Trajectory planning and obstacle avoidance control of redundant robots using differential evolution and particle swarm optimization algorithms

Sujan Mario Warnakulasooriya; Sivalinga Govinda Ponnambalam

doi:10.1007/978-3-319-20294-5_51

Trajectory planning and obstacle avoidance control of redundant robots using differential evolution and particle swarm optimization algorithms

Sujan Mario Warnakulasooriya, Sivalinga Govinda Ponnambalam

Malaysia School of Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

1 Citation (Scopus)

Abstract

The problem of trajectory planning and obstacle avoidance in redundant robots is addressed in this paper. Four variants of Particle Swarm Optimization (PSO) and a Differential Evolution (DE) algorithm are proposed to solve this problem. Simulation experiments on a 5 degree-of-freedom (DOF) robot manipulator in an environment with static obstacles are conducted. The manipulator is required to move from a start position to a goal position with minimum error while avoiding collision with the obstacles in the workspace. The performance of the proposed algorithms is compared with the results reported in the literature and the comparative results are presented. It is observed that qPSO-C performs better in free space and PSO-C performs better in environment with obstacles in terms of minimizing error average convergence time. The performance of DE improves when the number of obstacles increases.

Original language	English
Title of host publication	Proceedings of the Swarm, Evolutionary and Memetic Computing: 5th International Conference, SEMCCO 2014
Editors	Bijaya Ketan Panigrahi, Ponnuthurai Nagaratnam Suganthan, Swagatam Das
Place of Publication	Switzerland
Publisher	Springer
Pages	596 - 605
Number of pages	10
Volume	8947
ISBN (Print)	9783319202938
DOIs	https://doi.org/10.1007/978-3-319-20294-5_51
Publication status	Published - 2015
Event	International Conference on Swarm, Evolutionary, and Memetic Computing (SEMCCO) 2014 - e Institute of Technical Education and Research, Siksha 'O' Anusandhan University, Bhubaneswar, India Duration: 18 Dec 2014 → 20 Dec 2014 Conference number: 5th https://link.springer.com/book/10.1007/978-3-319-20294-5

Conference

Conference	International Conference on Swarm, Evolutionary, and Memetic Computing (SEMCCO) 2014
Abbreviated title	SEMCCO 2014
Country/Territory	India
City	Bhubaneswar
Period	18/12/14 → 20/12/14
Internet address	https://link.springer.com/book/10.1007/978-3-319-20294-5

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10.1007/978-3-319-20294-5_51

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Warnakulasooriya, S. M., & Ponnambalam, S. G. (2015). Trajectory planning and obstacle avoidance control of redundant robots using differential evolution and particle swarm optimization algorithms. In B. K. Panigrahi, P. N. Suganthan, & S. Das (Eds.), Proceedings of the Swarm, Evolutionary and Memetic Computing: 5th International Conference, SEMCCO 2014 (Vol. 8947, pp. 596 - 605). Springer. https://doi.org/10.1007/978-3-319-20294-5_51

Warnakulasooriya, Sujan Mario ; Ponnambalam, Sivalinga Govinda. / Trajectory planning and obstacle avoidance control of redundant robots using differential evolution and particle swarm optimization algorithms. Proceedings of the Swarm, Evolutionary and Memetic Computing: 5th International Conference, SEMCCO 2014. editor / Bijaya Ketan Panigrahi ; Ponnuthurai Nagaratnam Suganthan ; Swagatam Das. Vol. 8947 Switzerland : Springer, 2015. pp. 596 - 605

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title = "Trajectory planning and obstacle avoidance control of redundant robots using differential evolution and particle swarm optimization algorithms",

abstract = "The problem of trajectory planning and obstacle avoidance in redundant robots is addressed in this paper. Four variants of Particle Swarm Optimization (PSO) and a Differential Evolution (DE) algorithm are proposed to solve this problem. Simulation experiments on a 5 degree-of-freedom (DOF) robot manipulator in an environment with static obstacles are conducted. The manipulator is required to move from a start position to a goal position with minimum error while avoiding collision with the obstacles in the workspace. The performance of the proposed algorithms is compared with the results reported in the literature and the comparative results are presented. It is observed that qPSO-C performs better in free space and PSO-C performs better in environment with obstacles in terms of minimizing error average convergence time. The performance of DE improves when the number of obstacles increases.",

author = "Warnakulasooriya, \{Sujan Mario\} and Ponnambalam, \{Sivalinga Govinda\}",

year = "2015",

doi = "10.1007/978-3-319-20294-5\_51",

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editor = "Panigrahi, \{Bijaya Ketan\} and Suganthan, \{Ponnuthurai Nagaratnam\} and Swagatam Das",

booktitle = "Proceedings of the Swarm, Evolutionary and Memetic Computing: 5th International Conference, SEMCCO 2014",

publisher = "Springer",

address = "Switzerland",

note = "International Conference on Swarm, Evolutionary, and Memetic Computing (SEMCCO) 2014, SEMCCO 2014 ; Conference date: 18-12-2014 Through 20-12-2014",

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Warnakulasooriya, SM & Ponnambalam, SG 2015, Trajectory planning and obstacle avoidance control of redundant robots using differential evolution and particle swarm optimization algorithms. in BK Panigrahi, PN Suganthan & S Das (eds), Proceedings of the Swarm, Evolutionary and Memetic Computing: 5th International Conference, SEMCCO 2014. vol. 8947, Springer, Switzerland, pp. 596 - 605, International Conference on Swarm, Evolutionary, and Memetic Computing (SEMCCO) 2014, Bhubaneswar, India, 18/12/14. https://doi.org/10.1007/978-3-319-20294-5_51

Trajectory planning and obstacle avoidance control of redundant robots using differential evolution and particle swarm optimization algorithms. / Warnakulasooriya, Sujan Mario; Ponnambalam, Sivalinga Govinda.
Proceedings of the Swarm, Evolutionary and Memetic Computing: 5th International Conference, SEMCCO 2014. ed. / Bijaya Ketan Panigrahi; Ponnuthurai Nagaratnam Suganthan; Swagatam Das. Vol. 8947 Switzerland: Springer, 2015. p. 596 - 605.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

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AU - Ponnambalam, Sivalinga Govinda

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PY - 2015

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N2 - The problem of trajectory planning and obstacle avoidance in redundant robots is addressed in this paper. Four variants of Particle Swarm Optimization (PSO) and a Differential Evolution (DE) algorithm are proposed to solve this problem. Simulation experiments on a 5 degree-of-freedom (DOF) robot manipulator in an environment with static obstacles are conducted. The manipulator is required to move from a start position to a goal position with minimum error while avoiding collision with the obstacles in the workspace. The performance of the proposed algorithms is compared with the results reported in the literature and the comparative results are presented. It is observed that qPSO-C performs better in free space and PSO-C performs better in environment with obstacles in terms of minimizing error average convergence time. The performance of DE improves when the number of obstacles increases.

AB - The problem of trajectory planning and obstacle avoidance in redundant robots is addressed in this paper. Four variants of Particle Swarm Optimization (PSO) and a Differential Evolution (DE) algorithm are proposed to solve this problem. Simulation experiments on a 5 degree-of-freedom (DOF) robot manipulator in an environment with static obstacles are conducted. The manipulator is required to move from a start position to a goal position with minimum error while avoiding collision with the obstacles in the workspace. The performance of the proposed algorithms is compared with the results reported in the literature and the comparative results are presented. It is observed that qPSO-C performs better in free space and PSO-C performs better in environment with obstacles in terms of minimizing error average convergence time. The performance of DE improves when the number of obstacles increases.

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Warnakulasooriya SM, Ponnambalam SG. Trajectory planning and obstacle avoidance control of redundant robots using differential evolution and particle swarm optimization algorithms. In Panigrahi BK, Suganthan PN, Das S, editors, Proceedings of the Swarm, Evolutionary and Memetic Computing: 5th International Conference, SEMCCO 2014. Vol. 8947. Switzerland: Springer. 2015. p. 596 - 605 doi: 10.1007/978-3-319-20294-5_51

Trajectory planning and obstacle avoidance control of redundant robots using differential evolution and particle swarm optimization algorithms

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