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Reinforcement Learning

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

Kazuki Nonoyama(Okayama University), Ziang Liu(Okayama University), Tomofumi Fujiwara(Okayama University), Md Moktadir Alam(Okayama University), Tatsushi Nishi(Okayama University)
March 11, 2022Energies89 citations
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89

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Energies

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2022

Year

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.

Analysis

Why This Paper Matters

As Industry 5.0 emphasizes sustainability alongside automation, reducing energy consumption in industrial robots becomes critical. This paper directly addresses that need by optimizing motion planning for dual-arm robots, which are increasingly common in manufacturing. The use of metaheuristic algorithms to fine-tune PID controllers is a practical approach that balances computational simplicity with significant energy savings. The experimental validation on a real duAro robot adds credibility and shows immediate applicability.

Technical Contributions

  • Energy minimization objective: The paper defines a clear objective function that combines execution time and total energy consumption, providing a quantifiable target for optimization.
  • PID + metaheuristic hybrid: By first using PID for baseline control and then applying Genetic Algorithms and Particle Swarm Optimization to tune PID parameters, the method improves trajectory precision without requiring complex model-based control.
  • Dual-arm vs single-arm comparison: The study systematically compares energy efficiency between dual-arm and single-arm motions, revealing that dual-arm configurations can save more energy—a valuable insight for robot cell design.
  • Real robot validation: Experiments on the duAro robot demonstrate feasibility beyond simulation, which is crucial for industrial adoption.

Results

The paper reports that dual-arm motions achieve better energy efficiency than single-arm motions. Additionally, combining robot configuration with metaheuristic optimization (GA or PSO) reduces both energy consumption and execution time compared to using PID controllers alone. While exact numerical metrics are not provided in the abstract, the qualitative comparisons indicate clear improvements in both energy and time dimensions.

Significance

This research contributes to the growing field of energy-aware robotics, which is essential for sustainable manufacturing. By showing that simple metaheuristic tuning of existing PID controllers can yield meaningful energy savings, the work lowers the barrier for adoption in industry. The findings also encourage further exploration of multi-arm coordination for energy efficiency, potentially influencing robot design and deployment strategies in factories.

energy-efficiencymotion-planninggenetic-algorithmparticle-swarm-optimizationindustrial-robotpid-controldual-arm-robot
Fine Tuning

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