The DOI matches its Crossref record.
Multirobot Rendezvous Planning for Recharging in Persistent Tasks
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Otto, A., Agatz, N., Campbell, J., Golden, B., & Pesch, E. (2018) · Networks · doi:10.1002/net.21818 · Cited by 985
“[165, 166] consider drones supported by a set of mobile recharging robots.”
BackgroundChung, S. H., Sah, B., & Lee, J. (2020) · Computers & Operations Research · doi:10.1016/j.cor.2020.105004 · Cited by 441
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Multirobot Rendezvous Planning for Recharging in Persistent Tasks. https://app.cochat.ai/papers/10.1109/tro.2014.2380593
- The paper
- 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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.