A Novel Latent-Representation-Based Algorithm with Dynamic Obstacle Avoidance (LADy) for Autonomous Mobile Robots (AMRs)

Conventional path-planning algorithms are often tailored to industrial and warehouse settings, creating the necessity to integrate two or more planners, which requires more memory and computation limitations. To overcome this limitation, this study aims to develop a novel algorithm, semantic cost encoding-based A* with Dynamic obstacle avoidance, specifically designed for Autonomous Mobile Robots (AMRs) in warehouses. In this study, the proposed algorithm is benchmarked in Matplotlib against A* and RRT* showing 60.33% higher memory efficiency and 60.36% more efficiency in aspect of number of computed nodes than RRT*, while all equivalent to A* in a static environment, and benchmarked against D* lite for dynamic environment, as well as against a hybrid algorithm, a simplified interpretation of commercial AMR path planning approaches, to show 45.30% more memory-efficiency than the hybrid algorithm, which is much more preferred in a real time implementation than D* lite, for AMRs.