Optimal Marching of Autonomous Networked Robots

Ban, Buri; Jin, Miao; Wu, Hongyi

doi:10.1109/icdcs.2016.51

Cited by 2 publications

(2 citation statements)

References 23 publications

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“…[8,9] did not consider obstacles in the workspace. The authors of [10] considered multiple agents that are instructed to cover a field of interest (FoI). After agents complete a task at one FoI, they move to a new FoI, which may be far away from the current FoI.…”

Section: Literature Reviewmentioning

confidence: 99%

“…After agents complete a task at one FoI, they move to a new FoI, which may be far away from the current FoI. According to [10], agents can automatically adjust their deployment density in the new FoI based on the requirements of various tasks or regions. However, coverage strategies for an 3D cluttered unknown workspace were not considered in these papers.…”

Section: Literature Reviewmentioning

confidence: 99%

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Three-Dimensional Multi-Agent Foraging Strategy Based on Local Interaction

Kim

2023

Sensors

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In this paper, we consider a multi-agent foraging problem in which multiple autonomous agents find resources (called pucks) in a bounded workspace and carry the found resources to a designated location called the base. We consider the case where autonomous agents move in unknown 3D workspace with many obstacles. This article describes 3D multi-agent foraging based on local interaction, which does not rely on global localization of an agent. We propose a 3D foraging strategy with the following two steps. The first step is to detect all pucks inside the 3D cluttered unknown workspace such that every puck in the workspace is detected in a provably complete manner. The next step is to generate a path from the base to every puck, collect every puck, and return them to the base. Because an agent cannot use global localization, each agent depends on local interaction to bring every puck to the base. In this article, every agent on a path to a puck is used in guiding an agent to reach the puck and to bring it to the base. To the best of our knowledge, this article is novel in letting multiple agents perform foraging and puck-carrying in 3D cluttered unknown workspace while not relying on global localization of an agent. In addition, the proposed search strategy is provably complete in detecting all pucks in the 3D cluttered bounded workspace. MATLAB simulations demonstrate that the proposed multi-agent foraging strategy outperforms alternatives in a 3D cluttered workspace.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%