Algorithm for directing cooperative vehicles of a vehicle routing problem for improving fault-tolerance

Dulai, Tibor; Werner-Stark, Ágnes; Hangos, Katalin M.

doi:10.1007/s11081-017-9353-6

Cited by 2 publications

(1 citation statement)

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“…Despite the advantages of heterogeneous multi-UAV systems, effective deployment hinges on managing inevitable uncertainties during task execution. The uncertainties can be mainly categorized into environmental (e.g., meteorological condition changes [6], sudden ground threats [7]), task-related (e.g., task location ambiguities [8,9], resource needs [10], and time window variations [11]), and UAV-specific (e.g., inaccuracies in dynamic models [3,[12][13][14], payload performance inconsistencies [3,15], and unexpected UAV failures [16,17]). It is imperative for task planning to comprehensively consider the potential consequences of these uncertainties and accordingly adapt the task plans, thereby minimizing their disruptive effects on the overall task execution.…”

Section: Introductionmentioning

confidence: 99%

Impact of Uncertain Flight Time on Heterogeneous UAVs’ Task Planning with Temporal Constraints

Wang,

Jia,

Guo

et al. 2024

Aerospace

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Heterogeneous multi-UAV systems offer distinct advantages through their complementary and coordinated use of their diverse capabilities. However, this complexity poses significant challenges in task planning, particularly in considering temporal constraints among tasks. As task dependencies evolve from simple linear chains to complex networked associations, uncertainties in flight times can have a substantial impact on the overall schedule. To address these challenges, this study introduces a rapid estimation method that recursively calculates task completion times, derives their probability distributions, and assesses the robustness of the plan. Furthermore, a neighborhood search algorithm guided by dynamic time windows is designed to effectively evaluate the consequences of task insertions, precisely to adjust high-risk tasks, and reduce blindness in enumerative neighborhood exploration. Simulation results demonstrate that the proposed approach effectively accounts for inherent randomness in the problem and exhibits strong adaptability to changes in the problem scale, flight time fluctuations, and variations in time window constraints.

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