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.

翻译：当前的运动规划方法依赖于二元碰撞检测来评估状态的有效性，从而决定机器人允许移动的区域。这种方法使得机器人与物体进行接触的空间极为有限，而在密集杂乱环境中操作时，这种接触往往是必需的。本研究提出了一种替代方案，将接触状态视为高成本状态——机器人应尽量避免，但为完成任务必要时可穿越。具体而言，我们引入了基于接触许可转换的快速探索随机树（CAT-RRT），该规划器采用新颖的单连杆成本启发式方法，通过穿越高成本障碍区域寻找路径。通过大量测试发现，现有优化规划器倾向于过度探索低成本状态，导致向接触区域收敛缓慢且低效。相比之下，CAT-RRT通过在每个机器人连杆处执行自适应阈值机制，同步搜索低成本与高成本区域，从而在路径效率、长度与接触成本之间取得平衡。