Development and Enhancement of Autonomous Mobile Robots Using Reinforcement Learning: Improving Navigation and Obstacle Avoidance in Small-Scale Industrial Settings
Keywords:
Automation, Dynamic Object Collision Avoidance, Reinforcement Learning, Robotics, Small-Scale ManufacturingAbstract
The rapid development and integration of Autonomous Mobile Robots (AMRs) have revolutionised industries by enhancing automation capabilities. A critical challenge in this evolution is achieving effective navigation and obstacle avoidance, essential for deploying AMRs seamlessly in varied environments. This paper presents a detailed exploration of AMR navigation and obstacle avoidance advancements through the application of reinforcement learning, specifically focusing on small-scale Sri Lankan manufacturing facilities. The study demonstrates the effectiveness of Q-learning in managing dynamic obstacles within a factory environment. The AMR avoided obstacles in 36 out of 50 test runs, achieving a 72\% success rate, and maintained an average distance of 12 cm from each obstacle, underscoring the algorithm's precision in maintaining safe navigation paths while dynamically adapting to environmental changes. The continuous monitoring by ultrasonic sensors, combined with iterative learning, enabled the robot to refine its decision-making process and efficiently navigate through the environment. This paper also provides a comprehensive examination of conventional methods, tracing their historical development and assessing their role in addressing real-world challenges. The results highlight the significant improvements brought by reinforcement learning, particularly when integrated with sensor fusion and motor control technologies, to enhance navigation and dynamic object collision avoidance.
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