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Surveillance mission scheduling with unmanned aerial vehicles in dynamic heterogeneous environments

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Abstract

In this study, we design, evaluate, and compare multiple heuristic techniques for mission scheduling of distributed systems comprising unmanned aerial vehicles (UAVs) in energy-constrained dynamic environments. These techniques find effective mission schedules in real-time to determine which UAVs and sensors are used to surveil which targets. We develop a surveillance value metric to quantify the effectiveness of mission schedules, incorporating the amount and usefulness of information obtained from surveilling targets. We use the surveillance value metric in simulation studies to evaluate the heuristic techniques with a reality-based randomized model. We consider two comparison heuristics, three value-based heuristics, and a metaheuristic that intelligently switches between the best value-based heuristics. Additionally, preemption and filtering techniques are applied to further improve the metaheuristic. We show that, for all scenarios that we consider, the novel modified metaheuristics find solutions that are the best on average compared to all other techniques that we evaluate.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. However, the process used to randomly generate all input data used in this study is detailed in Sect. 4.

References

  1. Crowder J and Carbone J (2018) “Autonomous Mission Planner and Supervisor (AMPS) for UAVs.” 20th International Conference on Artificial Intelligence (ICAI ’18), 195–201,

  2. Garey MR, Johnson DS (1979) Computers and intractability: a guide to the theory of np-completeness. W. H. Freeman and Co.

    MATH  Google Scholar 

  3. Machovec D, Khemka B, Kumbhare N, Pasricha S, Maciejewski AA, Siegel HJ, Akoglu A, Koenig GA, Hariri S, Tunc C, Wright M, Hilton M, Rambharos R, Blandin C, Fargo F, Louri A, Imam N (2019) Utility-based resource management in an oversubscribed energy-constrained heterogeneous environment executing parallel applications. Parallel Comput 83:48–72

    Article  Google Scholar 

  4. Khemka B, Friese R, Pasricha S, Maciejewski AA, Siegel HJ, Koenig GA, Powers S, Hilton M, Rambharos R, Poole S (2015) Utility maximizing dynamic resource management in an oversubscribed energy-constrained heterogeneous computing system. Sustain Comput: Inf Syst 5:14–30

    Google Scholar 

  5. Wang JJ, Zhang YF, Geng L, Fuh JYH, and Teo SH (2014)“Mission planning for heterogeneous tasks with heterogeneous UAVs.” 13th International Conference on Control Automation Robotics & Vision (ICARCV), 1484–1489, Mar. 2014.

  6. Schumacher C, Chandler P, Pachter M, and Pachter L (2003) “UAV Task assignment with timing constraints.” AIAA Guidance, Navigation, and Control Conference and Exhibit, 9 pp.

  7. Zeng J, Yang X, Yang L, Shen G (2010) Modeling for UAV resource scheduling under mission synchronization. J Syst Eng Electron 21(5):821–826

    Article  Google Scholar 

  8. Leary S, Deittert M, and Bookless J (2011) “Constrained UAV mission planning: a comparison of approaches.” 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), pp. 2002–2009,

  9. Faied M, Mostafa A, and Girard A (2010) “Vehicle routing problem instances: application to multi-UAV mission planning.” AIAA Guidance, Navigation, and Control Conference, 12 pp.,

  10. Tipantuña C, Hesselbach X, Sánchez-Aguero V, Valera F, Vidal I, Nogales B (2019) An NFV-based energy scheduling algorithm for a 5G enabled fleet of programmable unmanned aerial vehicles. Wireless Commun Mobile Comput 15:20

    Google Scholar 

  11. Evers L, Dollevoet T, Barros AI, Monsuur H (2012) Robust UAV Mission planning. Ann Oper Res 222:293–315

    Article  MathSciNet  MATH  Google Scholar 

  12. Mufalli F, Batta R, Nagi R (2012) Simultaneous sensor selection and routing of unmanned aerial vehicles for complex mission plans. Comput Oper Res 39:2787–2799

    Article  MATH  Google Scholar 

  13. Chung W, Crespi V, Cybenko G, and Jordan A (2005) “Distributed sensing and UAV scheduling for surveillance and tracking of unidentifiable targets.” Proceedings of SPIE 5778, Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense IV, pp. 226–235

  14. Pascarella D, Venticinque S, and Aversa R (2013) “Agent-based design for UAV mission planning.” 8th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC), pp. 76–83

  15. Kim J, Song BD, Morrison JR (2013) On the scheduling of systems of UAVs and fuel service stations for long-term mission fulfillment. J Intell Rob Syst 70:347–359

    Article  Google Scholar 

  16. Atencia CR, Ser JD, Camacho D (2019) Weighted strategies to guide a multi-objective evolutionary algorithm for multi-UAV mission planning. Swarm Evol Comput 44:480–495

    Article  Google Scholar 

  17. Zhen Z, Chen Y, Wen L, Han B (2020) An intelligent cooperative mission planning scheme of UAV swarm in uncertain dynamic environment. Aerospace Sci Technol 100:16

    Article  Google Scholar 

  18. Li GQ, Zhou XG, Yin J, Xiao QY (2014) An UAV scheduling and planning method for post-disaster survey. ISPRS – Int Arch Photogrammetry, Remote Sens Spatial Inf Sci 10:169–172

    Google Scholar 

  19. Yang W, Lei L, and Deng J (2014) “Optimization and improvement for multi-UAV cooperative reconnaissance mission planning problem.” 11th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), pp. 10–15,

  20. Thibbotuwawa A, Bocewicz G, Radzki G, Nielsen P, Banaszak Z (2020) UAV mission planning resistant to weather uncertainty. Sensors 20:24

    Article  Google Scholar 

  21. Zhen Z, Chen Y, Wen L, Han B (2020) An Intelligent cooperative mission planning scheme of UAV swarm in uncertain dynamic environment. Aerospace Sci Technol 100:16

    Article  Google Scholar 

  22. Choi M, Contreras P, Shon PVST, Choi HH (2016) Exploring an area by groups of UAVs in the presence of a refueling base. J Supercomput 72:3409–3427

    Article  Google Scholar 

  23. Chen J, Qing X, Ye F, Xiao K, You K, Sun Q (2022) Consensus-based bundle algorithm with local replanning for heterogeneous Multi-UAV system in the time-sensitive and dynamic environment. J Supercomput 78:1712–1740

    Article  Google Scholar 

  24. Friese RD, Crowder JA, Siegel HJ, and Carbone JN (2018) “Bi-objective study for the assignment of unmanned aerial vehicles to targets.” 20th International Conference on Artificial Intelligence (ICAI ’18), pp. 207–213

  25. Friese RD, Crowder JA, Siegel HJ, and Carbone JN (2019), “Surveillance mission planning: model, performance measure, bi-objective analysis, partial surveils.” 21st International Conference on Artificial Intelligence (ICAI ’19), 7 pp.

  26. Machovec D, Crowder JA, Siegel HJ, Pasricha S, and Maciejewski AA (2020) “Dynamic heuristics for surveillance mission scheduling with unmanned aerial vehicles in heterogeneous environments.” 22nd International Conference on Artificial Intelligence (ICAI ‘20), 21 pp.

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Acknowledgements

Preliminary portions of this material appeared in conference papers [24–26]. The differences between this work and those preliminary versions include: (a) this work models surveillance intervals for each target instead of allowing targets to be surveilled at any time, (b) the characteristics of the UAVs and targets are not static in this work and can dynamically change during the day, (c) we design, analyze, and evaluate two new modifications to our heuristics called preemption and filtering, (d) we improve our simulation model by using more realistic distributions for the characteristics of the randomized scenarios, and (e) we simulated the heuristics in both small-scale and large-scale scenarios to demonstrate their scalability for use in real-time environments. The authors thank Patrick J. Burns and John N. Carbone for their comments on this research.

Funding

No funding was received for conducting this study.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO, USA

    Dylan Machovec, Howard Jay Siegel, Sudeep Pasricha & Anthony A. Maciejewski

  2. Department of Computer Science, Colorado State University, Fort Collins, CO, USA

    Howard Jay Siegel & Sudeep Pasricha

  3. CAES Advanced Technology and Engineering (AT&E), Colorado Springs, CO, USA

    James A. Crowder

  4. Pacific Northwest National Laboratory, , Richland, WA, USA

    Ryan D. Friese

Authors
  1. Dylan Machovec
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  2. Howard Jay Siegel
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  3. James A. Crowder
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  4. Sudeep Pasricha
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  5. Anthony A. Maciejewski
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  6. Ryan D. Friese
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Contributions

All authors contributed to the conception and design of the study. DM ran the simulations for the study and prepared the results. All authors met and discussed the simulations and results throughout the process. DM wrote the first draft of the manuscript and all authors reviewed and commented on the draft. All authors read, reviewed, and approved the final manuscript.

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Correspondence to Dylan Machovec.

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Machovec, D., Siegel, H.J., Crowder, J.A. et al. Surveillance mission scheduling with unmanned aerial vehicles in dynamic heterogeneous environments. J Supercomput 79, 13864–13888 (2023). https://doi.org/10.1007/s11227-023-05225-z

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