Kure, Halima Ibrahim, Retnakumari, Jishna, Nita, Lucian, Sharif, Saeed, Balogun, Hamed and Nwajana, Augustine O. ORCID: https://orcid.org/0000-0001-6591-5269 (2025) Energy consumption of robotic arm with the local reduction method. In: 2025 PhotonIcs & Electromagnetics Research Symposium. IEEE Xplore . Institute of Electrical and Electronics Engineers (IEEE). (In Press)

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PDF (Accepted Conference Paper) 50501 NWAJANA_ Energy_Consumption_Of_Robotic_Arm_With_The_Local_Reduction_Method_(CONFERENCE PAPER AAM)_2025.pdf - Accepted Version Download (437kB) | Preview

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.

Item Type:	Conference Proceedings
Title of Proceedings:	2025 PhotonIcs & Electromagnetics Research Symposium
Uncontrolled Keywords:	energy optimization, robotic arm, local reduction method, trajectory optimization, sustainable robotics
Subjects:	Q Science > Q Science (General) T Technology > T Technology (General)
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Engineering (ENG)
Related URLs:	http://arxiv.org/abs/2503.04340
Last Modified:	21 May 2025 11:47
URI:	http://gala.gre.ac.uk/id/eprint/50501

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