Deep Reinforcement Learning-Based Real-time Data Analytics of Automatic Industrial Robot for High Speed Communication

Abstract

In Industry 4.0, industrial robot trajectory planning requires real-time collision avoidance. This is solved by our innovative Collision Criticality (CC) LSTM-DRL approach. This work suggests navigating many landscapes with diverse barriers using deep reinforcement learning (DRL) and long-short-term memory for data analytics in high Speed Communication. The robot's current state is used by Crit LSTM DRL to estimate collision timing and obstacle CC. Obstacle courses use CC ranks. Robotics creates a fixed-dimensional vector that accurately represents reality using LSTM models. After that, the robot's current state is added to this vector to complete its perception and cognition. Deep Reinforcement Learning (DRL) optimalizes state values using input data. At each time step, Deep Reinforcement Learning (DRL) and Long Short-Term Memory (LSTM) identify the next state values for all possible actions. System operation depends on the highest value's action. We learned three environmental models in this research. A scenario with five, 10, or one to ten barriers was classified. These models were tested for adaptability to different obstacle densities. In obstacle-rich situations, researchers tested these three models' strengths and weaknesses. Testing the algorithm in various settings proves its efficacy. The program also considers the combined influence of impediments on the robot by portraying them as a joint state. CC-LSTM-DRL performs well in Industry 4.0's dynamic and challenging conditions, according to experiments. Mobile robots can confidently navigate changing environments due to their versatility and flexibility, making them ideal industrial tools.