Deep MARL-Based Resilient Motion Planning for Decentralized Space Manipulator

Abstract

Space manipulators play an important role in the on-orbit services and planetary surface operation. In the extreme environment of space, space manipulators are susceptible to a variety of unknown disturbances. How to have a resilient guarantee in failure or disturbance is the core capability of its future development. Compared with traditional motion planning, learning-based motion planning has gradually become a hot spot in current research. However, no matter what kind of research ideas, the single robotic manipulator is studied as an independent agent, making it unable to provide sufficient flexibility under conditions such as external force disturbance, observation noise, and mechanical failure. Therefore, this paper puts forward the idea of “discretization of the traditional single manipulator”. Different discretization forms are given through the analysis of the multi-degree-of-freedom single-manipulator joint relationship, and a single-manipulator representation composed of multiple new subagents is obtained. Simultaneously, to verify the ability of the new multiagent representation to deal with interference, we adopted a centralized multiagent reinforcement learning framework. The influence of the number of agents and communication distances on learning-based planning results is analyzed in detail. In addition, by imposing joint locking failures on the manipulator and introducing observation and action interference, it is verified that the “multiagent robotic manipulator” obtained after discretization has stronger antidisturbance resilient capability than the traditional single manipulator.