Comparison of GSA, SA and PSO Based Intelligent Controllers for Path Planning of Mobile Robot in Unknown Environment
Now-a-days autonomous mobile robots have found
applications in diverse fields. An autonomous robot system must be
able to behave in an intelligent manner to deal with complex and
changing environment. This work proposes the performance of path
planning and navigation of autonomous mobile robot using
Gravitational Search Algorithm (GSA), Simulated Annealing (SA)
and Particle Swarm optimization (PSO) based intelligent controllers
in an unstructured environment. The approach not only finds a valid
collision free path but also optimal one. The main aim of the work is
to minimize the length of the path and duration of travel from a
starting point to a target while moving in an unknown environment
with obstacles without collision. Finally, a comparison is made
between the three controllers, it is found that the path length and time
duration made by the robot using GSA is better than SA and PSO
based controllers for the same work.
[1] J. H. Lin and Li-Ren Huang, “Chaotic Bee Swarm Optimization
Algorithm for Path Planning of Mobile Robots,” Proceedings of 10th
WSEAS International Conference on Evolutionary Computing, pp. 84-
89, Wisconsin, USA, March 23, 2009.
[2] P K Panigrahi, S Ghosh and D R Parhi, “A Comparison of Mamdani
and Sugeno Based Fuzzy Controller for Mobile Robot to Avoid Static
Obstacles”, 5th International Elsevier Conference Electronics and
Computer Science (IEMCON), pp.226-231, Aug 28-31, Kolkata, 2014.
[3] P K Panigrahi, S Ghosh and D R Parhi, “A Novel Intelligent Mobile
Robot Navigation Technique for avoiding Obstacles Using RBF Neural
Network”, IEEE International Conference on Control, Instrumentation,
Energy and Communication (CIEC), pp. 51-56 , Dec 31-Feb 2, Kolkata,
2014.
[4] O.Castillo, L.Trujillo and P. Melin, “Multiple Objective Genetic
Algorithms for Path Planning Optimization in Autonomous Mobile
Robot,” Springer International Journal of Soft Computing, vol. 11, pp.
269-279, 2007.
[5] G. K. Venayagamoorthy, L. L. Grant and S. Doctor, “Collective Robotic
Search Using Hybrid Techniques: Fuzzy Logic and Swarm Intelligence
inspired by Nature”, Journal of Engineering Applications of Artificial
Intelligence, vol. 22, pp. 431-441, 2009.
[6] M. Yarmohamadi, H. H. S. Javadi and H. Erfani, “Improvement of
Robot Path Planning Using Particle Swarm Optimization in Dynamic
Environments with Mobile Obstacles and Target”, Journal of Advanced
Studies in Biology, vol. 3, no.1, pp. 43-53, 2011.
[7] B. Mohajer, K. Kiani, E. Sameiei and M. Sharifi, “A New Online
Random Particles Optimization Algorithm for Mobile Robot Path
planning in Dynamic environments”, Hindwai Journal of Mathematical
Problems in Engineering, vol. 2, pp. 1-9, 2013.
[8] S. Ahmadzadeh and M. Ghanavati, “Navigation of Mobile Robot using
the Particle Swarm Optimization”, Journal of Academic and Applied
Studies (JAAS), vol. 2, pp. 32-38, 2012.
[9] D. R. Parhi, J. K. Pothal and M. K. Singh, “Navigation of Multiple
Mobile Robots using Swarm Intelligence”, IEEE conference on Nature
and Biological Inspired Computing, pp. 1145-1149, Coimbatore, Dec 9-
11, 2009.
[10] R. R. Sahoo, P. Rakshit, Md T. Haider, S. Swarnalipi, B. K.
Balabantaray and S. Mohapatra, “Navigational Path Planning of Multi-
Robot using Honey Bee Mating Optimization Algorithm (HBMO)”,
International Journal of Computer Applications, vol. 27, no.11, August
2011.
[11] N. A. Shiltagh and L. D. Jalal, “Optimal Path Planning for Intelligent
Mobile Robot Navigation using Modified Particle Swarm
Optimization”, International Journal of Engineering and Advanced
Technology, vol. 2, Issue - 4, pp. 260-267, April 2013.
[12] C. A. Sierakowski and L. D.S. Coelho, “Path Planning Optimization for
Mobile Robots Based on Bacteria Colony Approach“, Springer Book
Series of Applied Soft Computing Technologies: The challenge of
complexity, vol. 34, pp.187-198, 2006.
[13] E. Masehian and D. Sedighizadeh, “Multi-Objective PSO and NPSO
based Algorithms for Robot Path Planning”, Journal of Advances in
Electrical and Computer Engineering, vol. 10, no.4, pp. 69-76, 2010.
[14] Xianmin Wei, “Robot Path Planning Based on Simulated Annealing and
Artificial Neural networks”, Journal of Applied Sciences, Engineering
and Technology, vol. 5(23), pp.5384-5390, 2006.
[15] P. Curkovic and B. Jerbic, “Honey Bees Optimization Algorithm
Applied to Path Planning Problem”, International Journal of Simulation
Model, vol. 6, pp. 154-164, 2007.
[16] W. Parvez and S. Dhar, “Path Planning Optimization Using Genetic
Algorithm –A literature review”, International Journal of Computational
Engineering Research, vol. 3, Issue 4, pp. 23-28, 2013.
[17] D R Parhi, P K Panigrahi and S Ghosh,“ Wavelet Neural Network Based
Intelligent Mobile Robotic Agent Operating in an Unknown
Environment”, International Journal of Artificial Intelligence and
Computational Research, vol no.6, Issue no 1, pp. 77-83,2014.
[18] S. Yazdani, H. Nezamabadi-pour and S. Kamyab, “A Gravitational
Search Algorithm for Multimodal Optimization”, Elsevier Journal of
Swarm and Evolutionary Computation, vol. 14, pp.1-14, 2014.
[19] T Ghose, “Optimization Technique and an Introduction to Genetic
Algorithms and Simulated Annealing”, Proceedings of International
workshop on Soft Computing and Systems, pp.1-19, August, Mesra,
2002.
[20] G Bayar, E I Konukseven and A BugraKoku,“ Control of Differentially
Driven Mobile Robot Using Radial Basis Function Based Neural
Networks”, WSEAS Transactions on Systems and Control, vol-3, issue
12, Dec 2008.
[21] S Noman , S M Shamsuddin and A E Hassanien,“ Hybrid Learning
Enhancement of RBF Network with Particle Swarm Optimization”,
Foundations of Computational Intelligence , vol. 1,pp. 381-397,2009.
[1] J. H. Lin and Li-Ren Huang, “Chaotic Bee Swarm Optimization
Algorithm for Path Planning of Mobile Robots,” Proceedings of 10th
WSEAS International Conference on Evolutionary Computing, pp. 84-
89, Wisconsin, USA, March 23, 2009.
[2] P K Panigrahi, S Ghosh and D R Parhi, “A Comparison of Mamdani
and Sugeno Based Fuzzy Controller for Mobile Robot to Avoid Static
Obstacles”, 5th International Elsevier Conference Electronics and
Computer Science (IEMCON), pp.226-231, Aug 28-31, Kolkata, 2014.
[3] P K Panigrahi, S Ghosh and D R Parhi, “A Novel Intelligent Mobile
Robot Navigation Technique for avoiding Obstacles Using RBF Neural
Network”, IEEE International Conference on Control, Instrumentation,
Energy and Communication (CIEC), pp. 51-56 , Dec 31-Feb 2, Kolkata,
2014.
[4] O.Castillo, L.Trujillo and P. Melin, “Multiple Objective Genetic
Algorithms for Path Planning Optimization in Autonomous Mobile
Robot,” Springer International Journal of Soft Computing, vol. 11, pp.
269-279, 2007.
[5] G. K. Venayagamoorthy, L. L. Grant and S. Doctor, “Collective Robotic
Search Using Hybrid Techniques: Fuzzy Logic and Swarm Intelligence
inspired by Nature”, Journal of Engineering Applications of Artificial
Intelligence, vol. 22, pp. 431-441, 2009.
[6] M. Yarmohamadi, H. H. S. Javadi and H. Erfani, “Improvement of
Robot Path Planning Using Particle Swarm Optimization in Dynamic
Environments with Mobile Obstacles and Target”, Journal of Advanced
Studies in Biology, vol. 3, no.1, pp. 43-53, 2011.
[7] B. Mohajer, K. Kiani, E. Sameiei and M. Sharifi, “A New Online
Random Particles Optimization Algorithm for Mobile Robot Path
planning in Dynamic environments”, Hindwai Journal of Mathematical
Problems in Engineering, vol. 2, pp. 1-9, 2013.
[8] S. Ahmadzadeh and M. Ghanavati, “Navigation of Mobile Robot using
the Particle Swarm Optimization”, Journal of Academic and Applied
Studies (JAAS), vol. 2, pp. 32-38, 2012.
[9] D. R. Parhi, J. K. Pothal and M. K. Singh, “Navigation of Multiple
Mobile Robots using Swarm Intelligence”, IEEE conference on Nature
and Biological Inspired Computing, pp. 1145-1149, Coimbatore, Dec 9-
11, 2009.
[10] R. R. Sahoo, P. Rakshit, Md T. Haider, S. Swarnalipi, B. K.
Balabantaray and S. Mohapatra, “Navigational Path Planning of Multi-
Robot using Honey Bee Mating Optimization Algorithm (HBMO)”,
International Journal of Computer Applications, vol. 27, no.11, August
2011.
[11] N. A. Shiltagh and L. D. Jalal, “Optimal Path Planning for Intelligent
Mobile Robot Navigation using Modified Particle Swarm
Optimization”, International Journal of Engineering and Advanced
Technology, vol. 2, Issue - 4, pp. 260-267, April 2013.
[12] C. A. Sierakowski and L. D.S. Coelho, “Path Planning Optimization for
Mobile Robots Based on Bacteria Colony Approach“, Springer Book
Series of Applied Soft Computing Technologies: The challenge of
complexity, vol. 34, pp.187-198, 2006.
[13] E. Masehian and D. Sedighizadeh, “Multi-Objective PSO and NPSO
based Algorithms for Robot Path Planning”, Journal of Advances in
Electrical and Computer Engineering, vol. 10, no.4, pp. 69-76, 2010.
[14] Xianmin Wei, “Robot Path Planning Based on Simulated Annealing and
Artificial Neural networks”, Journal of Applied Sciences, Engineering
and Technology, vol. 5(23), pp.5384-5390, 2006.
[15] P. Curkovic and B. Jerbic, “Honey Bees Optimization Algorithm
Applied to Path Planning Problem”, International Journal of Simulation
Model, vol. 6, pp. 154-164, 2007.
[16] W. Parvez and S. Dhar, “Path Planning Optimization Using Genetic
Algorithm –A literature review”, International Journal of Computational
Engineering Research, vol. 3, Issue 4, pp. 23-28, 2013.
[17] D R Parhi, P K Panigrahi and S Ghosh,“ Wavelet Neural Network Based
Intelligent Mobile Robotic Agent Operating in an Unknown
Environment”, International Journal of Artificial Intelligence and
Computational Research, vol no.6, Issue no 1, pp. 77-83,2014.
[18] S. Yazdani, H. Nezamabadi-pour and S. Kamyab, “A Gravitational
Search Algorithm for Multimodal Optimization”, Elsevier Journal of
Swarm and Evolutionary Computation, vol. 14, pp.1-14, 2014.
[19] T Ghose, “Optimization Technique and an Introduction to Genetic
Algorithms and Simulated Annealing”, Proceedings of International
workshop on Soft Computing and Systems, pp.1-19, August, Mesra,
2002.
[20] G Bayar, E I Konukseven and A BugraKoku,“ Control of Differentially
Driven Mobile Robot Using Radial Basis Function Based Neural
Networks”, WSEAS Transactions on Systems and Control, vol-3, issue
12, Dec 2008.
[21] S Noman , S M Shamsuddin and A E Hassanien,“ Hybrid Learning
Enhancement of RBF Network with Particle Swarm Optimization”,
Foundations of Computational Intelligence , vol. 1,pp. 381-397,2009.
@article{"International Journal of Electrical, Electronic and Communication Sciences:68729", author = "P. K. Panigrahi and Saradindu Ghosh and Dayal R. Parhi", title = "Comparison of GSA, SA and PSO Based Intelligent Controllers for Path Planning of Mobile Robot in Unknown Environment", abstract = "Now-a-days autonomous mobile robots have found
applications in diverse fields. An autonomous robot system must be
able to behave in an intelligent manner to deal with complex and
changing environment. This work proposes the performance of path
planning and navigation of autonomous mobile robot using
Gravitational Search Algorithm (GSA), Simulated Annealing (SA)
and Particle Swarm optimization (PSO) based intelligent controllers
in an unstructured environment. The approach not only finds a valid
collision free path but also optimal one. The main aim of the work is
to minimize the length of the path and duration of travel from a
starting point to a target while moving in an unknown environment
with obstacles without collision. Finally, a comparison is made
between the three controllers, it is found that the path length and time
duration made by the robot using GSA is better than SA and PSO
based controllers for the same work.
", keywords = "Autonomous Mobile Robot, Gravitational Search Algorithm, Particle Swarm Optimization, Simulated Annealing Algorithm.", volume = "8", number = "10", pages = "1633-10", }
