面向机器人导航的安全鲁棒管型路径跟踪方法
Safe and robust tube-based path-following for robot navigation
AI总结：
该研究针对未知杂乱环境中的机器人路径跟踪任务，提出含标称制导控制、平滑避障、统一控制目标与自适应组件的管型控制导航框架，经仿真验证可保障安全鲁棒性。
AI中文摘要：
本文针对在未知杂乱环境中作业的机器人路径跟踪任务，提出一种新型鲁棒导航框架。该方法通过避障实现反应式安全保障，确保收敛至目标路径，同时采用管型控制策略抑制有界未知扰动的影响。该方法整合了机器人导航的关键要素：（i）采用人工势场制导与反步控制的标称综合制导与控制方案用于路径跟踪；（ii）用于构建连续控制律以实现无缝避障的平滑距离函数；（iii）平衡避障与路径跟踪的统一控制目标；（iv）用于提升外部扰动鲁棒性的自适应控制组件。我们利用障碍函数与李雅普诺夫稳定性理论对安全性与稳定性给出形式化证明，通过大量数值仿真验证了所提框架的有效性。
英文摘要：
In this paper, we propose a new robust navigation framework for path following tasks in robots operating within unknown, cluttered environments. Our approach ensures reactive safety through obstacle avoidance and guaranteed convergence to a target path, while simultaneously mitigating the impact of unknown-but-bounded disturbances using a tube-based control strategy. The methodology integrates key aspects in the robot navigation: (i) a nominal Integrated Guidance and Control scheme for path-following employing Artificial Vector Fields guidance and Backstepping control; (ii) a smooth distance function that enables a continuous control law formulation for seamless obstacle avoidance; (iii) a unified control objective that balances collision avoidance with path-following; and (iv) an adaptive control component to provide robustness against external disturbances. We provide formal proofs of safety and stability using barrier functions and Lyapunov stability theory. The effectiveness of the proposed framework is validated through extensive numerical simulations.
