ML-Based Fast and Precise Embedded Rack Detection Software for Docking and Transport of Autonomous Mobile Robots Using 2-D LiDAR

Abstract

Autonomous mobile robots (AMRs) are widely used in dynamic warehouse environments for automated material handling, which is one of the fundamental parts of building intelligent logistics systems. A target docking system to transport materials, such as racks, carts, and pallets is an important technology for AMRs that directly affects production efficiency. In this letter, we propose a fast and precise rack detection algorithm based on 2-D LiDAR data for AMRs that consume power from batteries. This novel detection method based on machine learning to quickly detect various racks in a dynamic environment consists of three modules: first classification, secondary classification, and multiple-matching-based 2-D point cloud registration. We conducted various experiments to verify the rack detection performance of the existing and proposed methods in a low-power embedded system. As a result, the relative pose accuracy is improved and the inference speed is increased by about 3 times, which shows that the proposed method has faster inference speed while reducing the relative pose error.