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.
Highlights
IntroductionThe target of doubling global energy efficiency by 2030 is one of the major objectives of the Sustainable
To the best of our knowledge, Particle Swarm Optimization (PSO) is not used for industrial robots’ energy efficiency gain tuning even though we found that this algorithm is applied for mobile types of robots [38,39]
In comparison to the case in which the weighting factors changed from 0 to 10, the total completion time increased by 1.7 times, while the energy consumption can be lowered by 0.2 times
Summary
The target of doubling global energy efficiency by 2030 is one of the major objectives of the Sustainable. According to the report of Energy Information Administration (EIA) [2], globally, the industrial sector consumes more energy than any other sector, accounting for around 54% of total supplied energy. In this phase of Industry 5.0, industrial robots have a broad range of applications in current production and will continue to impact smart manufacturing because of their superior repeatability, controllability, and flexibility, and may face a substantial problem due to inefficiency in energy consumption. Optimization in robot path planning has been established as one of the ways for enhancing energy efficiency in robotic systems
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