📝 SummarXiv

Abstract

Control barrier functions (CBFs) provide a theoretical foundation for safety-critical control in robotic systems. However, most existing methods rely on the analytical expressions of unsafe state regions, which are often impractical for irregular and dynamic unsafe regions. This paper introduces SafeLink, a novel CBF construction method based on cost-sensitive incremental random vector functional-link (RVFL) neural networks. By designing a valid cost function, SafeLink assigns different sensitivities to safe and unsafe state points, thereby eliminating false negatives in classification of unsafe state points. Furthermore, an incremental update theorem is established, enabling precise real-time adaptation to changes in unsafe regions. An analytical expression for the gradient of SafeLink is also derived to facilitate control input computation. The proposed method is validated on the endpoint position control task of a nonlinear two-link manipulator. Experimental results demonstrate that the method effectively learns the unsafe regions and rapidly adapts as these regions change, achieving an update speed significantly faster than comparison methods, while safely reaching the target position. The source code is available at https://github.com/songqiaohu/SafeLink.