Hybrid Feedback Control Design for Nonconvex Obstacle Avoidance

• 成果类型: Article
• 署名作者: Sawant, Mayur; Polushin, Ilia; Tayebi, Abdelhamid
• 署名单位: Western University (University of Western Ontario); Western University (University of Western Ontario); Lakehead University
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2024.3388952
• 发表日期: 2024
• 页码: 7508-7523
• 关键词: NAVIGATION; CONVERGENCE; vectors; Robot sensing systems; autonomous robots; Collision avoidance; Noise measurement; Autonomous robot navigation; hybrid control systems; obstacle avoidance

摘要

We develop an autonomous navigation algorithm for a robot operating in 2-D environments containing obstacles, with arbitrary nonconvex shapes, which can be in close proximity with each other, as long as there exists at least one safe path connecting the initial and the target location. An instrumental transformation that modifies (virtually) the nonconvex obstacles, in a nonconservative manner, is introduced to facilitate the design of the obstacle-avoidance strategy. The proposed navigation approach relies on a hybrid feedback that guarantees global asymptotic stabilization of a target location while ensuring the forward invariance of the modified obstacle-free workspace. The proposed hybrid feedback controller guarantees Zeno-free switching between the move-to-target mode and the obstacle-avoidance mode based on the proximity of the robot with respect to the modified obstacle-occupied workspace. Finally, we provide an algorithmic procedure for the sensor-based implementation of the proposed hybrid controller and validate its effectiveness via some numerical simulations.