Integrated Consensus Framework for Task Assignment and Path Planning of a Degraded UAV Fleet

Waleed T. Barnawi , Gillian M. Nicholls , Charles D. McAllister , Alisha Y. Ortiz

Drones Auton. Veh. ›› 2025, Vol. 2 ›› Issue (4) : 10016

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Drones Auton. Veh. ›› 2025, Vol. 2 ›› Issue (4) :10016 DOI: 10.70322/dav.2025.10016
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Integrated Consensus Framework for Task Assignment and Path Planning of a Degraded UAV Fleet
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Abstract

Unmanned aerial vehicle (UAV) systems can fail during civil and military operations. This presents a significant challenge for human teleoperators (remote pilots) in determining task reallocation after member loss within the fleet. To alleviate the high cognitive load on teleoperators in critical situations, a decentralized strategy was developed to resolve the combined task assignment and vehicle routing problems. This Integrated Consensus Framework (ICF) not only solves the combined problem but also adds a unique ability to identify the loss of a vehicle and dynamically reroute agents to abandoned tasks to achieve a satisfactory solution. ICF is a two-tiered approach that combines a novel algorithm, the Caravan Auction (CarA) algorithm, with a path-planning strategy to identify when UAVs are lost and reallocate orphaned tasks. The CarA Algorithm consists of three phases: auction, consensus, and validation phases. An experiment using Monte Carlo simulations was conducted to determine the performance of ICF. Teleoperators assigned to complete multiple tasks with UAVs in dangerous environments can allow the proposed system to perform task assignments and reallocation while offering only supervisory control as needed. The results indicate this novel approach provides comparable performance to existing strategies, doing so with the addition of randomized UAV loss.

Keywords

Unmanned aircraft systems / Multi-vehicle intelligent systems / Coordinated control

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Waleed T. Barnawi, Gillian M. Nicholls, Charles D. McAllister, Alisha Y. Ortiz. Integrated Consensus Framework for Task Assignment and Path Planning of a Degraded UAV Fleet. Drones Auton. Veh., 2025, 2(4): 10016 DOI:10.70322/dav.2025.10016

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Author Contributions

Conceptualization, W.T.B.; Methodology, W.T.B., G.M.N.; Software, W.T.B.; Validation, W.T.B., G.M.N., C.D.M. and A.Y.O.; Formal Analysis, W.T.B.; Investigation, W.T.B., G.M.N.; Resources, W.T.B.; Data Curation, W.T.B.; Writing—Original Draft Preparation, W.T.B., G.M.N.; Writing—Review & Editing, W.T.B., G.M.N., C.D.M. and A.Y.O.; Visualization, W.T.B.; Supervision, G.M.N.; Project Administration, W.T.B.; Funding Acquisition, W.T.B.

Ethics Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Research data is available by request to the corresponding author.

Funding

This research was funded in part by the United States Department of Defense, Naval Information Warfare Center Atlantic.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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