Statistical evaluation of an evolutionary algorithm for minimum time trajectory planning problem for industrial robots

Fares J. Abu-Dakka; Iyad F. Assad; Rasha M. Alkhdour; Mohamed Abderahim

doi:10.1007/s00170-016-9050-1

Statistical evaluation of an evolutionary algorithm for minimum time trajectory planning problem for industrial robots

Fares J. Abu-Dakka, Iyad F. Assad, Rasha M. Alkhdour, Mohamed Abderahim

Mechanical Engineering

Research output: Contribution to journal › Article › peer-review

Abstract

This paper presents, evaluates, and validates a genetic algorithm procedure with parallel-populations for the obtaining of minimum time trajectories for robot manipulators. The aim of the algorithm is to construct smooth joint trajectories for robot manipulators using cubic polynomial functions, where the sequence of the robot configurations is already given. Three different types of constraints are considered in this work: (1) Kinematics: these include the limits of joint velocities, accelerations, and jerk. (2) Dynamic: which include limits of torque, power, and energy. (3) Payload constraints. A complete statistical analysis using ANOVA test is introduced in order to evaluate the efficiency of the proposed algorithm. In addition, a comparison analysis between the results of the proposed algorithm and other different techniques found in the literature is described in the experimental section of this paper.

Original language	English (US)
Pages (from-to)	389-406
Number of pages	18
Journal	International Journal of Advanced Manufacturing Technology
Volume	89
Issue number	1-4
DOIs	https://doi.org/10.1007/s00170-016-9050-1
State	Published - Mar 1 2017

Keywords

Industrial robots
Minimum-time trajectory planning
Obstacle avoidance

ASJC Scopus subject areas

Control and Systems Engineering
Software
Mechanical Engineering
Computer Science Applications
Industrial and Manufacturing Engineering

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10.1007/s00170-016-9050-1

Cite this

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title = "Statistical evaluation of an evolutionary algorithm for minimum time trajectory planning problem for industrial robots",

abstract = "This paper presents, evaluates, and validates a genetic algorithm procedure with parallel-populations for the obtaining of minimum time trajectories for robot manipulators. The aim of the algorithm is to construct smooth joint trajectories for robot manipulators using cubic polynomial functions, where the sequence of the robot configurations is already given. Three different types of constraints are considered in this work: (1) Kinematics: these include the limits of joint velocities, accelerations, and jerk. (2) Dynamic: which include limits of torque, power, and energy. (3) Payload constraints. A complete statistical analysis using ANOVA test is introduced in order to evaluate the efficiency of the proposed algorithm. In addition, a comparison analysis between the results of the proposed algorithm and other different techniques found in the literature is described in the experimental section of this paper.",

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AU - Abu-Dakka, Fares J.

AU - Assad, Iyad F.

AU - Alkhdour, Rasha M.

AU - Abderahim, Mohamed

PY - 2017/3/1

Y1 - 2017/3/1

N2 - This paper presents, evaluates, and validates a genetic algorithm procedure with parallel-populations for the obtaining of minimum time trajectories for robot manipulators. The aim of the algorithm is to construct smooth joint trajectories for robot manipulators using cubic polynomial functions, where the sequence of the robot configurations is already given. Three different types of constraints are considered in this work: (1) Kinematics: these include the limits of joint velocities, accelerations, and jerk. (2) Dynamic: which include limits of torque, power, and energy. (3) Payload constraints. A complete statistical analysis using ANOVA test is introduced in order to evaluate the efficiency of the proposed algorithm. In addition, a comparison analysis between the results of the proposed algorithm and other different techniques found in the literature is described in the experimental section of this paper.

AB - This paper presents, evaluates, and validates a genetic algorithm procedure with parallel-populations for the obtaining of minimum time trajectories for robot manipulators. The aim of the algorithm is to construct smooth joint trajectories for robot manipulators using cubic polynomial functions, where the sequence of the robot configurations is already given. Three different types of constraints are considered in this work: (1) Kinematics: these include the limits of joint velocities, accelerations, and jerk. (2) Dynamic: which include limits of torque, power, and energy. (3) Payload constraints. A complete statistical analysis using ANOVA test is introduced in order to evaluate the efficiency of the proposed algorithm. In addition, a comparison analysis between the results of the proposed algorithm and other different techniques found in the literature is described in the experimental section of this paper.

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Statistical evaluation of an evolutionary algorithm for minimum time trajectory planning problem for industrial robots

Abstract

Keywords

ASJC Scopus subject areas

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