Optimized Global Path Planning with SLAM for Efficient Warehouse Autonomous Mobile Robot

Abstract

Current Automated Guided Vehicle (AGV) systems in warehouses often lack flexibility in navigating around obstacles due to limitation of line and marker-based navigation methods. This study presents an intelligent navigation system for an autonomous mobile robot (AMR) designed to improve efficiency of item retrieval in warehouse environment. The proposed system employs a Simultaneous Localization and Mapping (SLAM) algorithm to enable real-time mapping, obstacle avoidance, and efficient path generation. The global planner is a critical component in the navigation framework, as it determines the optimal and collision-free route through the warehouse, providing an efficient high-level path for the local planner to refine during real time navigation. Extensive evaluations were conducted to determine the optimal combination of global path planners between 1) RRT and Hybrid A* with local planners DWA and 2) TEB for path optimization, based on minimizing path length and travel time to the goal. Experimental results showed that the Hybrid A* with TEB planner achieved the fastest navigation time of 1.25 minutes with a 90% success rate, outperforming the RRT with DWA combination in both speed and reliability. This advancement offers significant potential to enhance warehouse operations by enabling fully autonomous mobile robots capable of performing item retrieval without human intervention.