LiDAR-based Model Predictive Control using Control Barrier Functions

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...

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Bibliographic Details
Published in:Proceedings of the American Control Conference pp. 315 - 322
Main Authors: Tooranjipour, Pouria, Kiumarsi, Bahare
Format: Conference Proceeding
Language:English
Published: AACC 08.07.2025
Subjects:
Collision avoidance
Computational complexity
Control Barrier Function
Fitting
Laser radar
LiDAR
Model Predictive Control
Navigation
Prediction algorithms
Predictive control
Robot sensing systems
ROS
Safety
ISSN:2378-5861
Online Access:Get full text
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Summary: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.
ISSN:2378-5861
DOI:10.23919/ACC63710.2025.11107424