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Online stochastic UAV mission planning with time windows and time-sensitive targets

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  • Evers, Lanah
  • Barros, Ana Isabel
  • Monsuur, Herman
  • Wagelmans, Albert

Abstract

In this paper we simultaneously consider three extensions to the standard Orienteering Problem (OP) to model characteristics that are of practical relevance in planning reconnaissance missions of Unmanned Aerial Vehicles (UAVs). First, travel and recording times are uncertain. Secondly, the information about each target can only be obtained within a predefined time window. Due to the travel and recording time uncertainty, it is also uncertain whether a target can be reached before the end of its time window. Finally, we consider the appearance of new targets during the flight, so-called time-sensitive targets, which need to be visited immediately if possible. We tackle this online stochastic UAV mission planning problem with time windows and time-sensitive targets using a re-planning approach. To this end, we introduce the Maximum Coverage Stochastic Orienteering Problem with Time Windows (MCS-OPTW). It aims at constructing a tour with maximum expected profit of targets that were already known before the flight. Secondly, it directs the planned tour to predefined areas where time-sensitive targets are expected to appear. We have developed a fast heuristic that can be used to re-plan the tour, each time before leaving a target. In our computational experiments we illustrate the benefits of the MCS-OPTW planning approach with respect to balancing the two objectives: the expected profits of foreseen targets, and expected percentage of time-sensitive targets reached on time. We compare it to a deterministic planning approach and show how it deals with uncertainty in travel and recording times and the appearance of time-sensitive targets.

Suggested Citation

  • Evers, Lanah & Barros, Ana Isabel & Monsuur, Herman & Wagelmans, Albert, 2014. "Online stochastic UAV mission planning with time windows and time-sensitive targets," European Journal of Operational Research, Elsevier, vol. 238(1), pages 348-362.
    • Handle: RePEc:eee:ejores:v:238:y:2014:i:1:p:348-362
    DOI: 10.1016/j.ejor.2014.03.014
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    References listed on IDEAS

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    Cited by:

    1. Verbeeck, C. & Vansteenwegen, P. & Aghezzaf, E.-H., 2016. "Solving the stochastic time-dependent orienteering problem with time windows," European Journal of Operational Research, Elsevier, vol. 255(3), pages 699-718.
    2. Bijun Wang & Zheyong Bian & Mo Mansouri, 2023. "Self-adaptive heuristic algorithms for the dynamic and stochastic orienteering problem in autonomous transportation system," Journal of Heuristics, Springer, vol. 29(1), pages 77-137, February.
    3. He Luo & Zhengzheng Liang & Moning Zhu & Xiaoxuan Hu & Guoqiang Wang, 2018. "Integrated optimization of unmanned aerial vehicle task allocation and path planning under steady wind," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-24, March.
    4. Zajac, Sandra & Huber, Sandra, 2021. "Objectives and methods in multi-objective routing problems: a survey and classification scheme," European Journal of Operational Research, Elsevier, vol. 290(1), pages 1-25.
    5. Kyriakakis, Nikolaos A. & Marinaki, Magdalene & Matsatsinis, Nikolaos & Marinakis, Yannis, 2022. "A cumulative unmanned aerial vehicle routing problem approach for humanitarian coverage path planning," European Journal of Operational Research, Elsevier, vol. 300(3), pages 992-1004.
    6. Caraballo, Luis E. & Díaz-Báñez, José M. & Fabila-Monroy, Ruy & Hidalgo-Toscano, Carlos, 2022. "Stochastic strategies for patrolling a terrain with a synchronized multi-robot system," European Journal of Operational Research, Elsevier, vol. 301(3), pages 1099-1116.
    7. Peng, Rui, 2018. "Joint routing and aborting optimization of cooperative unmanned aerial vehicles," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 131-137.
    8. Bian, Zheyong & Liu, Xiang, 2018. "A real-time adjustment strategy for the operational level stochastic orienteering problem: A simulation-aided optimization approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 115(C), pages 246-266.

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