Abstract
This paper presents a simultaneous task assignment and trajectory planning method for unmanned system swarm by using optimal transport and model predictive control (OT-MPC). Unlike the conventional hierarchical assignment and planning, the proposed approach addresses both the task assignment and trajectory planning subproblems concurrently. To be specific, a unified cost function is designed to solve task assignment and trajectory planning problem. Moreover, the multi-tasks are assigned by using optimal transport, which establishes an optimal mapping between tasks and unmanned system vehicles based on transportation cost. The trajectory planning is achieved by using model predictive control, which generates high-quality navigation trajectories considering obstacle avoidance. Finally, the proposed method is applied to the unmanned surface vehicles swarm. Numerical simulations and experiments were conducted to validate the effectiveness of the proposed method.
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Funding
This work was supported by the National Natural Science Foundation of China (No. 11972373), the Natural Science Foundation of Shandong Province (No. ZR2020ME265, ZR2021ME155), the Innovation Capability Improvement Project for Technology Oriented Small and Medium-sized Enterprises of Shandong Province (No. 2021TSGC1376), the Key Research and Development Program of Shandong (No. 2022ZLGX04).
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All authors contributed to the study conception and design. Method designing, coding, experiment preparation, data collection and analysis were performed by Xiwei Wu. The first draft of the manuscript was written by Xiwei Wu and Bing Xiao, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Wu, X., Xiao, B., Cao, L. et al. Optimal Transport and Model Predictive Control-based Simultaneous Task Assignment and Trajectory Planning for Unmanned System Swarm. J Intell Robot Syst 110, 28 (2024). https://doi.org/10.1007/s10846-024-02060-z
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DOI: https://doi.org/10.1007/s10846-024-02060-z