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Real-Time Sensor-Based Feedback Control for Obstacle Avoidance in Unknown Environments

We revisit the Safety Velocity Cones (SVCs) obstacle avoidance approach for real-time autonomous navigation in an unknown $n$-dimensional environment. We propose a locally Lipschitz continuous implementation of the SVC controller using the distance-to-the-obstacle function and its gradient. We then show that the proposed implementation guarantees safe navigation in generic environments and almost globally asymptotic stability (AGAS) of the desired destination when the workspace contains strongly convex obstacles. The proposed computationally efficient control algorithm can be implemented onboard vehicles equipped with limited range sensors (e.g., LiDAR, depth camera), allowing the controller to be locally evaluated without requiring prior knowledge of the environment.

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