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Cooperative Navigation Planning of Multiple Mobile Robots Using Improved Krill Herd

  • D. Chandrasekhar Rao
  • Manas R. Kabat
  • Pradipta K. Das
  • Prabir K. Jena
Research Article - Computer Engineering and Computer Science

Abstract

Robotics has been adequately utilized in different application areas due to its ability to work in a dynamic environment. The aim of finding a collision-free optimal path with minimum energy utilization is of prime concern in the present scenario. The current study presents an improvement of the classical krill herd (KH) in a new conceptual manner to address the multi-robot navigation problem in a clutter environment. The improved KH (IKH) involves tuning of influence parameters of basic KH to generate safe waypoints for each robot from their respective current positions to target by utilizing the herding behavior of krill swarm. The proposed algorithm mainly emphasizes maintaining a good balance between intensification and diversification. The results obtained from IKH have been compared with those acquired by KH and differential evolution (DE) in a similar environment to verify the effectiveness and robustness of the proposed algorithm. The error estimated for total navigation path traveled (TNPT) and average navigation path deviation (ANPD) between simulation and experimental investigation employing IKH is (9.68, 8.92) and reaches (12.42, 11.24) for KH and (15.03, 14.24) for DE. The outcome reveals that the proposed IKH algorithm is superior to KH and DE in terms of generating optimal and safe navigation path in simulation and experiments.

Keywords

Navigation planning Waypoint Aggregate navigation path deviation Average unexplored goal distance Energy utilization Krill herd 

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References

  1. 1.
    Lottermoser, A.; Berger, C.; Braunreuther, S.; Reinhart, G.: Method of usability for mobile robotics in a manufacturing environment. Proc. CIRP 62, 594–599 (2017)CrossRefGoogle Scholar
  2. 2.
    Bakhshipour, M.; Ghadi, M.J.; Namdari, F.: Swarm robotics search & rescue: A novel artificial intelligence-inspired optimization approach. Appl. Soft Comput. 57, 708–726 (2017)CrossRefGoogle Scholar
  3. 3.
    Bayat, B.; Crasta, N.; Crespi, A.; Pascoal, A.M.; Ijspeert, A.: Environmental monitoring using autonomous vehicles: a survey of recent searching techniques. Curr. Opin. Biotechnol. 45, 76–84 (2017)CrossRefGoogle Scholar
  4. 4.
    Stoyanov, T.; Mojtahedzadeh, R.; Andreasson, H.; Lilienthal, A.J.: Comparative evaluation of range sensor accuracy for indoor mobile robotics and automated logistics applications. Robot. Auton. Syst. 61(10), 1094–1105 (2013)CrossRefGoogle Scholar
  5. 5.
    Liu, Y.; Tian, Z.; Liu, Y.; Li, J.; Fu, F.; Bian, J.: Cognitive modeling for robotic assembly/maintenance task in space exploration. In: Advances in Neuroergonomics and Cognitive Engineering, pp. 143–153. Springer, Cham (2018)Google Scholar
  6. 6.
    Sokolov, S.; Zhilenkov, A.; Nyrkov, A.; Chernyi, S.: The Use robotics for underwater research complex objects. In: Computational Intelligence in Data Mining. Springer, Singapore, pp. 421–427 (2017)Google Scholar
  7. 7.
    Ishida, S; Miyamoto, H.: Collision-detecting device for omnidirectional electric wheelchair. ISRN Robotics, 2012.  https://doi.org/10.5402/2013/672826 (2013)
  8. 8.
    Krishna, K.R.: Push Button Agriculture: Robotics, Drones, Satellite-Guided Soil and Crop Management. CRC Press, Boca Raton (2016)CrossRefGoogle Scholar
  9. 9.
    Dudek, G.; Jenkin, M.R.; Milios, E.; Wilkes, D.: A taxonomy for multi-agent robotics. Auton. Robots 3(4), 375–397 (1996)CrossRefGoogle Scholar
  10. 10.
    Gerke, M.; Hoyer, H.: Planning of optimal paths for autonomous agents moving in homogeneous environments. In: Proceedings of the 8th International Conference on Advanced Robotics, pp. 347–357 (1997)Google Scholar
  11. 11.
    Bien, Z.; Lee, J.: A minimum-time trajectory planning method for two robots. IEEE Trans. Robot. Autom. 8(3), 443–450 (1992)CrossRefGoogle Scholar
  12. 12.
    Duleba, I.; Sasiadek, J.Z.: Nonholonomic motion planning based on Newton algorithm with energy optimization. IEEE Trans. Control Syst. Technol. 11(3), 355–363 (2003)CrossRefGoogle Scholar
  13. 13.
    Zhang, H.; Butzke, J.; Likhachev, M.: Combining global and local planning with guarantees on completeness. In: International Conference on Robotics and Automation, pp. 4500–4506 (2012)Google Scholar
  14. 14.
    Muralidharan, A.; Mostofi, Y.: 2017, First passage distance to connectivity for mobile robots. In: American Control Conference (ACC), IEEE, pp. 1517–1523 (2017)Google Scholar
  15. 15.
    Das, P.K.; Behera, H.S.; Jena, P.K.; Panigrahi, B.K.: Multi-robot path planning in a dynamic environment using improved gravitational search algorithm. J. Electr. Syst. Inf. Technol. 3(2), 295–313 (2016)Google Scholar
  16. 16.
    Ayari, A.; Bouamama, S.: A new multiple robot path planning algorithm: dynamic distributed particle swarm optimization. Robot. Biomim. 4(1), 1–15 (2017)CrossRefGoogle Scholar
  17. 17.
    Kavraki, L.E.; Svestka, P.; Latombe, J.C.; Overmars, M.H.: Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Trans. Robot. Autom. 12(4), 566–580 (1996)CrossRefGoogle Scholar
  18. 18.
    Geraerts, R.; Overmars, M. H.: A comparative study of probabilistic roadmap planners. In: Algorithmic Foundations of Robotics V, pp. 43–57 (2004)Google Scholar
  19. 19.
    Lingelbach, F.: Path planning using probabilistic cell decomposition. IEEE Int. Conf. Robot. Autom. 1, 467–472 (2004)Google Scholar
  20. 20.
    Kim, M. H.; Heo, J. H.; Wei, Y.; Lee, M. C.: A path planning algorithm using artificial potential field based on probability map. In: 8th International Conference on Ubiquitous Robots and Ambient Intelligence, pp. 41–43 (2011)Google Scholar
  21. 21.
    Merheb, A.R.; Gazi, V.; Sezer-Uzol, N.: Implementation studies of robot swarm navigation using potential functions and panel methods. IEEE/ASME Trans. Mechatron. 21(5), 2556–2567 (2016)CrossRefGoogle Scholar
  22. 22.
    Bhattacharya, P.; Gavrilova, M.L.: Roadmap-based path planning-Using the Voronoi diagram for a clearance-based shortest path. IEEE Robot. Autom. Mag. 15(2), 58–66 (2008)CrossRefGoogle Scholar
  23. 23.
    Garber, M.; Lin, M. C.: Constraint-based motion planning using Voronoi diagrams. In: Algorithmic Foundations of Robotics V, pp. 541–558 (2004)Google Scholar
  24. 24.
    Jeddisaravi, K.; Alitappeh, R. J.; Guimarães, F. G.: Multi-objective mobile robot path planning based on A* search. In: 6th International Conference on Computer and Knowledge Engineering, pp. 7–12 (2016)Google Scholar
  25. 25.
    Guruji, A.K.; Agarwal, H.; Parsediya, D.K.: Time-efficient A* algorithm for Robot path planning. Proc. Technol. 23, 144–149 (2016)CrossRefGoogle Scholar
  26. 26.
    Sudhakara, P.; Ganapathy, V.: Trajectory planning of a mobile robot using enhanced A-star algorithm. Indian J. Sci. Technol. 9(41), 1–10 (2016)CrossRefGoogle Scholar
  27. 27.
    Duchoň, F.; Babinec, A.; Kajan, M.; Beňo, P.; Florek, M.; Fico, T.; Jurišica, L.: Path planning with modified A star algorithm for a mobile robot. Proc. Eng. 96, 59–69 (2014)CrossRefGoogle Scholar
  28. 28.
    Tsuzuki, M.D.S.G.; de Castro Martins, T.; Takase, F.K.: Robot path planning using simulated annealing. IFAC Proc. 39(3), 175–180 (2006)CrossRefGoogle Scholar
  29. 29.
    Miao, H.; Tian, Y. C.: Robot path planning in dynamic environments using a simulated annealing based approach. In: International Conference on Control, Automation, Robotics and Vision, pp. 1253–1258 (2008)Google Scholar
  30. 30.
    Wang, Z.; Dai, Y.: A new gradient annealing algorithm (GAA) and its applications in path planning of mobile robot. In International Conference on Automation and Logistics, pp. 1968–1973 (2007)Google Scholar
  31. 31.
    Pradhan, S.K.; Parhi, D.R.; Panda, A.K.: Fuzzy logic techniques for navigation of several mobile robots. Appl. Soft Comput. 9(1), 290–304 (2009)CrossRefGoogle Scholar
  32. 32.
    Parhi, D.R.; Mohanta, J.C.: Navigational control of several mobile robotic agents using Petri-potential-fuzzy hybrid controller. Appl. Soft Comput. 11(4), 3546–3557 (2011)CrossRefGoogle Scholar
  33. 33.
    Chang, H.; Jin, T.: Command fusion based fuzzy controller design for moving obstacle avoidance of mobile robot. In: Future Information Communication Technology and Applications, pp. 905–913 (2013)Google Scholar
  34. 34.
    Singh, M.K.; Parhi, D.R.: Path optimisation of a mobile robot using an artificial neural network controller. Int. J. Syst. Sci. 42(1), 107–120 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  35. 35.
    Al-Sagban, M.; Dhaouadi, R.: Neural-based navigation of a differential-drive mobile robot. In: International Conference on Control Automation Robotics & Vision, pp 353–358 (2012)Google Scholar
  36. 36.
    Dezfoulian, S.H.; Wu, D.; Ahmad, I.S.: A generalized neural network approach to mobile robot navigation and obstacle avoidance. Intell. Auton. Syst. 12, 25–42 (2013)CrossRefGoogle Scholar
  37. 37.
    Ni, J.; Yang, S.X.: Bioinspired neural network for real-time cooperative hunting by multirobots in unknown environments. IEEE Trans. Neural Netw. 22(12), 2062–2077 (2011)CrossRefGoogle Scholar
  38. 38.
    Ozkan, M.; Yazici, A.; Kapanoglu, M.; Parlaktuna, O.: A genetic algorithm for task completion time minimization for multi-robot sensor-based coverage. In: Control Applications (CCA) & Intelligent Control (ISIC), pp. 1164–1169 (2009)Google Scholar
  39. 39.
    Elhoseny, M.; Shehab, A.; Yuan, X.: Optimizing robot path in dynamic environments using Genetic Algorithm and Bezier Curve. J. Intell. Fuzzy Syst. 33(4), 2305–2316 (2017).  https://doi.org/10.3233/JIFS-17348 CrossRefzbMATHGoogle Scholar
  40. 40.
    Elhoseny, M.; Tharwat, A.; Hassanien, A.E.: Bezier curve based path planning in a dynamic field using modified genetic algorithm. J. Comput. Sci. (2017).  https://doi.org/10.1016/j.jocs.2017.08.004 Google Scholar
  41. 41.
    Kala, R.: Multi-robot path planning using co-evolutionary genetic programming. Expert Syst. Appl. 39(3), 3817–3831 (2012)CrossRefGoogle Scholar
  42. 42.
    Qu, H.; Xing, K.; Alexander, T.: An improved genetic algorithm with co-evolutionary strategy for global path planning of multiple mobile robots. Neurocomputing 120, 509–517 (2013).  https://doi.org/10.1016/j.neucom.2013.04.020 CrossRefGoogle Scholar
  43. 43.
    Purcaru, C.; Precup, R.E.; Iercan, D.; Fedorovici, L.O.; David, R.C.; Dragan, F.: Optimal robot path planning using gravitational search algorithm. Int. J. Artif. Intell. 10, 1–20 (2013)Google Scholar
  44. 44.
    Zhang, Y.; Gong, D.W.; Zhang, J.H.: Robot path planning in uncertain environment using multi-objective particle swarm optimization. Neurocomputing 103, 172–185 (2013)CrossRefGoogle Scholar
  45. 45.
    Dadgar, M.; Jafari, S.; Hamzeh, A.: A PSO-based multi-robot cooperation method for target searching in unknown environments. Neurocomputing 177, 62–74 (2016)CrossRefGoogle Scholar
  46. 46.
    Song, B.; Wang, Z.; Zou, L.: On global smooth path planning for mobile robots using a novel multimodal delayed PSO algorithm. Cognit. Comput. 9(1), 5–17 (2016)CrossRefGoogle Scholar
  47. 47.
    Tang, B.; Zhanxia, Z.; Luo, J.: A convergence-guaranteed particle swarm optimization method for mobile robot global path planning. Assembly Autom. 37(1), 114–129 (2017)CrossRefGoogle Scholar
  48. 48.
    Gandomi, A.H.; Alavi, A.H.: Krill herd: a new bio-inspired optimization algorithm. Commun. Nonlinear Sci. Numer. Simul. 17(12), 4831–4845 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  49. 49.
    Wang, H.; Yi, J.H.: An improved optimization method based on krill herd and artificial bee colony with information exchange. Memetic Comput. (2017).  https://doi.org/10.1007/s12293-017-0241-6
  50. 50.
    Li, J.; Tang, Y.; Hua, C.; Guan, X.: An improved krill herd algorithm: Krill herd with linear decreasing step. Appl. Math. Comput. 234, 356–367 (2014)MathSciNetzbMATHGoogle Scholar
  51. 51.
    Guo, L.; Wang, G.G.; Gandomi, A.H.; Alavi, A.H.; Duan, H.: A new improved krill herd algorithm for global numerical optimization. Neurocomputing 138, 392–402 (2014)CrossRefGoogle Scholar

Copyright information

© King Fahd University of Petroleum & Minerals 2018

Authors and Affiliations

  • D. Chandrasekhar Rao
    • 1
  • Manas R. Kabat
    • 2
  • Pradipta K. Das
    • 1
  • Prabir K. Jena
    • 3
  1. 1.Department of Information TechnologyVSSUTBurlaIndia
  2. 2.Department of Computer Science and EngineeringVSSUTBurlaIndia
  3. 3.Department of Mechanical EngineeringVSSUTBurlaIndia

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