LiDAR-based Model Predictive Control using Control Barrier Functions - Tooranjipour, Pouria; Kiumarsi, Bahare (Proceedings of the American Control Conference, 2025)
This paper presents a model predictive control (MPC) with recursive feasibility guarantees for robots with LiDAR sensory measurements. Control barrier functions (CBF) are incorporated within the MPC framework to shorten the prediction horizon of MPC compared to the standard MPC, effectively reducing computational complexity while ensuring the avoidance of unsafe sets. A CBF is synthesized from 2D LiDAR data points by first clustering obstacles using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and then fitting an ellipse to each cluster using the OpenCV library's fitting tool. The resulting CBFs from each obstacle are subsequently unified and integrated into the MPC framework. The recursive feasibility of the proposed MPC is analyzed and guaranteed by choosing an appropriate terminal set. The effectiveness of the approach is demonstrated through simulations in the Gazebo robotic simulator using ROS 2, followed by experimental validation with a unicycle-type robot equipped with a LiDAR sensor.