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RESEARCH ARTICLE   Open Access    

A constraint-based approach for planning unmanned aerial vehicle activities

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  • Abstract: Unmanned Aerial Vehicles (UAV) represent a major advantage in defense, disaster relief and first responder applications. UAV may provide valuable information on the environment if their Command and Control (C2) is shared by different operators. In a C2 networking system, any operator may request and use the UAV to perform a remote sensing operation. These requests have to be scheduled in time and a consistent navigation plan must be defined for the UAV. Moreover, maximizing UAV utilization is a key challenge for user acceptance and operational efficiency. The global planning problem is constrained by the environment, targets to observe, user availability, mission duration and on-board resources. This problem follows previous research works on automatic mission Planning & Scheduling for defense applications. The paper presents a full constraint-based approach to simultaneously satisfy observation requests, and resolve navigation plans.
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  • Aarts E. & Lenstra J. 1997. Local Search in Combinatorial Optimization. Princeton University Press.

    Google Scholar

    Abramson M., Kim P. & Williams B. 2001. Executing reactive, model-based programs through graph-based temporal planning. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI).

    Google Scholar

    Ajili F. & Wallace M. 2004. Hybrid problem solving in ECLiPSe. In Constraint and Integer Programming Toward a Unified Methodology, volume 27 of Operations Research/Computer Science Interfaces Series, Chapter 6. Springer, 2004.

    Google Scholar

    Botea A., Mller M. & Schaeffer J. 2004. Near optimal hierarchical path-finding. Journal of Game Development 1(1), 7–28.

    Google Scholar

    Cerny V. 1985. A thermodynamical approach to the travelling salesman problem: an efficient simulation algorithm. Journal of Optimization Theory and Applications 45, 41–51.

    Google Scholar

    Dorigo M. & Gambardella L. 1997. Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation 1(1), 53–66.

    Google Scholar

    Fox M. & Long D. 2000. Automatic synthesis and use of generic types in planning. In Proceedings of the Artificial Intelligence Planning System, AAAI Press, 196–205.

    Google Scholar

    Fox M. & Long D. 2003. PDDL 2.1: an extension to PDDL for expressing temporal planning domains. Journal of Artificial Intelligence Research 20, 61–124.

    Google Scholar

    Goldberg D. 1989. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley.

    Google Scholar

    Goldman R., Haigh K., Musliner D. & Pelican M. 2002. MACBeth: a multi-agent constraint-based planner. In Proceedings of the 21st Digital Avionics Systems Conference, 2, 7E3:1–8.

    Google Scholar

    Gondran M. & Minoux M. 1995. Graphes et Algorithmes. Editions Eyrolles.

    Google Scholar

    Hansen E. & Zhou R. 2007. Anytime heuristic search. Journal of Artificial Intelligence Research 28, 267–297.

    Google Scholar

    Hart P., Nilsson N. & Raphael B. 1968. A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems, Science and Cybernetics 4(2), 100–107.

    Google Scholar

    Hentenryck P. Van, Saraswat V. A. & Deville Y. 1998. Design, implementation, and evaluation of the constraint language CC(FD). The Journal of Logic Programming 37(1–3), 139–164.

    Google Scholar

    Koenig S., Sun X. & Yeoh W. 2009. Dynamic Fringe-Saving A*. In Proceedings of the 8th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2, 891–898.

    Google Scholar

    Laborie P. & Ghallab M. 1995. Planning with Sharable Resource Constraints. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI).

    Google Scholar

    Lucas F. & Guettier C. 2010. Automatic vehicle navigation with bandwidth constraints. In Proceedings of MILCOM 2010, November.

    Google Scholar

    Lucas F. & Guettier C. 2012. Hybrid solving technique for vehicle planning. In Proceedings of Military Communication Conference (MILCOM).

    Google Scholar

    Lucas F., Guettier C., Siarry P., de La Fortelle A. & Milcent A.-M. 2010. Constrained navigation with mandatory waypoints in uncertain environment. International Journal of Information Sciences and Computer Engineering (IJISCE) 1, 75–85.

    Google Scholar

    Meuleau N., Plaunt C., Smith D. & Smith T. 2009. Emergency landing planning for damaged aircraft. In Proceedings of the 21st Innovative Applications of Artificial Intelligence Conference.

    Google Scholar

    Meuleau N., Neukom C., Plaunt C., Smith D. E. & Smithy T. 2011. The emergency landing planner experiment. In 21st International Conference on Automated Planning and Scheduling.

    Google Scholar

    Muscettola N. 1993. HSTS: integrating planning and scheduling. In Technical Report CMU-RI-TR-93-05, The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA.

    Google Scholar

    Sakkout H. E. & Wallace M. 2000. Probe backtrack search for minimal perturbations in dynamic scheduling. Constraints Journal 5(4), 359–388.

    Google Scholar

  • Cite this article

    Christophe Guettier, François Lucas. 2016. A constraint-based approach for planning unmanned aerial vehicle activities. The Knowledge Engineering Review 31(5)486−497, doi: 10.1017/S0269888916000291
    Christophe Guettier, François Lucas. 2016. A constraint-based approach for planning unmanned aerial vehicle activities. The Knowledge Engineering Review 31(5)486−497, doi: 10.1017/S0269888916000291

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RESEARCH ARTICLE   Open Access    

A constraint-based approach for planning unmanned aerial vehicle activities

The Knowledge Engineering Review  31 2016, 31(5): 486−497  |  Cite this article

Abstract: Abstract: Unmanned Aerial Vehicles (UAV) represent a major advantage in defense, disaster relief and first responder applications. UAV may provide valuable information on the environment if their Command and Control (C2) is shared by different operators. In a C2 networking system, any operator may request and use the UAV to perform a remote sensing operation. These requests have to be scheduled in time and a consistent navigation plan must be defined for the UAV. Moreover, maximizing UAV utilization is a key challenge for user acceptance and operational efficiency. The global planning problem is constrained by the environment, targets to observe, user availability, mission duration and on-board resources. This problem follows previous research works on automatic mission Planning & Scheduling for defense applications. The paper presents a full constraint-based approach to simultaneously satisfy observation requests, and resolve navigation plans.

    • We acknowledge Pr. Arnaud de la Fortelle and Pr. Patrick Siarry for their constant support.

    • © Cambridge University Press, 2017 2017Cambridge University Press
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    Cite this article
    Christophe Guettier, François Lucas. 2016. A constraint-based approach for planning unmanned aerial vehicle activities. The Knowledge Engineering Review 31(5)486−497, doi: 10.1017/S0269888916000291
    Christophe Guettier, François Lucas. 2016. A constraint-based approach for planning unmanned aerial vehicle activities. The Knowledge Engineering Review 31(5)486−497, doi: 10.1017/S0269888916000291
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