Energy Consumption of Robotic Arm with the Local Reduction Method
Journal:
arXiv
Published Date:
Mar 6, 2025
Abstract
Energy consumption in robotic arms is a significant concern in industrial
automation due to rising operational costs and environmental impact. This study
investigates the use of a local reduction method to optimize energy efficiency
in robotic systems without compromising performance. The approach refines
movement parameters, minimizing energy use while maintaining precision and
operational reliability. A three-joint robotic arm model was tested using
simulation over a 30-second period for various tasks, including pick-and-place
and trajectory-following operations. The results revealed that the local
reduction method reduced energy consumption by up to 25% compared to
traditional techniques such as Model Predictive Control (MPC) and Genetic
Algorithms (GA). Unlike MPC, which requires significant computational
resources, and GA, which has slow convergence rates, the local reduction method
demonstrated superior adaptability and computational efficiency in real-time
applications. The study highlights the scalability and simplicity of the local
reduction approach, making it an attractive option for industries seeking
sustainable and cost-effective solutions. Additionally, this method can
integrate seamlessly with emerging technologies like Artificial Intelligence
(AI), further enhancing its application in dynamic and complex environments.
This research underscores the potential of the local reduction method as a
practical tool for optimizing robotic arm operations, reducing energy demands,
and contributing to sustainability in industrial automation. Future work will
focus on extending the approach to real-world scenarios and incorporating
AI-driven adjustments for more dynamic adaptability.
