Weighted control improves safety and precision of flexible robot arms
Safety Control of a Hyper-redundant Robot via Adaptive Weighted Control Barrier Functions
Robotics
Summary
Controlling long, flexible robot arms that work in tight spaces is tricky because they can bend unevenly and hit obstacles. The authors developed a new control method that uses weighted rules to keep the robot safe while reducing its tracking errors caused by uneven loads. They tested it both in simulations and real trials, showing better performance than previous methods. The system also adapts its control weights over time to improve accuracy further, and it performed well in a cleaning task compared to manual control.
What this means in practice
- •For robotics engineers: Use adaptive weighted control functions to enable safe and precise movement of slender, flexible robot arms in cluttered, confined spaces.
- •For industrial automation teams: Automate area coverage tasks with flexible robots that avoid collisions better than manual teleoperation in constrained environments.
Authors
Zijian Cai, Kiwan Wong, Wenci Xin, Wei Xiao, Daniela Rus, Cecilia Laschi
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.