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Learning Connectivity for Data Distribution in Robot Teams
[article]
2021
arXiv
pre-print
Many algorithms for control of multi-robot teams operate under the assumption that low-latency, global state information necessary to coordinate agent actions can readily be disseminated among the team. However, in harsh environments with no existing communication infrastructure, robots must form ad-hoc networks, forcing the team to operate in a distributed fashion. To overcome this challenge, we propose a task-agnostic, decentralized, low-latency method for data distribution in ad-hoc networks
arXiv:2103.05091v2
fatcat:v5qedkge7jc5pnowrfm6ydvmri