Abstract
The collision-free path planning problem for multi-agent systems becomes increasingly complex as the number of agents increases. Mean-field game theory has proven effective for solving large-scale problems but is limited by the difficulty of solving higher-dimensional equations, which must fix beginnings and terminations. This paper proposes a new algorithm combining upper and lower layers to achieve online path planning. We utilize the mean field games process as the primary action of the lower layer, while the path planning of the grid method determines the global Markov process of the upper layer. By integrating the mean field games as an act of the global Markov process, we transfer the system’s state by considering the environment. Simulation experiments verify the feasibility of this approach, providing a new and effective means for online path planning of large-scale multi-agent systems.
Supported by National Key R &D Program of China (2022ZD0116401).
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Liu, J., Yao, W., Zhang, X. (2023). An MFG Online Path Planning Algorithm Based on Upper and Lower Structure. In: Yang, H., et al. Intelligent Robotics and Applications. ICIRA 2023. Lecture Notes in Computer Science(), vol 14273. Springer, Singapore. https://doi.org/10.1007/978-981-99-6498-7_31
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DOI: https://doi.org/10.1007/978-981-99-6498-7_31
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