Action-limited, multimodal deep Q learning for AGV fleet route planning
AGV system for advanced manufacturing
AGVs (automated guided vehicles) have been a staple in material handling industries such as manufacturing and warehousing where they can increase productivity, reduce labor costs, and improve safety. Customer demand for faster delivery and lower costs is driving a transformation in these industries for higher efficiency and throughput. One of the most important technologies for AGV systems is unmanned navigation technology, which allows such mobile robots to achieve incredible levels of performance [1]. Choosing the right vehicle navigation technology is critical since it will affect the performance of the whole AGV fleet.
