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Robust and Scalable UAV-UGV Planning for Aerial Monitoring under Energy and Precedence Constraints

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arXiv:2610.12670v1 Announce Type: new Abstract: We consider planning for unmanned aerial vehicle (UAV) and unmanned ground vehicle (UGV) teams in aerial monitoring missions with ordering requirements on the monitoring points to be visited by the UAV. The system consists of multiple UAV-UGV teams, each comprising one UAV and one UGV. UAV energy limitations are modeled through endurance, defined as the maximum flight time on a full battery. The UGVs serve as mobile charging stations and transport the UAVs between consecutive aerial tours. Given a set of monitoring points subject to ordering constraints and the teams' initial and terminal positions, the objective is to minimize mission completion time while satisfying UAV energy limits and the required visit order under bounded travel-time d…

SourcearXiv RoboticsAuthor: Cahit Ikbal Er, Yasin Yazicioglu
Robust and Scalable UAV-UGV Planning for Aerial Monitoring under Energy and Precedence Constraints
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[Submitted on 8 Oct 2026]

Title:Robust and Scalable UAV-UGV Planning for Aerial Monitoring under Energy and Precedence Constraints

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Abstract:We consider planning for unmanned aerial vehicle (UAV) and unmanned ground vehicle (UGV) teams in aerial monitoring missions with ordering requirements on the monitoring points to be visited by the UAV. The system consists of multiple UAV-UGV teams, each comprising one UAV and one UGV. UAV energy limitations are modeled through endurance, defined as the maximum flight time on a full battery. The UGVs serve as mobile charging stations and transport the UAVs between consecutive aerial tours. Given a set of monitoring points subject to ordering constraints and the teams' initial and terminal positions, the objective is to minimize mission completion time while satisfying UAV energy limits and the required visit order under bounded travel-time disturbances that may arise during execution. Unlike prior work, the plan jointly determines vehicle routes and their timing, allowing teams to slow down as needed to satisfy the ordering constraints. We develop a polynomial-time algorithm that generates efficient solutions to the underlying NP-hard planning problem. We establish feasibility guarantees, characterize the algorithm's computational complexity, and evaluate its performance via numerical comparisons.

Subjects:

Robotics (cs.RO); Multiagent Systems (cs.MA); Systems and Control (eess.SY)

Cite as: arXiv:2610.12670 [cs.RO]

(or arXiv:2610.12670v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2610.12670

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Cahit Ikbal Er [view email] [v1] Thu, 8 Oct 2026 19:47:55 UTC (111 KB)

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  • AI generation is temporarily unavailable; this entry was preserved with deterministic fallback metadata.
  • arXiv:2610.12670v1 Announce Type: new Abstract: We consider planning for unmanned aerial vehicle (UAV) and unmanned ground vehicle (UGV) teams in aerial monitoring missions with o…

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