发表机构
Singapore-MIT Alliance for Research and Technology (SMART) Centre; Massachusetts Institute of Technology; Nanyang Technological University; National University of Singapore（新加坡-麻省理工学院研究与技术联盟中心; 麻省理工学院; 南洋理工大学; 新加坡国立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对超冗余机器人在杂乱环境中的安全与跟踪误差问题，提出加权控制障碍函数框架，通过自适应权重显著降低跟踪误差，并在清洁任务中实现优于手动操作的覆盖性能。
AI 中文摘要
超冗余机器人因其高灵活性而非常适合受限空间操作，但在杂乱环境中安全操作仍然具有挑战性。此外，其细长结构常常导致负载分布不均和沿身体的不均匀跟踪误差。为了解决这些问题，本工作提出了一种加权控制障碍函数（W-CBFs）框架，该框架在强制执行安全约束的同时，减少由不均匀负载引起的跟踪误差。所提出的控制器首先在不同障碍物配置下的圆形路径跟踪任务中进行了评估。在固定权重下，与非加权方法相比，模拟中均方根（RMS）跟踪误差的最大减少为59.6%，物理实验中为87.7%。随后，基于不同映射函数下模拟与实验性能之间的差异，研究了一种自适应加权策略。RMS误差进一步分别减少了21.9%和8.5%，尽管当障碍物靠近机器人本体时误差会增加。最后，在一个需要覆盖矩形区域的清洁任务中评估了该机器人，并与手动遥控操作进行了比较。尽管控制器并未针对区域覆盖进行显式优化，但自主策略在避免与周围框架碰撞的同时实现了相当或更好的覆盖性能，而手动操作期间发生了碰撞。
英文摘要
Hyper-redundant robots are well suited for confined-space manipulation due to their high dexterity, but safe operation in cluttered environments remains challenging. In addition, their slender structures often lead to uneven load distributions and nonuniform tracking errors along the body. To address these issues, this work proposes a weighted control barrier functions (W-CBFs) framework that enforces safety constraints while reducing tracking errors caused by uneven loading. The proposed controller was first evaluated on a circular path-following task under different obstacle configurations. With fixed weights, compared to the non-weighted method, the maximum reduction in root-mean-square (RMS) tracking error was 59.6\% in simulation and 87.7\% in physical experiments. An adaptive weighting strategy was then investigated based on the discrepancy between simulated and experimental performance under different mapping functions. The RMS errors were further reduced by 21.9\% and 8.5\%, respectively, although the error increases when obstacles were located close to the robot body. Finally, the robot was evaluated in a cleaning task requiring coverage of a rectangular area and compared with manual teleoperation. Although the controller was not explicitly optimized for area coverage, the autonomous strategy achieved comparable or better coverage performance while avoiding collisions with the surrounding frame, whereas collisions occurred during manual operation.
