of these component roadmaps is searched implicitly online in a way that asymptotic optimality is provided. Appropriate heuristics from the component roadmaps are utilized for discovering the solution in the composite space effectively. Evaluation on various dual-arm problems show that the method returns paths of increasing quality, has significantly reduced space requirements and improved convergence rate relative to the standard asymptotically optimal approaches.