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Multirobot Rendezvous Planning for Recharging in Persistent Tasks

Mathew, Neil, Smith, Stephen L., Waslander, Steven L.·2015·IEEE Transactions on Robotics

Abstract

This paper addresses a multirobot scheduling problem in which autonomous unmanned aerial vehicles (UAVs) must be recharged during a long-term mission. The proposal is to introduce a separate team of dedicated charging robots that the UAVs can dock with in order to recharge. The goal is to schedule and plan minimum cost paths for charging robots such that they rendezvous with and replenish the UAVs, as needed, during the mission. The approach is to discretize the 3-D UAV flight trajectories into sets of projected charging points on the ground, thus allowing the problem to be abstracted onto a partitioned graph. Solutions consist of charging robot paths that collectively charge each of the UAVs. The problem is solved by first formulating the rendezvous planning problem to recharge each UAV once using both an integer linear program and a transformation to the Travelling Salesman Problem. The methods are then leveraged to plan recurring rendezvous' over longer horizons using fixed horizon and receding horizon strategies. Simulation results using realistic vehicle and battery models demonstrate the feasibility and robustness of the proposed approach.

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Metadata

Authors
Mathew, N., Smith, S. L., & Waslander, S. L.
Venue
IEEE Transactions on Robotics
Published
2015-02
Volume
31
Issue
1
Pages
128-142
ISSN
  • 1552-3098 (ISSN-L)
  • 1941-0468
OpenAlex
W2011193826
Cited by
173
References
0
FWCI
12.42
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Multirobot Rendezvous Planning for Recharging in Persistent Tasks. https://app.cochat.ai/papers/10.1109/tro.2014.2380593
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Mathew, N., Smith, S. L., & Waslander, S. L. (2015). Multirobot Rendezvous Planning for Recharging in Persistent Tasks. IEEE Transactions on Robotics. https://doi.org/10.1109/tro.2014.2380593

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.