Motion Planning for a Climbing Robot with Stochastic Grasps

Abstract

ReachBot is a robot that uses extendable and retractable booms as limbs to move around unpredictable environments such as martian caves. Each boom is capped by a microspine gripper designed for grasping rocky surfaces. Motion planning for ReachBot must be versatile to accommo-date variable terrain features and robust to mitigate risks from the stochastic nature of grasping with spines. In this paper, we introduce a graph traversal algorithm to select a discrete sequence of grasps based on available terrain features suitable for grasping. This discrete plan is complemented by a decoupled motion planner that considers the alternating phases of body movement and end-effector movement, using a combination of sampling-based planning and sequential convex programming to optimize individual phases. We use our motion planner to plan a trajectory across a simulated 2D cave environment with at least 90% probability of success and demonstrate improved robustness over a baseline trajectory. Finally, we use a simplified prototype to verify a body movement trajectory generated by our motion planning algorithm.