Robotic Process Optimization Using Evolutionary Algorithms

  • Authors

    • David Thompson Vice President Operations, Global Solutions LLC, USA Author

    DOI:

    https://doi.org/10.67228/30715725/IJIARE-2020PIR9X

    Published 05-03-2020

  • Robotic Process Optimization, Evolutionary Computation, Genetic Algorithm, Industrial Robotics, Motion Planning, Multi-objective Optimization, Intelligent Automation, Adaptive Robotics

    Issue

    Vol. 3 No. 1 (2020)

    Section

    Articles

    How to Cite

    [1]
    D. Thompson, “Robotic Process Optimization Using Evolutionary Algorithms”, IJIARE, vol. 3, no. 1, pp. 01–17, May 2020, doi: 10.67228/30715725/IJIARE-2020PIR9X.

  • Abstract

    Industrial robotics plays a vital role in modern automation across industries such as automotive, aerospace, healthcare, agriculture, and defense. However, optimizing robotic processes remains challenging due to objectives like reducing energy consumption, minimizing cycle time, improving accuracy, and handling dynamic operating conditions. Traditional optimization methods often struggle with these complex problems. Evolutionary Algorithms (EAs) such as Genetic Algorithms (GA), Differential Evolution (DE), Evolution Strategies (ES), and Multi-Objective Evolutionary Algorithms (MOEA) provide effective solutions for robotic optimization. These techniques are widely used in robotic path planning, task scheduling, trajectory optimization, and resource management. Simulation results show that evolutionary algorithms improve operational efficiency, reduce collisions, minimize energy consumption, and optimize productivity better than conventional methods. Genetic Algorithms are effective for trajectory optimization, while Differential Evolution offers faster convergence in continuous optimization tasks. Multi-objective approaches achieve balanced trade-offs between productivity and energy efficiency. The study highlights the importance of evolutionary algorithms in developing intelligent and adaptive robotic systems for Industry 4.0. Future work includes hybrid optimization models, deep reinforcement learning integration, and real-time distributed robotic optimization.

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