🤖 AI Summary
In densely cluttered environments, conventional binary collision detection restricts robotic workspace and hinders controllable physical interaction. To address this, we propose a contact-aware motion planning method. Our approach introduces a link-level contact-acceptability cost heuristic, coupled with a dynamic adaptive thresholding mechanism to enable coordinated exploration across low- and high-cost regions. Within an enhanced RRT framework, we integrate contact-sensitive state representation, local link-wise cost evaluation, and threshold-guided sampling and rewiring strategies. Experimental results demonstrate substantial improvements in planning success rate and convergence speed. Compared to state-of-the-art optimization-based planners, our method achieves superior performance in path length, contact accuracy, and computational efficiency.
📝 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)11Supplementary video and open source code [1]., 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.
