Safety Control of a Hyper-redundant Robot via Adaptive Weighted Control Barrier Functions

2026-09-20

AI Summary

本文针对超冗余机器人在狭小空间操作时的安全性和跟踪误差问题，提出了一种自适应加权控制屏障函数（W-CBFs）方法，有效减少了由于不均匀负载导致的跟踪误差。

Abstract

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.

Problem

• Hyper-redundant robots
• Safety Control
• Tracking Errors
• Uneven Load Distribution

Innovation

• weighted control barrier functions
• adaptive weighting strategy
• uneven loading
• tracking error reduction
• safe operation