Control Barrier Functions (CBF) are widely used to enforce the safety-critical constraints on nonlinear systems. Recently, these functions are being incorporated into a path planning framework to design a safety-critical path planner . However, these methods fall short of providing a realistic path considering both  run-time complexity and  safety-critical constraints. This paper proposes a novel motion planning approach using Rapidly exploring Random Trees (RRT) algorithm to enforce the robust CBF and kinodynamic constraints to generate a safety-critical path that is free of any obstacles while taking into account the model uncertainty from robot dynamics as well as perception. Result analysis indicates that the proposed method outperforms various conventional RRT based path planners, guaranteeing a safety-critical path with reduced computational overhead. We present numerical validation of the algorithm on the Hamster V7 robot car, a micro autonomous Unmanned Ground Vehicle , where it performs dynamic navigation on an obstacle-ridden path with various uncertainties in perception noises , and robot dynamics.
Control Barrier Functions (CBF) are widely used to enforce the safety-critical constraints on nonlinear systems. Recently, these functions are being incorporated into a path planning framework to design safety-critical path planners . However, these methods fall short of providing a realistic path considering both the algorithm's run-time complexity and enforcement of the safety-critical constraints. This paper proposes a novel motion planning approach using the well-known Rapidly Exploring Random Trees (RRT) algorithm that enforces both CBF and the robot Kinodynamic constraints to generate a safety-critical path . The proposed algorithm also outputs the corresponding control signals that resulted in the obstacle-free path. The approach also allows considering model uncertainties by incorporating the robust CBF constraints into the proposed framework. Thus, the resulting path is free of any obstacles and accounts for the model uncertainty from robot dynamics and perception. Result analysis indicates that the proposed method outperforms various conventional RRT-based path planners, guaranteeing a safety-critical path with minimal computational overhead. We present numerical validation of the algorithm on the Hamster V7 robot car, a micro autonomous Unmanned Ground Vehicle that performs dynamic navigation on an obstacle-ridden path with various uncertainties in perception noises and robot dynamics.
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