Abstract
Collision avoidance is one of the most primary problems in the decentralized multiagent navigation: while the agents are moving towards their own targets, attentions should be paid to avoid the collisions with the others. In this paper, we introduced the concept of the local action cell, which provides for each agent a set of velocities that are safe to perform. Consequently, as long as the local action cells are updated on time and each agent selects its motion within the corresponding cell, there should be no collision caused. Furthermore, we coupled the local action cell with an adaptive learning framework, in which the performance of selected motions are evaluated and used as the references for making decisions in the following updates. The efficiency of the proposed approaches were demonstrated through the experiments for three commonly considered scenarios, where the comparisons have been made with several well studied strategies.
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In general, for the parameters that should be specified in the experiments, we tested with several values, including the one recommended in the paper that proposed the considered algorithm, and selected the best choice.
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Ning, L., Zhang, Y. (2020). LAC-Nav: Collision-Free Multiagent Navigation Based on the Local Action Cells. In: Taylor, M.E., Yu, Y., Elkind, E., Gao, Y. (eds) Distributed Artificial Intelligence. DAI 2020. Lecture Notes in Computer Science(), vol 12547. Springer, Cham. https://doi.org/10.1007/978-3-030-64096-5_2
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DOI: https://doi.org/10.1007/978-3-030-64096-5_2
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