Application of swarm intelligence to a two-fold optimization scheme for trajectory planning of a robot arm

Tathagata Chakraborti; Abhronil Sengupta; Amit Konar; Ramadoss Janarthanan

doi:10.1007/978-3-642-27242-4_11

Application of swarm intelligence to a two-fold optimization scheme for trajectory planning of a robot arm

Tathagata Chakraborti, Abhronil Sengupta, Amit Konar, Ramadoss Janarthanan

Electrical Engineering

Research output: Contribution to journal › Conference article › peer-review

4 Scopus citations

Abstract

Motion planning of a robotic arm has been an important area of research for the last decade with the growing application of robot arms in medical science and industries. In this paper the problem of motion planning has been dealt with in two stages, first by developing appropriate cost functions to determine a set of via points and then fitting an optimal energy trajectory. Lbest Particle Swarm Optimization has been used to solve the minimization problem and its relative performance with respect to two other popular evolutionary algorithms, Differential Evolution and Invasive Weed Optimization, has been studied. Experiments indicate swarm intelligence techniques to be far more efficient to solve the optimization problem.

Original language	English (US)
Pages (from-to)	89-96
Number of pages	8
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	7077 LNCS
Issue number	PART 2
DOIs	https://doi.org/10.1007/978-3-642-27242-4_11
State	Published - 2011
Event	2nd International Conference on Swarm, Evolutionary, and Memetic Computing, SEMCCO 2011 - Visakhapatnam, Andhra Pradesh, India Duration: Dec 19 2011 → Dec 21 2011

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

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Chakraborti, T., Sengupta, A., Konar, A., & Janarthanan, R. (2011). Application of swarm intelligence to a two-fold optimization scheme for trajectory planning of a robot arm. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7077 LNCS(PART 2), 89-96. https://doi.org/10.1007/978-3-642-27242-4_11

Chakraborti, Tathagata ; Sengupta, Abhronil ; Konar, Amit ; Janarthanan, Ramadoss. / Application of swarm intelligence to a two-fold optimization scheme for trajectory planning of a robot arm. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2011 ; Vol. 7077 LNCS, No. PART 2. pp. 89-96.

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abstract = "Motion planning of a robotic arm has been an important area of research for the last decade with the growing application of robot arms in medical science and industries. In this paper the problem of motion planning has been dealt with in two stages, first by developing appropriate cost functions to determine a set of via points and then fitting an optimal energy trajectory. Lbest Particle Swarm Optimization has been used to solve the minimization problem and its relative performance with respect to two other popular evolutionary algorithms, Differential Evolution and Invasive Weed Optimization, has been studied. Experiments indicate swarm intelligence techniques to be far more efficient to solve the optimization problem.",

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Chakraborti, T, Sengupta, A, Konar, A & Janarthanan, R 2011, 'Application of swarm intelligence to a two-fold optimization scheme for trajectory planning of a robot arm', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7077 LNCS, no. PART 2, pp. 89-96. https://doi.org/10.1007/978-3-642-27242-4_11

Application of swarm intelligence to a two-fold optimization scheme for trajectory planning of a robot arm. / Chakraborti, Tathagata; Sengupta, Abhronil; Konar, Amit; Janarthanan, Ramadoss.

In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 7077 LNCS, No. PART 2, 2011, p. 89-96.

Research output: Contribution to journal › Conference article › peer-review

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