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Volume 9 Issue 11
Nov.  2022

IEEE/CAA Journal of Automatica Sinica

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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
Citation: 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

Receding-Horizon Trajectory Planning for Under-Actuated Autonomous Vehicles Based on Collaborative Neurodynamic Optimization

doi: 10.1109/JAS.2022.105524
Funds:  This work was supported in part by the Research Grants Council of the Hong Kong Special Administrative Region of China (11202318, 11203721), and the Australian Research Council (DP200100700)
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  • 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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    Highlights

    • The receding-horizon trajectory planning of under-actuated vehicles is formulated as a sequential optimization problem with kinetic, kinematic, collision-avoidance constraints
    • Conditions are derived for ensuring the feasibility of the sequential optimization problem
    • Conditions are derived for the global convergence of the neurodynamics-driven trajectory planning method

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