Safety-critical model predictive control with control barrier function for dynamic obstacle avoidance
In this paper, a safety critical control scheme for a nonholonomic robot is developed to generate control signals that result in optimal obstacle-free paths through dynamic environments. A barrier function is used to obtain a safety envelope for the robot. We formulate the control synthesis problem as an optimal control problem that enforces control barrier function (CBF) constraints to achieve obstacle avoidance. A nonlinear model predictive control (NMPC) with CBF is studied to guarantee system safety and accomplish optimal performance at a short prediction horizon, which reduces computational burden in real-time NMPC implementation. An obstacle avoidance constraint under the Euclidean norm is also incorporated into NMPC to emphasize the effectiveness of CBF in both point stabilization and trajectory tracking problem of the robot. The performance of the proposed controller achieving both static and dynamic obstacle avoidance is verified using several simulation scenarios.
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