Point Cloud-Based Control Barrier Functions for Model Predictive Control in Safety-Critical Navigation of Autonomous Mobile Robots

In this work, we propose a novel motion planning algorithm to facilitate safety-critical navigation for autonomous mobile robots. The proposed algorithm integrates a real-time dynamic obstacle tracking and mapping system that categorizes point clouds into dynamic and static components. For dynamic point clouds, the Kalman filter is employed to estimate and predict their motion states. Based on these predictions, we extrapolate the future states of dynamic point clouds, which are subsequently merged with static point clouds to construct the forward-time-domain (FTD) map. By combining control barrier functions (CBFs) with nonlinear model predictive control, the proposed algorithm enables the robot to effectively avoid both static and dynamic obstacles. The CBF constraints are formulated based on risk points identified through collision detection between the predicted future states and the FTD map. Experimental results from both simulated and real-world scenarios demonstrate the efficacy of the proposed algorithm in complex environments. In simulation experiments, the proposed algorithm is compared with two baseline approaches, showing superior performance in terms of safety and robustness in obstacle avoidance. The source code is released for the reference of the robotics community.