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

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-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 languageEnglish
Pages (from-to)389-406
Number of pages18
JournalInternational Journal of Advanced Manufacturing Technology
Volume89
Issue number1-4
DOIs
Publication statusPublished - Mar 2017
MoE publication typeA1 Journal article-refereed

Keywords

  • Industrial robots
  • Minimum-time trajectory planning
  • Obstacle avoidance
  • POLYNOMIAL JOINT TRAJECTORIES
  • MECHANICAL MANIPULATORS
  • PAYLOAD CONSTRAINTS
  • OPTIMIZATION
  • ENERGY
  • FORMULATION
  • OBSTACLES
  • DYNAMICS
  • PATHS
  • ARMS

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