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

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 adap...

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Bibliographic Details
Published in:Robotica Vol. 42; no. 9; pp. 2991 - 3004
Main Authors: Guo, Liang, Zhang, Suyu, Zhao, Wenlong, Liu, Jun, Liu, Ruijun
Format: Journal Article
Language:English
Published: Cambridge, UK Cambridge University Press 01.09.2024
Subjects:
Adaptive control
Algorithms
Clustering
Dynamic control
Feasibility
Kalman filters
Kinematics
Noise prediction
Obstacle avoidance
Optimization
Parameterization
Predictive control
Terrain
Unmanned ground vehicles
Vehicles
model predictive control
control barrier functions
collision avoidance
obstacle recognition
navigation in complex environments
ISSN:0263-5747, 1469-8668
Online Access:Get full text
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Summary: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.
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ISSN:0263-5747
1469-8668
DOI:10.1017/S026357472400122X