Tendon-driven continuum robots (TDCRs), with their flexible backbones, offer the advantage of being used for navigating complex, cluttered environments. However, to do so, they typically require multiple segments, often leading to complex actuation and control challenges. To this end, we propose a novel approach to navigate cluttered spaces effectively for a single-segment long TDCR which is the simplest topology from a mechanical point of view. Our key insight is that by leveraging contact with the environment we can achieve multiple curvatures without mechanical alterations to the robot. Specifically, we propose a search-based motion planner for a single-segment TDCR. This planner, guided by a specially designed heuristic, discretizes the configuration space and employs a best-first search. The heuristic, crucial for efficient navigation, provides an effective cost-to-go estimation while respecting the kinematic constraints of the TDCR and environmental interactions. We empirically demonstrate the efficiency of our planner-testing over 525 queries in environments with both convex and non-convex obstacles, our planner is demonstrated to have a success rate of about 80% while baselines were not able to obtain a success rate higher than 30%. The difference is attributed to our novel heuristic which is shown to significantly reduce the required search space.

翻译：腱驱动连续体机器人（TDCR）凭借其柔性骨架，在复杂、杂乱环境中导航具有优势。然而，实现这一目标通常需要多个分段，往往导致驱动与控制上的复杂挑战。为此，我们提出一种新方法，使机械结构最简单的单段长TDCR能有效穿越杂乱空间。我们的关键思路是：通过利用与环境接触，可在不改变机器人机械结构的情况下实现多种曲率。具体而言，我们为单段TDCR设计了一种基于搜索的运动规划器。该规划器在特殊设计的启发式函数引导下，对配置空间进行离散化并采用最佳优先搜索。这一启发式函数在考虑TDCR运动学约束与环境交互的同时，提供有效的代价估计，对高效导航至关重要。我们通过实验验证了规划器的效率：在包含凸面与非凸面障碍物的环境中进行525次查询测试，该规划器的成功率约为80%，而基准方法的成功率均未超过30%。这一差异归功于我们新颖的启发式函数，它显著缩减了所需的搜索空间。