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An iterative approach for makespan-minimized multi-agent path planning in discrete space

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Abstract

Makespan-minimized multi-agent path planning (MAPP) seeks to minimize the time taken by the slowest of n agents to reach its destination and this is essentially a minimax-constrained optimization problem. In this work, an iterative max-min improvement (IMMI) algorithm is proposed to approximate the optimal solution of the makespan-minimized MAPP problem. At each iteration, a linear maximization problem is solved using a simplex method followed by a computationally hard MAPP minimization problem that is solved using a local search approach. To keep the local search from being trapped in an unfeasible solution, a Guided Local Search technique is proposed. Comparative results with other MAPP algorithms suggest that the proposed IMMI algorithm strikes a good tradeoff between the ability to find feasible solutions that can be traversed quickly and the computational time incurred in determining these paths.

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Acknowledgments

This research is funded by the Singapore National Research Foundation Interactive Digital Media for Education Grant NRF2008-IDM001-017.

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  1. Nanyang Technological University, Singapore, Singapore

    Wenjie Wang & Wooi Boon Goh

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  1. Wenjie Wang
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  2. Wooi Boon Goh
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Correspondence to Wenjie Wang.

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Wang, W., Goh, W.B. An iterative approach for makespan-minimized multi-agent path planning in discrete space. Auton Agent Multi-Agent Syst 29, 335–363 (2015). https://doi.org/10.1007/s10458-014-9259-z

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