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Safe control in unknown environments is a significant challenge in robotics. While Control Barrier Functions (CBFs) are widely used to guarantee system safety, they often assume known environments with predefined obstacles. The proposed method constructs CBFs directly from perception sensor input and introduces a new first-order barrier function for a 3D kinematic robot motion model. The proposed CBF is constructed by combining Occupancy Grid Mapping (OGM) and Signed Distance Functions (SDF). The OGM framework abstracts sensor inputs, making the solution compatible with any sensor modality capable of generating occupancy maps. Moreover, the OGM enhances situational awareness along the robot's motion trajectory, by integrating both current and previously mapped data. The SDF encapsulates complex obstacle shapes defined by OGM into real-time computable values, enabling the method to handle obstacles of arbitrary shapes. This enables a single constraint in the CBF-QP optimization for each point on the robot, regardless of the number or shape of obstacles. The effectiveness of the proposed approach is demonstrated through simulations on autonomous driving in the CARLA simulator and real-world experiments with an industrial mobile robot, using a simplified 2D version of the method. |
