Robotized task time scheduling and optimization based on Genetic Algorithms for non redundant industrial manipulators

Khelifa Baizid, Amal Meddahi, Ali Yousnadj, Ryad Chellali, Hamza Khan, Jamshed Iqbal

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    9 Citations (Scopus)

    Abstract

    Industrial robot manipulators must work as fast as possible in order to increase the productivity. This goal could be achieved by increasing robots speed or/and optimizing the trajectories followed by robots while performing assembly, welding or similar tasks. In our contribution, we focus on the second aspect and we target the shortening of paths between task-points. In other words, the goal is to find the shorter traveled distance between different configurations in the coordinate space. In addition to the short distance goal, we aim as well to impose both IKM (Inverse Kinematic Model) and the relative position and orientation of the manipulator regarding the task-points. To this end, we propose an optimization method based on Genetics Algorithms. The method is validated via numerical and graphical simulation, where, results show that the total cycle time required to perform a spot-welding task of an industrial car-body by a 6-DOFs (Degree Of Freedoms) industrial manipulator was drastically reduced.

    Original languageEnglish
    Title of host publicationROSE 2014 - 2014 IEEE International Symposium on RObotic and SEnsors Environments, Proceedings
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages112-117
    Number of pages6
    ISBN (Electronic)9781479949274
    DOIs
    Publication statusPublished - 1 Jan 2014
    Event2014 12th IEEE International Symposium on Robotic and Sensors Environments, ROSE 2014 - Timisoara, Romania
    Duration: 16 Oct 201418 Oct 2014

    Conference

    Conference2014 12th IEEE International Symposium on Robotic and Sensors Environments, ROSE 2014
    Country/TerritoryRomania
    CityTimisoara
    Period16/10/1418/10/14

    Keywords

    • genetic algorithms
    • Industrial manipulator
    • optimization

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Human-Computer Interaction
    • Electrical and Electronic Engineering

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