Abstract
A profitable strand of literature has lately capitalized on the exploitation of the collaborative capabilities of robotic swarms for efficiently undertaking diverse tasks without any human intervention, ranging from the blind exploration of devastated areas after massive disasters to mechanical repairs of industrial machinery in hostile environments, among others. However, most contributions reported to date deal only with robotic missions driven by a single task-related metric to be optimized by the robotic swarm, even though other objectives such as energy consumption may conflict with the imposed goal. In this paper four multi-objective heuristic solvers, namely NSGA-II, NSGA-III, MOEA/D and SMPSO, are used to command and route a set of robots towards efficiently reconstructing a scene using simple camera sensors and stereo vision in two phases: explore the area and then achieve validated map points. The need for resorting to multi-objective heuristics stems, from the consideration of energy efficiency as a second target of the mission plan. In this regard, by incorporating energy trophallaxis within the swarm, overall autonomy is increased. An environment is arranged in V-REP to shed light on the performance over a realistically emulated physical environment. SMPSO shows better exploration capabilities during the first phase of the mission. However, in the second phase the performance of SMPSO degrades in contrast to NSGA-II and NSGA-III. Moreover, the entire robotic swarm is able to return to the original departure position in all the simulations. The obtained results stimulate further research lines aimed at considering decentralized heuristics for the considered problem.
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Notes
- 1.
Future research paths in this regard will be sketched in Sect. 7.
References
Beni, G.: From swarm intelligence to swarm robotics. In: Şahin, E., Spears, W.M. (eds.) SR 2004. LNCS, vol. 3342, pp. 1–9. Springer, Heidelberg (2005). https://doi.org/10.1007/978-3-540-30552-1_1
Tan, Y., Zheng, Z.: Research advance in swarm robotics. Defence Technol. 9(1), 18–39 (2013)
Ben-Ari, M., Mondada, F.: Swarm robotics. In: Elements of Robotics, pp. 251–265. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-62533-1_15
Wong, C., Yang, E., Yan, X.T., Gu, D.: Autonomous robots for harsh environments: a holistic overview of current solutions and ongoing challenges. Syst. Sci. Control Eng. 6(1), 213–219 (2018)
Wong, C., Yang, E., Yan, X.T., Gu, D.: An overview of robotics and autonomous systems for harsh environments. In: International Conference on Automation and Computing, pp. 1–6 (2017)
Barca, J.C., Sekercioglu, Y.A.: Swarm robotics reviewed. Robotica 31(3), 345–359 (2013)
Korst, P., Velthuis, H.: The nature of trophallaxis in honeybees. Insectes Soc. 29(2), 209–221 (1982)
Hamilton, C., Lejeune, B.T., Rosengaus, R.B.: Trophallaxis and prophylaxis: social immunity in the carpenter ant camponotus pennsylvanicus. Biol. Lett. 7(1), 89–92 (2011)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
Nebro, A.J., Durillo, J.J., García-Nieto, J., Coello Coello, C., Luna, F., Alba, E.: SMPSO: a new PSO-based metaheuristic for multi-objective optimization. In: IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making, pp. 66–73 (2009)
Zhang, Q., Li, H.: MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 8(11), 712–731 (2008)
Jain, H., Deb, K.: An evolutionary many-objective optimization algorithm using reference-point based nondominated sorting approach, Part II: handling constraints and extending to an adaptive approach. IEEE Trans. Evol. Comput. 18(4), 602–622 (2014)
Haek, M., Ismail, A.R., Basalib, A.O.A., Makarim, N.: Exploring energy charging problem in swarm robotic systems using foraging simulation. Jurnal Teknologi 76(1), 239–244 (2015)
Schmickl, T., Crailsheim, K.: Trophallaxis among swarm-robots: a biologically inspired strategy for swarm robotics. In: IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, pp. 377–382 (2006)
Schmickl, T., Crailsheim, K.: Trophallaxis within a robotic swarm: bio-inspired communication among robots in a swarm. Auton. Robots 25(1–2), 171–188 (2008)
Melhuish, C., Kubo, M.: Collective energy distribution: maintaining the energy balance in distributed autonomous robots using trophallaxis. Distrib. Auton. Robot. Syst. 6, 275–284 (2007)
Schiøler, H., Ngo, T.D.: Trophallaxis in robotic swarms-beyond energy autonomy. In: International Conference on Control, Automation, Robotics and Vision, pp. 1526–1533 (2008)
Carrillo, M., et al.: A bio-inspired approach for collaborative exploration with mobile battery recharging in swarm robotics. In: Korošec, P., Melab, N., Talbi, E.-G. (eds.) BIOMA 2018. LNCS, vol. 10835, pp. 75–87. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91641-5_7
Mostaghim, S., Steup, C., Witt, F.: Energy aware particle swarm optimization as search mechanism for aerial micro-robots. In: IEEE Symposium Series on Computational Intelligence, pp. 1–7 (2016)
Ismail, A.R., Desia, R., Zuhri, M.F.R.: The initial investigation of the design and energy sharing algorithm using two-ways communication mechanism for swarm robotic systems. In: Phon-Amnuaisuk, S., Au, T.W. (eds.) Computational Intelligence in Information Systems. AISC, vol. 331, pp. 61–71. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-13153-5_7
Bonin-Font, F., Ortiz, A., Oliver, G.: Visual navigation for mobile robots: a survey. J. Intell. Rob. Syst. 53(3), 263 (2008)
Hong, S., Li, M., Liao, M., van Beek, P.: Real-time mobile robot navigation based on stereo vision and low-cost GPS. Electron. Imaging 2017, 10–15 (2017)
Sugihara, K.: Three principles in stereo vision. Adv. Robot. 1(4), 391–400 (1986)
Pollefeys, M., Koch, R., Gool, L.V.: Self-calibration and metric reconstruction inspite of varying and unknown intrinsic camera parameters. Int. J. Comput. Vis. 32(1), 7–25 (1999)
Mattoccia, S., De-Maeztu, L.: A fast segmentation-driven algorithm for accurate stereo correspondence. In: International Conference on 3D Imaging, pp. 1–6 (2011)
Chrysostomou, D., Gasteratos, A., Nalpantidis, L., Sirakoulis, G.C.: Multi-view 3D scene reconstruction using ant colony optimization techniques. Meas. Sci. Technol. 23(11), 114002 (2012)
Rohmer, E., Singh, S.P., Freese, M.: V-REP: a versatile and scalable robot simulation framework. In: International Conference on Intelligent Robots and Systems (IROS), pp. 1321–1326. IEEE (2013)
De Meyer, K., Slawomir, N.J., Mark, B.: Stochastic diffusion search: partial function evaluation in swarm intelligence dynamic optimisation. In: Swarm Intelligence Dynamic Optimisation, pp. 185–207. Springer, Heidelberg (2006)
Zhu, D., Tian, C., Sun, B., Luo, C.: Complete coverage path planning of autonomous underwater vehicle based on GBNN algorithm. J. Intell. Robot. Syst. 1–13 (2018). https://link.springer.com/article/10.1007/s10846-018-0787-7
Horvátha, E., Pozna, C., Precup, R.E.: Robot coverage path planning based on iterative structured orientation. Acta Polytechnica Hungarica 15(2), 231–249 (2018)
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This work was supported by the Basque Government through the EMAITEK program.
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Carrillo, M., Sánchez-Cubillo, J., Osaba, E., Bilbao, M.N., Del Ser, J. (2019). Trophallaxis, Low-Power Vision Sensors and Multi-objective Heuristics for 3D Scene Reconstruction Using Swarm Robotics. In: Kaufmann, P., Castillo, P. (eds) Applications of Evolutionary Computation. EvoApplications 2019. Lecture Notes in Computer Science(), vol 11454. Springer, Cham. https://doi.org/10.1007/978-3-030-16692-2_40
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