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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| Veröffentlicht in: | Robotica Jg. 42; H. 9; S. 2991 - 3004 |
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| Sprache: | Englisch |
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Cambridge, UK
Cambridge University Press
01.09.2024
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| ISSN: | 0263-5747, 1469-8668 |
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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. |
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| AbstractList | 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. |
| Author | Zhao, Wenlong Zhang, Suyu Liu, Ruijun Guo, Liang Liu, Jun |
| Author_xml | – sequence: 1 givenname: Liang surname: Guo fullname: Guo, Liang organization: College of Information Engineering, Nanchang Hangkong University, Nanchang, China – sequence: 2 givenname: Suyu orcidid: 0009-0000-7609-4697 surname: Zhang fullname: Zhang, Suyu email: 1228474146@qq.com organization: College of Information Engineering, Nanchang Hangkong University, Nanchang, China – sequence: 3 givenname: Wenlong surname: Zhao fullname: Zhao, Wenlong organization: College of Information Engineering, Nanchang Hangkong University, Nanchang, China – sequence: 4 givenname: Jun surname: Liu fullname: Liu, Jun organization: College of Information Engineering, Nanchang Hangkong University, Nanchang, China – sequence: 5 givenname: Ruijun surname: Liu fullname: Liu, Ruijun organization: Jiangxi DonGRUI MACHINERY CO. LTD. Nanchang, China |
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| Keywords | model predictive control control barrier functions collision avoidance obstacle recognition navigation in complex environments |
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| SubjectTerms | Adaptive control Algorithms Clustering Dynamic control Feasibility Kalman filters Kinematics Noise prediction Obstacle avoidance Optimization Parameterization Predictive control Terrain Unmanned ground vehicles Vehicles |
| Title | Obstacle avoidance control of UGV based on adaptive-dynamic control barrier function in unstructured terrain |
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