Integrating Neural Networks for Predictive Torque Control and Obstacle Avoidance in Autonomous Robot

In the field of robotics, precise motion control and accurate computation of joint forces are critical for ensuring optimal performance. Traditional methods, such as using the Jacobian matrix for joint angle determination and Euler-Lagrange equations for torque computation, are reliable but computationally intensive, making them less suitable for real-time applications. This paper presents an advanced approach to improving the productivity and efficiency of a 3-Degree of Freedom (DOF) robotic arm by utilizing Artificial Neural Network (ANN). The proposed system dynamically predicts joint angles and torque, enabling faster and more efficient motion control. To address the challenge of obstacle avoidance in dynamic environments, a Convolutional Neural Network (CNN) is integrated with pathfinding algorithms such as the A* algorithm. This combination facilitates real-time obstacle detection and avoidance while maintaining accurate predictions for joint angles and torque requirements. The implementation showcases the effectiveness of neural networks in optimizing motion control and improving the overall performance of robotic systems. The proposed methodology highlights a significant advancement in real-time robotic motion planning and control, providing a scalable solution for robotic systems operating in dynamic environments.