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Obstacle avoidance control of UGV based on adaptive-dynamic control barrier function in unstructured terrain

Published online by Cambridge University Press:  18 September 2024

Liang Guo ,
Suyu Zhang Open the ORCID record for Suyu Zhang [Opens in a new window] ,
Wenlong Zhao ,
Jun Liu  and
Ruijun Liu
Liang Guo
Affiliation:
College of Information Engineering, Nanchang Hangkong University, Nanchang, China
Suyu Zhang*
Affiliation:
College of Information Engineering, Nanchang Hangkong University, Nanchang, China
Wenlong Zhao
Affiliation:
College of Information Engineering, Nanchang Hangkong University, Nanchang, China
Jun Liu
Affiliation:
College of Information Engineering, Nanchang Hangkong University, Nanchang, China
Ruijun Liu
Affiliation:
Jiangxi DonGRUI MACHINERY CO. LTD. Nanchang, China
*
Corresponding author: Suyu Zhang; Email: [email protected]
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Abstract

The widely used model predictive control of discrete-time control barrier functions (MPC-CBF) has difficulties in obstacle avoidance for unmanned ground vehicles (UGVs) in complex terrain. To address this problem, we propose adaptive dynamic control barrier functions (AD-CBF). AD-CBF is able to adaptively select an extended class of functions of CBF to optimize the feasibility and flexibility of obstacle avoidance behaviors based on the relative positions of the UGV and the obstacle, which in turn improves the obstacle avoidance speed and safety of the MPC algorithm when integrated with MPC. The algorithmic constraints of the CBF employ hierarchical density-based spatial clustering of applications with noise (HDBSCAN) for parameterization of dynamic obstacle information and unscaled Kalman filter (UKF) for trajectory prediction. Through simulations and practical experiments, we demonstrate the effectiveness of the AD-CBF-MPC algorithm in planning optimal obstacle avoidance paths in dynamic environments, overcoming the limitations of the point-by-point feasibility of MPC-CBF.

Keywords

navigation in complex environmentscollision avoidanceobstacle recognitioncontrol barrier functionsmodel predictive control
Type
Research Article
Information
Robotica , Volume 42 , Issue 9 , September 2024 , pp. 2991 - 3004
Copyright
© The Author(s), 2024. Published by Cambridge University Press

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