Bio-inspired algorithms for industrial robot control using deep learning methods. (October 2021)

• Record Type: Journal Article
• Authors: Guan, Jiwen; Su, Yanzhao; Su, Ling; Sivaparthipan, C.B.; Muthu, BalaAnand
• Is Part Of: Sustainable energy technologies and assessments. Volume 47(2021)
• Publication Date: 2021-10
• Subjects: Deep learning -- Bio-inspired algorithm -- Industrial robot control; Renewable energy sources -- Periodicals; Energy development -- Technological innovations -- Periodicals; Electric power production -- Periodicals; Energy storage -- Periodicals; 333.79
• DOI: 10.1016/j.seta.2021.101473
• Languages: English
• ISSNs: 2213-1388

Abstract

Abstract: The biologically inspired algorithm is a significant embranchment of sequence on computational intelligence and plays a critical role in industrial robot control. Industrial robots are well suited for conducting manipulation or material handling functions in extremely constrained and predictable environments. By comparison, living organisms are highly resilient and able to perform tasks in evolving environments, such as stable locomotion over uneven terrain. The robotics challenge is to use inspiration from biology to build devices capable of functioning in unrestrained or moderately constrained environments. Hence, in this study, a Bio-inspired Intelligent Industrial Robot Control System (BIIRCS) has been suggested using Deep Learning methods. A bio-inspired neural network is considered to model the complex environment and to guide a team of robots for the coverage task. The collected data is fed into a Deep Learning Neural Network to comprehend the localization and recognition of present objects from various classes. With the derived data, appropriate robot actions can be planned and executed. Usable objects are identified and seized in the robot's workspace, or that feed is sufficient for unattainable objects. This study confirms the ability to create intelligent systems using existing Deep Learning algorithms and industrial robotics. The simulations' findings reveal that the new approach achieves a high-performance ratio of 83.5%, accuracy ratio of 95.4%, less operational time of 7.8%, low RMSE rate of 11.2%, and increased coverage task rate of 96.7% when compared to other existing approaches.