Motion Planning for a Climbing Robot with Stochastic Grasps

TL;DR

This paper presents a motion planning approach for ReachBot, a climbing robot with extendable booms and stochastic grasping, enabling navigation in unpredictable terrains like martian caves with high success probability.

Contribution

It introduces a novel graph traversal and decoupled motion planning framework tailored for a multi-limbed climbing robot with stochastic grasps in complex environments.

Findings

Achieved at least 95% success probability in simulated 2D cave navigation.

Demonstrated improved robustness over baseline trajectories.

Validated planning algorithm on a 2D prototype.

Abstract

Motion planning for a multi-limbed climbing robot must consider the robot's posture, joint torques, and how it uses contact forces to interact with its environment. This paper focuses on motion planning for a robot that uses nontraditional locomotion to explore unpredictable environments such as martian caves. Our robotic concept, ReachBot, uses extendable and retractable booms as limbs to achieve a large reachable workspace while climbing. Each extendable boom is capped by a microspine gripper designed for grasping rocky surfaces. ReachBot leverages its large workspace to navigate around obstacles, over crevasses, and through challenging terrain. Our planning approach must be versatile to accommodate 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 95% probability of success and demonstrate improved robustness over a baseline trajectory. Finally, we verify our motion planning algorithm through experimentation on a 2D planar prototype.