Hostname: page-component-6766d58669-7fx5l Total loading time: 0 Render date: 2026-05-23T16:25:38.236Z Has data issue: false hasContentIssue false
  • English
  • Français

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: 1228474146@qq.com
Get access Rights & Permissions [Opens in a new window]

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

Information

Type
Research Article
Information
Robotica , Volume 42 , Issue 9 , September 2024 , pp. 2991 - 3004
Copyright
© The Author(s), 2024. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Mohamed, I. S., Yin, K. and Liu, L., “Autonomous navigation of AGVs in unknown cluttered environments: Log-MPPI control strategy,” IEEE Robot Autom Lett 7(4), 10240–10247 (2022).CrossRefGoogle Scholar
MacLachlan, R. and Mertz, C., “Tracking of Moving Objects from a Moving Vehicle Using a Scanning Laser Rangefinder,” In: IEEE Intelligent Transportation Systems Conference, Edmonton, Canada (IEEE, 2006) pp. 301–306.CrossRefGoogle Scholar
Vatavu, A., Rahm, M., Govindachar, S., Krehl, G., Mantha, A., Bhavsar, S. R., Schier, M. R., Peukert, J. and Maile, M., “From particles to self-localizing tracklets: A multilayer particle filter-based estimation for dynamic grid maps,” IEEE Intel Transp Syst Mag 12(4), 149–168 (2020).CrossRefGoogle Scholar
Jung, J. and Bae, S., “Real-time road lane detection in urban areas using liDAR data,” Electronics 7(11), 276 (2018).CrossRefGoogle Scholar
Brito, B., Floor, B., Ferranti, L. and Alonso-Mora, J., “Model predictive contouring control for collision avoidance in unstructured dynamic environments,” IEEE Robot Autom Lett 4(4), 4459–4466 (2019).CrossRefGoogle Scholar
Jian, Z., Yan, Z., Lei, X., Lu, Z., Lan, B., Wang, X. and Liang, B., “Dynamic Control Barrier Function-based Model Predictive Control to Safety-Critical Obstacle-Avoidance of Mobile Robot,” In: IEEE International Conference on Robotics and Automation (ICRA), London, United Kingdom (IEEE, 2023) pp. 3679–3685.CrossRefGoogle Scholar
He, S., Dai, S.-L., Zhao, Z., Zou, T. and Ma, Y., “UDE-based distributed formation control for MSVs with collision avoidance and connectivity preservation,” IEEE Trans Ind Inform 20(2), 1476–1487 (2024).CrossRefGoogle Scholar
Hogan, F. and Rodriguez, A., “Reactive planar non-prehensile manipulation with hybrid model predictive control,” Int J Robot Res 39(7), 755–773 (2020).CrossRefGoogle Scholar
Ge, S. S. and Cui, Y. J., “Dynamic motion planning for mobile robots using potential field method,” Auton Robot 13(3), 207–222 (2002).CrossRefGoogle Scholar
Lee, D. H., Lee, S. S., Ahn, C. K., Shi, P. and Lim, C.-C., “Finite distribution estimation-based dynamic window approach to reliable obstacle avoidance of mobile robot,” IEEE Trans Ind Electron 68(10), 9998–10006 (2021).CrossRefGoogle Scholar
Scianca, N., De Simone, D., Lanari, L. and Oriolo, G., “MPC for humanoid gait generation: Stability and feasibility,” IEEE Trans Robot 36(4), 1171–1188 (2020).CrossRefGoogle Scholar
Zeng, J., Zhang, B. and Sreenath, K., “Safety-Critical Model Predictive Control with Discrete-Time Control Barrier Function,” In: American Control Conference (ACC), New Orleans, Louisiana (IEEE, 2021) pp. 3882–3889.CrossRefGoogle Scholar
Yu, H., Duan, J., Taheri, S., Cheng, H. and Qi, Z., “A model predictive control approach combined unscented Kalman filter vehicle state estimation in intelligent vehicle trajectory tracking,” Adv Mech Eng 7(5), 1–14 (2015).CrossRefGoogle Scholar
Rosolia, U., De Bruyne, S. and Alleyne, A. G., “Autonomous vehicle control: A nonconvex approach for obstacle avoidance,” IEEE Trans Contr Syst Technol 25(2), 469–484 (2017).CrossRefGoogle Scholar
Zhang, X., Liniger, A. and Borrelli, F., “Optimization-based collision avoidance, ”IEEE Trans Contr Syst Tech 29(3), 972–983 (2021).CrossRefGoogle Scholar
Son, T. D. and Nguyen, Q., “Safety-Critical Control for Non-affine Nonlinear Systems with Application on Autonomous Vehicle,” In: IEEE 58th Conference on Decision and Control (CDC), Nice, France (IEEE, 2019) pp. 7623–7628.CrossRefGoogle Scholar
Rosolia, U. and Ames, A. D., “Multi-rate control design leveraging control barrier functions and model predictive control policies,” IEEE Control Syst Lett 5(3), 1007–1012 (2021).CrossRefGoogle Scholar
Dhanushree, M., Priya, R., Aruna, P. and Bhavani, R., “A Keyframe Extraction Using HDBSCAN With Particle Swarm Optimization,” In: 10th International Conference on Signal Processing and Integrated Networks (SPIN), Noida, India (IEEE, 2023) pp. 2445–450.CrossRefGoogle Scholar
Neto, A. C. A., Sander, J., Campello, R. J. G. B. and Nascimento, M. A., “Efficient computation and visualization of multiple density-based clustering hierarchies,” IEEE Trans Knowl Data Eng 34(8), 3075–3089 (2021).CrossRefGoogle Scholar
Wan, E. A. and Van Der Merwe, R., “The Unscented Kalman Filter for Nonlinear Estimation,” In: Proceedings of the IEEE. 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium, Alberta, Canada (IEEE, 2000) pp. 153–158.Google Scholar
Yu, H., Duan, J., Taheri, S., Cheng, H. and Qi, Z., “A model predictive control approach combined unscented Kalman filter vehicle state estimation in intelligent vehicle trajectory tracking,” Adv Mech Eng 7(5), 655–663 (2015).CrossRefGoogle Scholar
Gao, B., Gao, S., Zhong, Y., Hu, G. and Gu, C., “Interacting multiple model estimation-based adaptive robust unscented Kalman filter,” Int J Control Autom Syst 15(5), 2013–2025 (2017).CrossRefGoogle Scholar
Zeng, J., Li, Z. and Sreenath, K., “Enhancing Feasibility and Safety of Nonlinear Model Predictive Control with Discrete-Time Control Barrier Functions,” In: 60th IEEE Conference on Decision and Control (CDC), Texas, USA (IEEE, 2021) pp. 6137–6144.CrossRefGoogle Scholar
Xu, W. and Zhang, F., “FAST-LIO: A fast, robust LiDAR-inertial odometry package by tightly-coupled iterated Kalman filter,” IEEE Robot Autom Lett 6(2), 3317–3324 (2021).CrossRefGoogle Scholar
Xu, W., Cai, Y., He, D., Lin, J. and Zhang, F., “FAST-LIO2: Fast direct LiDAR-inertial odometry,” IEEE Trans Robot 38(4), 2053–2073 (2022).CrossRefGoogle Scholar
Kim, G. and Kim, A., “Remove, then Revert: Static Point Cloud Map Construction using Multiresolution Range Images,” In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, USA (IEEE, 2020) pp. 10758–10765.CrossRefGoogle Scholar
Zhao, Y., Fritsche, C., Yin, F., Gunnarsson, F. and Gustafsson, F., “Sequential monte carlo methods and theoretical bounds for proximity report based indoor positioning,” IEEE Trans Veh Technol 67(6), 5372–5386 (2018).CrossRefGoogle Scholar
Fankhauser, P., Bloesch, M. and Hutter, M., “Probabilistic terrain mapping for mobile robots with uncertain localization,” IEEE Robot Autom Lett 3(4), 3019–3026 (2018).CrossRefGoogle Scholar
Gammell, J. D., Srinivasa, S. S. and Barfoot, T. D., “Informed RRT*: Optimal Sampling-Based Path Planning Focused via Direct Sampling of an Admissible Ellipsoidal Heuristic,” In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Illinois, USA (IEEE, 2014) pp. 2997–3004.CrossRefGoogle Scholar
Jian, Z., Lu, Z., Zhou, X., Lan, B., Xiao, A., Wang, X. and Liang, B., “PUTN: A Plane-fitting based Uneven Terrain Navigation Framework,” In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Kyoto, Japan (IEEE, 2022) pp. 7160–7166.CrossRefGoogle Scholar