Skip to main content
SpringerLink
Log in

Advance Particle Swarm Optimization-Based Navigational Controller For Mobile Robot

  • Research Article - Systems Engineering
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

While the robot is in motion, path planning should follow the three aspects: (1) acquire the knowledge from its environmental conditions. (2) determine its position in the environment and (3) decision-making and execution to achieve its highest-order goals. The present research work aims to develop an efficient particle swarm optimization-based path planner of an autonomous mobile robot. In this approach, a fitness function has been introduced for converting the mobile robot navigation problem into multi objective optimization problem. The fitness of the swarm mainly depends on two parameters: (1) distance between each particle of the swarm and target, (2) distance between each particle of the swarm and the nearest obstacle. From the obtained fitness values of the swarm, the global best position of the particle is selected in each cycle. Thereby, the robot reaches the global best position in sequence. The effectiveness of the developed algorithm in various environments has been verified by simulation modes.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Abbreviations

C 1 :

Cognitive parameter considering in position shift

C 2 :

Social parameter considering in position shift

F i :

ith particle primary fitness value

F final :

Final fitness function

(goalx, goaly):

Robot destination position in its work space

λ R-Ob :

Distance between robot and sensed obstacle

\({\lambda _{P_i -T}}\) :

Distance between ith particle and target position.

\({\lambda _{P_i -{\rm NOb}}}\) :

Distance between ith particle and nearest obstacle

p :

Population size/number of particles in the swarm.

NOb:

Nearest obstacle within robots sensing range

(NOb x , NOb y ):

Nearest obstacle centre in robotic environment

(obx i ,oby i ):

ith sensed obstacle centre in XY-plane

\({(p_{x_i }, p_{y_i })}\) :

Position of ith particle in the swarm

rand1 and rand2:

Random variables considering in position shift

(robotx, roboty):

X and Y coordinates of robot position in XY-plane

S ob :

Number of sensed obstacles by the robot

v i :

Velocity of ith particle

W 1 :

Proportionality constant of fitness function

X gbest :

Global best value of particle in the swarm

x i :

Position of ith particle

X pbest :

Position best value of particle in the swarm

z :

PSO iteration counter

References

  1. Park, M.G.; Lee, M.C.: Experimental evaluation of robot path planning by artificial potential field approach with simulated annealing. In: 41st SICE Annual Conference, August 2002, vol. 4. Osaka, pp. 2190-2195. (2002). doi:10.1109/SICE.2002.1195739

  2. Oommen B.J., Sitharama I., Nageswara S.V.R., Kashyap R.L.: Robot navigation in unknown terrains using learned visibility graphs. Part I: the disjoint convex obstacle case. IEEE J. Robot. Autom. 3(6), 672–681 (1997)

    Google Scholar 

  3. Qin, Y.Q.; Sun, D.B.; Li, M.; Cen; Y.G.: Path planning for mobile robot using the particle swarm optimization with mutation operator. In: Third International Conference on Machine Laming and Cybernetics, vol. 4, Shanghai, August 2004, pp. 2473–2478. (2004). doi:10.1109/ICMLC.2004.1382219

  4. Glavaški, D.; Volf M.; Bonković, M.: Robot motion planning using exact cell decomposition and potential field methods. In: 9th WSEAS International Conference on Simulation, Modelling and Optimization, Budapest, Hungary, pp. 126–131 (2009)

  5. Kennedy, J.; Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, Perth, Australia, Nov/Dec 1995, vol. 4, pp. 1942–1948. (1995). doi:10.1109/ICNN.1995.488968

  6. Hassan R., Cohanim B., Weck O.D., Venter G.: A comparison of particle swarm optimization and the genetic algorithm. In: 1st AIAA Multidisciplinary Design Optimization Specialist Conference., No. AIAA-2005-1897, Austin (2005)

    Google Scholar 

  7. Alireza1 A.L.F.I.: PSO with adaptive mutation and inertia weight and its application in parameter estimation of dynamic systems. Acta Autom. Sinica 37(5), 541–549 (2011)

  8. Zhang, Y.; Xuan, J.; Benildo, G.D.L.R.; Clarke, R.; Habtom, W.R.: Reverse engineering module networks by PSO-RNN hybrid modelling. In: International Conference on Bioinformatics and Computational Biology, Las Vegas, pp. 1–18 (2008)

  9. Wu Q.: Car assembly line fault diagnosis based on robust wavelet SVC and PSO. Exp. Syst. Appl. 37(7), 5423–5429 (2010)

    Article  Google Scholar 

  10. Sha D.Y., Lin H.H.: A multi-objective PSO for job-shop scheduling problems. Exp. Syst. Appl. 37(2), 1065–1070 (2010)

    Article  Google Scholar 

  11. Yiqing L., Xigang Y., Yongjian L.: An improved PSO algorithm for solving non-convex NLP/MINLP problems with equality constraints. Comput. Chem. Eng. 31(3), 153–162 (2007)

    Article  Google Scholar 

  12. Venayagamoorthy, G.K.; Doctor, S.: Navigation of mobile sensors using PSO and embedded PSO in a fuzzy logic controller. In: 39th IAS Annual Meeting Industry Applications IEEE Conference, vol. 2, October 2004, pp. 1200–1206. (2004). doi:10.1109/IAS.2004.1348565

  13. Zhang, Q.; Li, S.: A global path planning approach based on particle swarm optimization for a mobile robot. In: 7th WSEAS International Conference on Robotics, Control and Manufacturing Technology, Hangzhou, China, ISBN:111-222-5555-66-7, pp. 263-267. (2007)

  14. Masehian E., Sedighizadeh D.: Multi-Objective PSO- and NPSO-based algorithms for robot path planning. Adv. Electr. Comput. Eng. 10(4), 69–76 (2010)

    Article  Google Scholar 

  15. Kwok N.M., Liu D.K., Dissanayake G.: Evolutionary computing based mobile robot localization. Eng. Appl. Artif. Intell. 19(5), pp. 857–868 (2006)

    Article  Google Scholar 

  16. Derr, K.; Manic, M.; Multi-robot: multi-target particle swarm optimization search in noisy wireless environments. In: Proceedings Human System Interactions’09, Catania, Italy, May 2009, pp. 81–86. (2009). doi:10.1109/HSI.2009.5090958

  17. Doctor, S.; Venayagamoorthy, G.K.; Gudise, V.G.: Optimal PSO for collective robotic search applications. In: Congress on Evolutionary Computation, vol. 2, June 2004, pp. 1390–1395. (2004). doi:10.1109/CEC.2004.1331059

  18. Smith, L., Venayagamoorthy, G.K.; Holloway P.G.: Obstacle avoidance in collective robotic search using particle swarm optimization. In: IEEE Swarm Intelligence Symposium, Indianapolis, USA, May 2006

  19. Lu, L.; Gong, D.: Robot path planning in unknown environments using particle swarm optimization. In: Fourth International Conference on Natural Computation, vol. 4, Jinan, China, October 2008, pp. 422–426. (2008). doi:10.1109/ICNC.2008.923

  20. Li, Y. M.; Chen, X.: A new stochastic pso technique for neural network training. In: Wang, J.; Zhang, Y.; Zurada, J.M.; Lu, B.L.; Yin, H. (eds.) Advances in Neural Networks-ISNN2006, pp. 564–569. Springer Press, Berlin (2006)

  21. Deepak, B. B. V. L.; Parhi, D.: Intelligent adaptive immune-based motion planner of a mobile robot in cluttered environment. Intell. Serv. Robot. Springer, Berlin Heidelberg, (2013). doi:10.1007/s11370-013-0131-9

  22. Yong, B.Q.; Ming, L.S.; Yan S.W.; Jin, A.M.: A fuzzy behavior-based architecture for mobile robot navigation in unknown environments. Proc. Int. Conf. Artif. Intell. Comput. Intell. 257–261 (2009)

  23. Mester G., Rodić A.: Sensor-based intelligent mobile robot navigation in unknown environments. Int. J. Electr. Comput. Eng. Syst. 1(2), 55–62 (2010)

    Google Scholar 

  24. Deepak VL., Parhi DR.: PSO based path planner of an autonomous mobile robot. Cent. Eur. J. Comput. Sci. 2(2), pp. 152–168 (2012)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Robotics Lab., Department of Mechanical Engineering, National Institute of Technology, Rourkela, India

    B. B. V. L. Deepak & Dayal R. Parhi

  2. Department of Industrial Design, National Institute of Technology, Rourkela, India

    B. M. V. A. Raju

Authors
  1. B. B. V. L. Deepak
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dayal R. Parhi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. B. M. V. A. Raju
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to B. B. V. L. Deepak.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Deepak, B.B.V.L., Parhi, D.R. & Raju, B.M.V.A. Advance Particle Swarm Optimization-Based Navigational Controller For Mobile Robot. Arab J Sci Eng 39, 6477–6487 (2014). https://doi.org/10.1007/s13369-014-1154-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-014-1154-z

Keywords

Search

Navigation