Reinforcement Learning-Based Embedded Control System for An Autonomous Material Handling Robot

Abstract

This work introduces a design and simulation of an autonomous mobile robot with a self-learning control system by incorporating reinforcement learning for real-time optimization of adaptability in its performance. The use of Raspberry Pi with an attached camera module allows the robot to perform object detection for identification and executing automatic pick-and-place actions. Ultrasonic sensors can support navigation as well as obstacle detection, while the gripper mechanism will aid fine material handling. As such, the system is capable of application in loading and unloading as well as transport. With the RL framework, the robot can optimize its real-time activities with a reward-penalty system to perfect its operational behavior for efficient task execution. The L293D motor driver is used to control the robot's movement accurately, giving stability and precision in handling. Various material handling scenarios are tested by simulation to validate the performance of the control system and to check whether the robot can autonomously execute its tasks. This phase of simulation is aimed at proving that the proposed system will be effective in material handling as it lays the groundwork for hardware implementation with eventual real-world use in industrial settings.