Applied Intelligence

, Volume 45, Issue 2, pp 305–321 | Cite as

Multi-objective approach for robot motion planning in search tasks

  • Kossar Jeddisaravi
  • Reza Javanmard Alitappeh
  • Luciano C. A. Pimenta
  • Frederico G. Guimarães


This work addresses the problem of single robot coverage and exploration in an environment with the goal of finding a specific object previously known to the robot. As limited time is a constraint of interest we cannot search from an infinite number of points. Thus, we propose a multi-objective approach for such search tasks in which we first search for a good set of positions to place the robot sensors in order to acquire information from the environment and to locate the desired object. Given the interesting properties of the Generalized Voronoi Diagram, we restrict the candidate search points along this roadmap. We redefine the problem of finding these search points as a multi-objective optimization one. NSGA-II is used as the search engine and ELECTRE I is applied as a decision making tool to decide among the trade-off alternatives. We also solve a Chinese Postman Problem to optimize the path followed by the robot in order to visit the computed search points. Simulation results show a comparison between the solution found by our method and solutions defined by other known approaches. Finally, a real robot experiment indicates the applicability of our method in practical scenarios.


Mobile robot exploration Area coverage Multi-objective optimization 



We gratefully acknowledge the financial support provided by CNPq and FAPEMIG, Brazil.


  1. 1.
    Davoodi M, Panahi F, Mohades A, Hashemi SN (2013) Multi-objective path planning in discrete space. J Appl Soft Comput 13(1):709–720CrossRefGoogle Scholar
  2. 2.
    Davoodi M, Panahi F, Mohades A, Hashemi SN (2015) Clear and smooth path planning. J Appl Soft Comput 32:568–579CrossRefGoogle Scholar
  3. 3.
    Wanga X, Shia Y, Dingb D, Gua X (2015) Double global optimum genetic algorithm particle swarm optimization based welding robot path planning. Journal of Engineering Optimization, pp 1–18Google Scholar
  4. 4.
    Zhang Y, Gong DW, Zhang JH (2012) Robot path planning in uncertain environment using multi-objective particle swarm optimization. J Neurocomput 103:172–185CrossRefGoogle Scholar
  5. 5.
    Ortiza JAH, Rodríguez-Vázqueza K, Castañedab MAP, Cosíoa FA (2013) Autonomous robot navigation based on the evolutionary multi-objective optimization of potential fields. J Eng Optim 45(1):19–43MathSciNetCrossRefGoogle Scholar
  6. 6.
    Ioannidisa K, Sirakoulisa GC, Andreadis I (2011) A path planning method based on cellular automata for cooperative robots. Int J Appl Artif Intell 25(8):721–745CrossRefGoogle Scholar
  7. 7.
    Montiel O, Orozco-Rosas U, Sepúlveda R (2015) Path planning for mobile robots using Bacterial Potential Field for avoiding static and dynamic obstacles. J Expert Syst Appl 42(12): 5177–5191CrossRefGoogle Scholar
  8. 8.
    Kala R (2012) Multi-robot path planning using co-evolutionary genetic programming. J Expert Syst Appl 39 (3): 3817–3831CrossRefGoogle Scholar
  9. 9.
    Dasgupta B, Hespanha JP, Sontag E (2004) Aggregation-based approaches to honey-pot searching with local sensory information. In: Proceeding of American Control Conference (ACC), pp 1202–1207Google Scholar
  10. 10.
    Grady DK, Moll M, Hegde C, Sankaranarayanan AC, Baraniuk RG, Kavraki LE (2012) Multi-objective sensor-based replanning for a car-like robot. In: Proceeding of IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), pp 1–6Google Scholar
  11. 11.
    Amanatiadis AA, Chatzichristofis SA, Charalampous K, Doitsidis L, Kosmatopoulos EB, Tsalides P, Gasteratos A, Roumeliotis SI (2013) A multi-objective exploration strategy for mobile robots under operational constraints. IEEE Access 1:691–702CrossRefGoogle Scholar
  12. 12.
    Serpen G, Dou C (2015) Automated robotic parking systems: real-time, concurrent and multi-robot path planning in dynamic environments. Springer J Appl Intell 42:231–251CrossRefGoogle Scholar
  13. 13.
    Amigoni F (2008) Experimental evaluation of some exploration strategies for mobile robots. In: Proceeding of the IEEE International Conference on Robotics and Automation (ICRA) , pp 2818–2823Google Scholar
  14. 14.
    Almansa-Valverde S, Castillo JC, Fernández-Caballero A (2012) Mobile robot map building from time-of-flight camera. J Expert Syst Appl 39(10):8835–8843CrossRefGoogle Scholar
  15. 15.
    Latombe JC, Gonzales-Ba-nos HH (2002) Navigation strategies for exploring indoor environments. Int J Robot Res 21: 829–848CrossRefGoogle Scholar
  16. 16.
    Amigoni F, Gallo A (2005) A multi-objective exploration strategy for mobile robots. In: Proceeding of IEEE International Conference on Robotics and Automation (ICRA), pp 3850–3855Google Scholar
  17. 17.
    Kang JG, Kim S, An SY, Oh SY (2012) A new approach to simultaneous localization and map building with implicit model learning using neuro evolutionary optimization. Springer J Appl Intell 36(1):242–269CrossRefGoogle Scholar
  18. 18.
    Yamauchi B (1997) A frontier-based approach for autonomous exploration. In: Proceeding of Intelligence in Robotics and Automation, pp 146–151Google Scholar
  19. 19.
    Dornhege C, Kleiner A (2013) A frontier-void-based approach for autonomous exploration in 3D. J Adv Robot 27(6):459–468CrossRefGoogle Scholar
  20. 20.
    Juliá M, Reinoso O, Gil A, Ballesta M, Payá L (2010) A hybrid solution to the multi-robot integrated exploration problem. J Eng Appl Artif Intell 23:473–486CrossRefGoogle Scholar
  21. 21.
    Maohai L, Han W, Lining S, Zesu C (2013) Robust omnidirectional mobile robot topological navigation system using omnidirectional vision. J Eng Appl Artif Intell 26(8):1942–1952CrossRefGoogle Scholar
  22. 22.
    Oriolo G, Vendittelli M, Freda L, Troso G (2004) The SRT Method : Randomized strategies for exploration. In: Proceeding of IEEE International Conference on Robotics and Automation (ICRA), pp 4688–4694Google Scholar
  23. 23.
    El-Hussieny H, Assal SFM, Abdellatif M (2013) Improved Backtracking Algorithm for Efficient Sensor-Based Random Tree Exploration. In: Proceeding of Fifth International Conference on Computational Intelligence, Communication Systems and Networks, pp 19–24Google Scholar
  24. 24.
    Freda L, Oriolo G (2005) Frontier-based probabilistic strategies for sensor-based exploration. In: Proceeding of IEEE International Conference on Robotics and Automation (ICRA), pp 3881–3887Google Scholar
  25. 25.
    Franchi A, Oriolo G, Reda L, Vendittelli M (2007) A Decentralized Strategy for Cooperative Robot Exploration. In: Proceeding of First International Conference on Robot Communication and Coordination (ROBOCOMM), pp 1–8Google Scholar
  26. 26.
    Gabriely Y, Rimon E (2002) Spiral-stc: an on-line coverage algorithm of grid environments by a mobile robot. In: Proceeding of EEE International Conference on Robotics and Automation (ICRA), vol 1, pp 954–960Google Scholar
  27. 27.
    Gonzalez E, Alvarez O, Diaz Y, Parra C, Bustacara C (2005) Bsa: a complete coverage algorithm. In: Proceeding of EEE International Conference on Robotics and Automation (ICRA), pp 2040–2044Google Scholar
  28. 28.
    Choi Y-H, Lee T-K, Baek S-H, Oh S-Y (2009) Online Complete Coverage Path Planning for Mobile Robots Based on Linked Spiral Paths Using Constrained Inverse Distance Transform. In: Proceeding of IEEE/RSJ International Conference on Intelligent Robots and Systems, pp 5788–5793Google Scholar
  29. 29.
    Lee T-K., Baek S-H., Choi Y-H., Oh S-Y. (2011) Smooth coverage path planning and control of mobile robots based on high-resolution grid map representation. J Robot Auton Syst 59(10):801–812CrossRefGoogle Scholar
  30. 30.
    Galceran E, Carreras M (2013) A survey on coverage path planning for robotics. J Robot Auton Syst 61 (12):1258–1276CrossRefGoogle Scholar
  31. 31.
    Kohonen T (1982) Self-organized formation of topologically correct feature maps. Biol Cybern 43(1):59–69MathSciNetCrossRefzbMATHGoogle Scholar
  32. 32.
    Fritzke B (1993) Growing cell structures - a self-organizing network for unsupervised and supervised learning, Technical report-University of CaliforniaGoogle Scholar
  33. 33.
    Fritzke B (1995) A growing neural gas network learns topologies. Adv Neural Inf Process Syst 7:625–632Google Scholar
  34. 34.
    Jockusch J, Ritter H (1999) An instantaneous topological mapping model for correlated stimuli. In: Proceeding of International Joint Conference in Neural Networks (IJCNN ), vol 1, pp 529–534Google Scholar
  35. 35.
    Botelho S, Rocha Cd, Oliveira G, Figueiredo M, Drews P (2010) Self Organizing Maps for AUVs Mapping. In: Proceeding of Third Southern Conference on Computational Modeling , pp 115–129Google Scholar
  36. 36.
    Choset H, Lynch KM, Hutchinson S, Kantor G, Burgard W, Kavraki LE, Thrun S (2005) Principles of Robot Motion: Theory, Algorithms, and Implementation. MIT Press, BostonzbMATHGoogle Scholar
  37. 37.
    Branke J, Deb K, Miettinen K, Slowiski R (2008) Multi objective Optimization - Interactive and Evolutionary Approaches, 2008th edn. SpringerGoogle Scholar
  38. 38.
    Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197CrossRefGoogle Scholar
  39. 39.
    Roy B (1968) Classement et choix en présence de points de vue multiples (la méthode ELECTRE). Rev d’Inf Rech Opér (RIRO), fra 8:57–75Google Scholar
  40. 40.
    Eiselt HA, Gendreau M, Laporte G (1995) Arc routing problems, part I: The Chinese postman problem. J Oper Res 43(2):231–242MathSciNetCrossRefzbMATHGoogle Scholar
  41. 41.
    Edmonds J, Johnson EL (1973) Matching, Euler tours and the Chinese postman. J Math Program 5 (1):88–124MathSciNetCrossRefzbMATHGoogle Scholar
  42. 42.
    Pearson D, Bryant V (2004) Decision Math 1: Advancing Maths for AQA. Advancing. HeinemannGoogle Scholar
  43. 43.
    Fleischner H (1991) Eulerian Graphs and Related Topics, Part 1, vol 2. Elsevier, AmsterdamGoogle Scholar
  44. 44.
    Larson RC, Odoni AR (1981) Urban Operations Research. Prentice-HallGoogle Scholar
  45. 45.
    Bullon G, Santiago L, Lorente M, Enrique L, Rojas B, Dolores PP (2011) Path planning for mobile robot navigation using voronoi diagram and fast marching. Int J Robot Autom (IJRA) 2(1):42–64Google Scholar
  46. 46.
    Zhang Z (2012) Microsoft Kinect Sensor and Its Effect, vol 19, pp 4–10Google Scholar
  47. 47.
    Morgan Q, Gerkey B, Conley K, Faust J, Foote T, Leibs J, Berger E, Wheeler R, Ng A (2009) ROS: an open-source Robot Operating System. In: Proceeding of ICRA workshop on open source softwareGoogle Scholar
  48. 48.
    Desai JP, Ostrowski J, Kumar V (1998) Controlling formations of multiple mobile robots. In: Proceeding of EEE International Conference on Robotics and Automation (ICRA), vol 4, pp 2864–2869Google Scholar
  49. 49.
    Bay H, Ess A, Tuytelaars T, Van Gool L (2008) Speeded-up robust features (SURF). J Comput Vis Image Underst 110(3):346–359CrossRefGoogle Scholar
  50. 50.
    Robin RM (2000) Introduction to AI Robotics. MIT Press Cambridge, MA, USAGoogle Scholar
  51. 51.
    Borenstein J, Koren Y (1991) The vector field histogram-fast obstacle avoidance for mobile robots. IEEE Trans Robot Autom 7(3):278–288CrossRefGoogle Scholar
  52. 52.
    Ge SS, Cui YJ (2002) Dynamic motion planning for mobile robots using potential field method. Auton Robot 13(3):207–222CrossRefzbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Kossar Jeddisaravi
    • 1
  • Reza Javanmard Alitappeh
    • 1
  • Luciano C. A. Pimenta
    • 1
  • Frederico G. Guimarães
    • 1
  1. 1.Universidade Federal de Minas Gerais (UFMG)Belo HorizonteBrazil

Personalised recommendations