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Energy-Efficient Robot Configuration and Motion Planning Using Genetic Algorithm and Particle Swarm Optimization

Author

Listed:
  • Kazuki Nonoyama

    (Graduate School of Natural Science and Technology, Okayama University, 3-1-1 Tsushima-naka, Kita-ku, Okayama City 700-8530, Okayama, Japan)

  • Ziang Liu

    (Graduate School of Natural Science and Technology, Okayama University, 3-1-1 Tsushima-naka, Kita-ku, Okayama City 700-8530, Okayama, Japan)

  • Tomofumi Fujiwara

    (Graduate School of Natural Science and Technology, Okayama University, 3-1-1 Tsushima-naka, Kita-ku, Okayama City 700-8530, Okayama, Japan)

  • Md Moktadir Alam

    (Graduate School of Natural Science and Technology, Okayama University, 3-1-1 Tsushima-naka, Kita-ku, Okayama City 700-8530, Okayama, Japan)

  • Tatsushi Nishi

    (Graduate School of Natural Science and Technology, Okayama University, 3-1-1 Tsushima-naka, Kita-ku, Okayama City 700-8530, Okayama, Japan)

Abstract

The implementation of Industry 5.0 necessitates a decrease in the energy consumption of industrial robots. This research investigates energy optimization for optimal motion planning for a dual-arm industrial robot. The objective function for the energy minimization problem is stated based on the execution time and total energy consumption of the robot arm configurations in its workspace for pick-and-place operation. Firstly, the PID controller is being used to achieve the optimal parameters. The parameters of PID are then fine-tuned using metaheuristic algorithms such as Genetic Algorithms and Particle Swarm Optimization methods to create a more precise robot motion trajectory, resulting in an energy-efficient robot configuration. The results for different robot configurations were compared with both motion planning algorithms, which shows better compatibility in terms of both execution time and energy efficiency. The feasibility of the algorithms is demonstrated by conducting experiments on a dual-arm robot, named as duAro. In terms of energy efficiency, the results show that dual-arm motions can save more energy than single-arm motions for an industrial robot. Furthermore, combining the robot configuration problem with metaheuristic approaches saves energy consumption and robot execution time when compared to motion planning with PID controllers alone.

Suggested Citation

  • Kazuki Nonoyama & Ziang Liu & Tomofumi Fujiwara & Md Moktadir Alam & Tatsushi Nishi, 2022. "Energy-Efficient Robot Configuration and Motion Planning Using Genetic Algorithm and Particle Swarm Optimization," Energies, MDPI, vol. 15(6), pages 1-20, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:6:p:2074-:d:769562
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    References listed on IDEAS

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    1. Rafal Szczepanski & Artur Bereit & Tomasz Tarczewski, 2021. "Efficient Local Path Planning Algorithm Using Artificial Potential Field Supported by Augmented Reality," Energies, MDPI, vol. 14(20), pages 1-14, October.
    2. Ziang Liu & Tatsushi Nishi, 2020. "Multipopulation Ensemble Particle Swarm Optimizer for Engineering Design Problems," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-30, November.
    3. Dariusz Horla & Jacek Cieślak, 2020. "On Obtaining Energy-Optimal Trajectories for Landing of UAVs," Energies, MDPI, vol. 13(8), pages 1-25, April.
    4. Pengwei Wang & Song Gao & Liang Li & Binbin Sun & Shuo Cheng, 2019. "Obstacle Avoidance Path Planning Design for Autonomous Driving Vehicles Based on an Improved Artificial Potential Field Algorithm," Energies, MDPI, vol. 12(12), pages 1-14, June.
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    Cited by:

    1. Tudorică, Bogdan-George & Bucur, Cristian & Panait, Mirela & Oprea, Simona-Vasilica & Bâra, Adela, 2024. "Energetic Equilibrium: Optimizing renewable and non-renewable energy sources via particle swarm optimization," Utilities Policy, Elsevier, vol. 87(C).
    2. Piotr Szeląg & Sebastian Dudzik & Anna Podsiedlik, 2023. "Investigation on the Mobile Wheeled Robot in Terms of Energy Consumption, Travelling Time and Path Matching Accuracy," Energies, MDPI, vol. 16(3), pages 1-30, January.
    3. Md Moktadir Alam & Tatsushi Nishi & Ziang Liu & Tomofumi Fujiwara, 2023. "A Novel Sampling-Based Optimal Motion Planning Algorithm for Energy-Efficient Robotic Pick and Place," Energies, MDPI, vol. 16(19), pages 1-22, September.
    4. Rabab Benotsmane & György Kovács, 2023. "Optimization of Energy Consumption of Industrial Robots Using Classical PID and MPC Controllers," Energies, MDPI, vol. 16(8), pages 1-28, April.

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