CAT-RRT: Motion Planning that Admits Contact One Link at a Time

TL;DR

This paper introduces CAT-RRT, a motion planner that effectively incorporates contact states into planning by using a per-link cost heuristic, enabling robots to navigate densely cluttered environments more efficiently.

Contribution

The paper presents a novel contact-aware planning algorithm, CAT-RRT, which balances contact and obstacle avoidance using adaptive thresholding, improving over traditional binary collision checking methods.

Findings

CAT-RRT finds more efficient paths in cluttered spaces.

It balances contact and obstacle avoidance effectively.

Compared to optimization planners, it converges faster to contact regions.

Abstract

Current motion planning approaches rely on binary collision checking to evaluate the validity of a state and thereby dictate where the robot is allowed to move. This approach leaves little room for robots to engage in contact with an object, as is often necessary when operating in densely cluttered spaces. In this work, we propose an alternative method that considers contact states as high-cost states that the robot should avoid but can traverse if necessary to complete a task. More specifically, we introduce Contact Admissible Transition-based Rapidly exploring Random Trees (CAT-RRT), a planner that uses a novel per-link cost heuristic to find a path by traversing high-cost obstacle regions. Through extensive testing, we find that state-of-the-art optimization planners tend to over-explore low-cost states, which leads to slow and inefficient convergence to contact regions. Conversely, CAT-RRT searches both low and high-cost regions simultaneously with an adaptive thresholding mechanism carried out at each robot link. This leads to paths with a balance between efficiency, path length, and contact cost.