CN-CBF: Composite Neural Control Barrier Function for Robot Navigation in Dynamic Environments
Safe navigation of autonomous robots remains one of the core challenges in the field, especially in dynamic and uncertain environments. One prevalent approach is safety filtering based on control barrier functions (CBFs), which are easy to deploy but difficult to design. Motivated by the shortcomings of existing learning- and model-based methods, we propose a simple yet effective neural CBF design method for safe robot navigation in dynamic environments. We employ the idea of a composite CBF, where multiple neural CBFs are combined into a single CBF. Individual CBFs are trained using data generated offline via the Hamilton-Jacobi reachability framework to approximate the optimal safe set ...
| Authors | Bojan Derajić, Sebastian Bernhard, Wolfgang Hönig |
|---|---|
| Primary category | cs.RO |
| Published | 6 March 2026 |
| Last updated at source | 6 July 2026 |
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