Implementation of Human Robot Interaction with Motion Planning and Control Parameters with Autonomous Systems in Industry 4.0

Abstract

By merging autonomous systems and human-robot interaction (HRI), Industry 4.0 has caused a paradigm shift in manufacturing and production processes. To maximize productivity and ensure smooth machine-human collaboration in this environment, motion planning, and control parameters must be effectively integrated with autonomous robots. With the help of hybrid deep learning models including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Recurrent Neural Networks (RNNs), the implementation of HRI with motion planning and control parameters in autonomous systems is proposed in this research. To increase productivity, safety, and flexibility in industrial settings, the suggested approach intends to enable robust and adaptive interaction between humans and robots. CNNs are used to analyze and comprehend the surroundings by the autonomous system through object identification and visual perception. LSTM and RNN models are used to combine this perception with the temporal components of motion planning and control. The suggested method allows the autonomous system to learn from sizable datasets of human-robot interactions, enabling the real-time prediction of human intentions and behavior. The models' ability to respond to changing settings and modify the motion planning and control parameters as necessary makes it easier to complete tasks quickly and work together. By solving important issues with human-robot collaboration, the adoption of this hybrid deep learning-based HRI system advances Industry 4.0. The suggested method improves the general effectiveness and safety of industrial operations by seamlessly integrating motion planning and control parameters with autonomous systems. Additionally, the hybrid deep learning models' adaptive nature enables ongoing learning and performance improvement over time.