Receding-Horizon Trajectory Planning for Under-Actuated Autonomous Vehicles Based on Collaborative Neurodynamic Optimization |
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| J. S. Wang, J. Wang, and Q.-L. Han, “Receding-horizon trajectory planning for under-actuated autonomous vehicles based on collaborative neurodynamic optimization,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 11, pp. 1909–1923, Nov. 2022. doi: 10.1109/JAS.2022.105524 |
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| Authors: | Jiasen Wang Jun Wang Qing-Long Han |
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| Affiliation: | 1. Future Network Research Center, Purple Mountain Laboratories, Nanjing 211111, China;2. Department of Computer Science, the School of Data Science, City University of Hong Kong, Hong Kong, China;3. School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne VIC 3122, Australia |
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| Abstract: | This paper addresses a major issue in planning the trajectories of under-actuated autonomous vehicles based on neurodynamic optimization. A receding-horizon vehicle trajectory planning task is formulated as a sequential global optimization problem with weighted quadratic navigation functions and obstacle avoidance constraints based on given vehicle goal configurations. The feasibility of the formulated optimization problem is guaranteed under derived conditions. The optimization problem is sequentially solved via collaborative neurodynamic optimization in a neurodynamics-driven trajectory planning method/procedure. Simulation results with under-actuated unmanned wheeled vehicles and autonomous surface vehicles are elaborated to substantiate the efficacy of the neurodynamics-driven trajectory planning method. |
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| Keywords: | Collaborative neurodynamic optimization receding-horizon planning trajectory planning under-actuated vehicles |
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