Multi-objective multi-robot deployment in a dynamic environment
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Finding a distribution of a group of robots in an environment is known as Deployment problem, which is one of the challenges in multi-robot systems. In real applications, the environment may change over time and thus deployment must be repeated periodically in order to redistribute the robots. In this paper, we propose a multi-objective optimization method to deploy/redeploy robots in the environment by considering two objectives for the deployment. One objective represents a good estimation of final positions, where the robots will be located, while the second objective is finding the shortest path from the robots initial location to these positions. Thus, our problem is modeled as a multi-objective optimization problem, which is approached with a multi-objective optimization evolutionary algorithm. To deal with the deployment problem, a discrete setup of locational optimization framework and Voronoi partitioning technique are employed. Simulation results on real application testify the performance of our proposed method in comparison with other methods.
KeywordsMulti-robot deployment Multi-objective optimization Voronoi partitioning
This work was supported by Brazilian agencies CAPES, CNPq and FAPEMIG. Reza Javanmard Alitappeh has received research grants from the Brazilian agency CNPq. Kossar Jeddisaravi has received research grants from the Brazilian agency CAPES. Frederico G. Guimarães is a faculty member of the Federal University of Minas Gerais and has received research grants from the Brazilian agencies CNPq and FAPEMIG.
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Conflict of interest
The authors declare that they have no conflict of interest.
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This article does not contain any studies with human or animal subjects.
- Budiharto W, Santoso A, Purwanto D, Jazidie A (2011) a method for path planning strategy and navigation of service robot. Paladyn J Behav Robot 2(2):100–108Google Scholar
- Chaimowicz L, Cowley A, Gomez-Ibanez D, Grocholsky B, Hsieh MA, Hsu H, Keller JF, Kumar V, Swaminathan R, Taylor CJ (2005) Multi-robot systems. From swarms to intelligent automata. In: Proceedings from the 2005 international workshop on multi-robot systems, vol III, Springer, Netherlands, Dordrecht, chap Deploying air-ground multi-robot teams in urban environments, pp 223–234Google Scholar
- Cormen TH, Leiserson CE, Rivest RL, Stein C (2009) Introduction to algorithms, 3rd edn. The MIT Press, London, p 1312Google Scholar
- Figueira J, Greco S, Ehrogott M (2005) Multiple criteria decision analysis: state of the art surveys series. Int Ser Oper Res Manag Sci 78:1048Google Scholar
- Fjallstrom PO (1998) Algorithms for graph partitioning: a survey. Linkop Electron Artic Comput Inf Sci 3(10)Google Scholar
- Hidalgo-Paniagua A, Vega-Rodrguez M, Ferruz J, Pavn N (2015) Solving the multi-objective path planning problem in mobile robotics with a firefly-based approach. Soft Comput 1–16Google Scholar
- Holder A, Lim G, Reese J (2007) The relationship between discrete vector quantization and the p-median problem. Technical reportGoogle Scholar
- Jeddisaravi K, Alitappeh RJ, Pimenta LCA, Guimarães FG (2016) Multi-objective approach for robot motion planning in search tasks. Appl Intell 1–17. doi: 10.1007/s10489-015-0754-y
- King R, Rughooputh H (2003) Elitist multiobjective evolutionary algorithm for environmental/economic dispatch. In: Evolutionary computation, 2003. CEC ’03. The 2003 Congress on, vol 2, IEEE, pp 1108–1114Google Scholar
- Klein PN (2005) Multiple-source shortest paths in planar graphs. In: Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms (SODA ’05), pp 146–155Google Scholar
- Knowles J, Corne D (1999) The pareto archived evolution strategy: A new baseline algorithm for pareto multiobjective optimisation. In: Proceedings of the 1999 congress on evolutionary computation, 1999 CEC 99, vol 1, pp 98–105Google Scholar
- Liu M, Zeng W (2010) Reducing the run-time complexity of NSGA-II for bi-objective optimization problem. In: Proceedings of IEEE international conference on intelligent computing and intelligent systems, IEEE, vol 2, pp 546–549Google Scholar
- Pimenta LCA, Kumar V, Mesquita RC, Pereira GAS (2008) Sensing and coverage for a network of heterogeneous robots. In: Proceedings of IEEE conference on decision and control (CDC), vol 2, pp 3947–3952Google Scholar
- Poduri S, Sukhatme GS (2004) Constrained coverage for mobile sensor networks. In: Proceedings of IEEE international conference on robotics and automation (ICRA), IEEE, pp 165–171Google Scholar
- Roy B (1968) Classement et choix en présence de points de vue multiples (la méthode ELECTRE). La Revue d’Informatique et de Recherche Opérationelle (RIRO) 8:57–75Google Scholar
- Stergiopoulos Y, Tzes A (2011) Coverage-oriented coordination of mobile heterogeneous networks. In: Proceedings of mediterranian control & automation (MED) pp 175–180Google Scholar
- Tzes A, Stergiopoulos Y (2010) Convex Voronoi-inspired space partitioning for heterogeneous networks: a coverage-oriented approach. IET Control Theory Appl 4(12):2802–2812Google Scholar
- Veloso M, Biswas J, Coltin B, Rosenthal S, Brandao S, Mericli T, Ventura R (2012) Symbiotic-autonomous service robots for userrequested tasks in a multi-floor building. In: Proceedings of the cognitive assistive systems workshop, IROS 2012Google Scholar
- Xue Y, Liu H (2010) Optimal path planning for service robot in indoor environment. In: Proceedings of international conference on intelligent computation technology and automation (ICICTA), IEEE, vol 2, pp 850–853Google Scholar
- Zitzler E, Laumanns M, Thiele L (2002) SPEA2: improving the strength Pareto evolutionary algorithm. In: Proceedings of the evolutionary methods for design, optimisation, and control, pp 95–100Google Scholar