과수 환경 내 경로를 활용한 AMR의 안정적인 객체 추종 방법
Stable AMR Object Following Method in Orchard Pathways
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초록
Autonomous Mobile Robots (AMRs) are widely utilized in various fields, including logistics, industrial automation, and agriculture, for object-following tasks. However, conventional object-following methods often experience increased instability or difficulty in directional adjustments when operating in narrow pathways due to the movement of the target object. A path-based object-following method is proposed to improve stability and tracking performance. The approach utilizes a predefined path to maintain a consistent distance between the AMR and the target object while determining the robot’s position within the designated trajectory. By leveraging these predefined positions, the AMR can navigate stably within the path without unnecessary oscillations.Experimental results demonstrate that the proposed method enables stable object following even when the target deviates from the path. Compared to conventional methods, the approach significantly improves control stability and achieves approximately seven times more stable path adherence.
Abstract
Autonomous Mobile Robots (AMRs) are widely utilized in various fields, including logistics, industrial automation, and agriculture, for object-following tasks. However, conventional object-following methods often experience increased instability or difficulty in directional adjustments when operating in narrow pathways due to the movement of the target object. A path-based object-following method is proposed to improve stability and tracking performance. The approach utilizes a predefined path to maintain a consistent distance between the AMR and the target object while determining the robot’s position within the designated trajectory. By leveraging these predefined positions, the AMR can navigate stably within the path without unnecessary oscillations.Experimental results demonstrate that the proposed method enables stable object following even when the target deviates from the path. Compared to conventional methods, the approach significantly improves control stability and achieves approximately seven times more stable path adherence.