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A constraint-based approach for planning unmanned aerial vehicle activities

Published online by Cambridge University Press:  22 February 2017

Christophe Guettier
Affiliation:
SAFRAN Electronics and Defense, 100 Avenue de Paris, 91344 Massy, France e-mail: [email protected]
François Lucas
Affiliation:
EPEX SPOT, 5 Boulevard Montmartre, 75002 Paris, France e-mail: [email protected]

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.

Type
Articles
Copyright
© Cambridge University Press, 2017 

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References

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.CrossRefGoogle Scholar
Botea, A., Mller, M. & Schaeffer, J. 2004. Near optimal hierarchical path-finding. Journal of Game Development 1(1), 728.Google Scholar
Cerny, V. 1985. A thermodynamical approach to the travelling salesman problem: an efficient simulation algorithm. Journal of Optimization Theory and Applications 45, 4151.CrossRefGoogle 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), 5366.CrossRefGoogle 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, 61124.CrossRefGoogle 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, 267297.CrossRefGoogle 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), 100107.CrossRefGoogle 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), 139164.CrossRefGoogle 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.CrossRefGoogle Scholar
Lucas, F. & Guettier, C. 2012. Hybrid solving technique for vehicle planning. In Proceedings of Military Communication Conference (MILCOM).CrossRefGoogle 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, 7585.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), 359388.CrossRefGoogle Scholar