Integrating LiDAR-Based Collision Avoidance on AMR-T for Advancing Operational Safety

Automatic Mobile Robot Transporter (AMR-T) represents a vital component in modern factory operations, offering enhanced efficiency and productivity. However, the deployment of AMR-T systems necessitates robust protection mechanisms to ensure safe navigation within dynamic environments. This study addresses the development of a protection system employing LiDAR sensors and fuzzy logic to enable obstacle detection and avoidance for AMR-T. LiDAR sensors are utilized to gather spatial data, enabling real-time analysis of the surrounding environment. Leveraging fuzzy logic, the system autonomously generates intelligent navigation decisions amidst uncertain or ambiguous conditions. The project encompasses comprehensive stages, including requirements analysis, system design, implementation, and rigorous testing. Key considerations such as system reliability, response, and detection accuracy are paramount throughout the development process. To maximize system performance, secondary sensors including inductive proximity sensors and limit switches were added. Results show high accuracy with an average error of 0.56%, effectiveness of inductive proximity sensors up to 14 mm, and reliability of limit switches for operational safety. The fuzzy logic approach successfully interprets collision threats and generates appropriate avoidance actions. This system enhances operational efficiency and safety, reduces the risk of product damage, and advances automation technology in the manufacturing industry. The anticipated outcomes aim to elevate operational efficiency and safety within factory settings, while also contributing to the advancement of automation technology across the manufacturing industry.