Safe and Robust Motion Planning for Dynamic Robotics via Control Barrier Functions

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.