PreprintArticleVersion 1This version is not peer-reviewed
Development and Enhancement of Autonomous Mobile Robots Using Reinforcement Learning: Improving Navigation and Obstacle Avoidance in Small-Scale Industrial Settings
Version 1
: Received: 23 August 2024 / Approved: 26 August 2024 / Online: 26 August 2024 (17:06:03 CEST)
How to cite:
Sharmilan, T.; Abeysekara, N. Development and Enhancement of Autonomous Mobile Robots Using Reinforcement Learning: Improving Navigation and Obstacle Avoidance in Small-Scale Industrial Settings. Preprints2024, 2024081849. https://doi.org/10.20944/preprints202408.1849.v1
Sharmilan, T.; Abeysekara, N. Development and Enhancement of Autonomous Mobile Robots Using Reinforcement Learning: Improving Navigation and Obstacle Avoidance in Small-Scale Industrial Settings. Preprints 2024, 2024081849. https://doi.org/10.20944/preprints202408.1849.v1
Sharmilan, T.; Abeysekara, N. Development and Enhancement of Autonomous Mobile Robots Using Reinforcement Learning: Improving Navigation and Obstacle Avoidance in Small-Scale Industrial Settings. Preprints2024, 2024081849. https://doi.org/10.20944/preprints202408.1849.v1
APA Style
Sharmilan, T., & Abeysekara, N. (2024). Development and Enhancement of Autonomous Mobile Robots Using Reinforcement Learning: Improving Navigation and Obstacle Avoidance in Small-Scale Industrial Settings. Preprints. https://doi.org/10.20944/preprints202408.1849.v1
Chicago/Turabian Style
Sharmilan, T. and Nadeesha Abeysekara. 2024 "Development and Enhancement of Autonomous Mobile Robots Using Reinforcement Learning: Improving Navigation and Obstacle Avoidance in Small-Scale Industrial Settings" Preprints. https://doi.org/10.20944/preprints202408.1849.v1
Abstract
The rapid development and integration of Autonomous Mobile Robots (AMRs) have 1 revolutionized industries by enhancing automation capabilities. A critical challenge in this evolution 2 is achieving effective navigation and obstacle avoidance, which are essential for deploying AMRs 3 seamlessly in varied environments. This paper presents a detailed exploration of AMR navigation 4 and obstacle avoidance advancements through the application of reinforcement learning, specifically 5 focusing on small-scale Sri Lankan manufacturing facilities. The study demonstrates the effectiveness 6 of Q-learning in managing dynamic obstacles within a factory environment. The AMR successfully 7 avoided obstacles in 36 out of 50 test runs, achieving a 72% success rate, and maintained an average 8 distance of 12 cm from each obstacle, underscoring the algorithm’s precision in maintaining safe nav- 9 igation paths while dynamically adapting to environmental changes. The continuous monitoring by 10 ultrasonic sensors, combined with iterative learning, enabled the robot to refine its decision-making 11 process and efficiently navigate through the environment. This paper also provides a comprehensive 12 examination of conventional methods, tracing their historical development and assessing their role in 13 addressing real-world challenges. The results highlight the significant improvements brought by rein- 14 forcement learning, particularly when integrated with sensor fusion and motor control technologies, 15 to enhance navigation and dynamic object collision avoidance. The study involves developing an 16 AMR prototype, refining algorithms, and assessing system performance in controlled environments. 17 By automating material transportation and addressing operational constraints in manufacturing 18 settings that predominantly rely on manual labor, this paper advances the practical deployment of 19 AMRs. Additionally, it discusses ethical considerations, potential limitations, and the importance 20 of real-world validation, offering valuable insights for the future development and integration of 21 autonomous mobile robotics in dynamic industrial environments.
Engineering, Industrial and Manufacturing Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
