Dynamic obstacle avoidance for car-like mobile robots based on neurodynamic optimization with control barrier functions

This paper studies the problem of dynamic obstacle avoidance for car-like mobile robots with physical limitations, including bounded steering angles and input saturation. By designing control barrier functions (CBFs), a novel collision-free module is proposed, which applies minimal adjustments to th...

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Bibliographic Details
Published inNeurocomputing (Amsterdam) Vol. 654; p. 131252
Main Authors Zhang, Zheng, Yang, Guang-Hong
Format Journal Article
LanguageEnglish
Published Elsevier B.V 14.11.2025
Subjects
Car-like mobile robots
Control barrier functions
Dynamic obstacle avoidance
Neurodynamic optimization
Control barrier functions
Car-like mobile robots
Dynamic obstacle avoidance
Neurodynamic optimization
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Summary:This paper studies the problem of dynamic obstacle avoidance for car-like mobile robots with physical limitations, including bounded steering angles and input saturation. By designing control barrier functions (CBFs), a novel collision-free module is proposed, which applies minimal adjustments to the velocity commands of a given controller when necessary to ensure dynamic obstacle avoidance motion under the physical limitations. Specifically, the module is implemented as a quadratic program that is both subject to constraints derived from the CBFs and solved in real-time through neurodynamic optimization. Compared with the existing results, the proposed scheme ensures real-time generation of physically realizable safe commands and enhances steering maneuverability for obstacle avoidance. Finally, the safety of the designed method is theoretically proven, and its effectiveness in dynamic obstacle avoidance is verified via numerical simulations.
ISSN:0925-2312
DOI:10.1016/j.neucom.2025.131252