Positioning of autonomous mobile robot using multi-lateration with pattern recognition and differential evolution

The extensive adoption of Autonomous Mobile Robots (AMR) in manufacturing, processing, and intelligent logistics has witnessed a remarkable increase, driven by the rapid growth of smart manufacturing and Industry 4.0. AMRs serve a dual role, facilitating both product handling and transportation. The precision of AMR positioning is of paramount importance. The prevalent approach to indoor positioning and navigation involves the use of cameras, optical Light Detection and Ranging (LiDAR) sensors. However, relying solely on LiDAR-based motion estimation for relative positioning can result in gradual displacement errors, impacting accuracy. This paper introduces a dual-positioning strategy to address this challenge, incorporating secondary localization methods to ensure precise spatial confirmation and task execution for a High Payload Autonomous Mobile Robot (HAMR). This proposed method integrates a RGB-D camera with the HAMR's manipulator. It recognizes wall patterns (ArUco) and measures their distance from the HAMR, employing multi-lateration to calculate the HAMR's position within the real-world coordinate system. This paper presents an indoor positioning method for HAMRs using ArUco code, enabling multi-lateration measurements within a 15 mm error. Differential Evolution (DE) is employed for motion analysis to solve inverse kinematics, enabling dynamic analysis of HAMRs with redundant degrees of freedom. This technique effectively compensates for positioning errors, significantly enhancing the AMR's capabilities.