Safety-Critical Control with Saliency Detection for Mobile Robots in Dynamic Multi-Obstacle Environments

This paper proposes a novel dual-filter architecture utilizing RGB-D camera data and dynamic control barrier functions (D-CBFs) for real-time obstacle avoidance in unstructured environments. The proposed method efficiently handles static, suddenly appearing, and dynamic obstacles, maintaining consistent computational performance across diverse scenarios. To achieve this, two key challenges must be addressed. First, the substantial volume of pixel and depth map data requires robust, real-time processing for efficient D-CBF construction. Second, constructing D-CBFs for each obstacle in multi-obstacle scenarios increases optimization solver time. To address these challenges, we adapt the concept of salient object detection (SOD), proposing an enhanced FastSOD (E-FastSOD) method for rapid risk area identification. This approach rapidly filters out low-risk areas, while high-risk regions are mathematically represented utilizing the proposed enhanced minimal bounding circle (E-MBC) technique. We differentiate static and dynamic obstacles by comparing current and previous MBC states, employing Kalman filtering for obstacle state prediction. This setup enables efficient online D-CBF construction for each MBC, balancing computational speed with accurate obstacle representation. Subsequently, the second filter establishes buffer zones around established D-CBFs, activating only those corresponding to zones the robot actually enters, rather than all D-CBFs to increase real-time performance. We prove the system's safety and asymptotic stabilization under this architecture. Simulated and real-world experiments validate our method, demonstrating an equipped mobile robot's ability to accomplish tasks while ensuring safety across diverse, unknown scenarios.