안전지표 기반 물류 작업자 사고 예방을 위한 자율주행 물류로봇 위험 회피 알고리즘 개발

초록

The advent of artificial intelligence has enabled the integration of robotic technologies into logistics operations. However, conventional autonomous mobile robots present safety challenges, particularly regarding the risk of collisions with workers. To address these concerns, Autonomous Mobility Robots (AMRs) have been introduced, equipped with sensors to detect potential collisions and reroute to avoid them. Demonstrating AMR effectiveness without impeding worker movements is essential for broader adoption in logistics. This study developed a collision avoidance algorithm to analyze the impact of Autonomous Mobility Robots in logistics (AML) using a traffic simulation environment. Efficiency and safety indicators were presented and compared based on logistics site size. The AMLs operated according to the algorithm, designed to prevent collisions by adjusting the number of robots and operating hours to optimize efficiency. Priorities were determined based on worker locations and AML status. The study identified scenarios with the best safety and operational performance for each site size, proposing a scheduling-based operational strategy for AML deployment. Findings indicated that the collision avoidance algorithm reduced conflicts and delays, enhancing both safety and operational performance. Additionally, the scheduling-based strategy demonstrated its efficacy in maintaining stable and efficient operations as site size increased, and more charging stations were introduced. By refining these scenarios and applying a scheduling strategy based on real-world data, a tailored operational plan can be developed for specific logistics environments. This approach is expected to prevent accidents involving workers and improve overall efficiency, contributing to the creation of safer logistics sites.

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