Route Planning for Autonomous Mobile Robots Using a Reinforcement Learning Algorithm

This research suggests a new robotic system technique that works specifically in settings such as hospitals or emergency situations when prompt action and preserving human life are crucial. Our framework largely focuses on the precise delivery of medical supplies medication inside defined area while avoiding robot collisions other obstacles. The suggested route planning algorithm (RPA) based reinforcement learning makes services effective by gathering sending data between robots healthcare professionals. In contrast, humans kept out patients’ field. Three key modules make up RPA: (i) Robot Finding Module (RFM), (ii) Charging (RCM), (iii) Route Selection (RSM). Using autonomous systems RPA places where there is need for essential, particularly field, which could reduce risk spreading viruses, save thousands lives. simulation results using proposed show flexible efficient movement compared to conventional methods under various environments. RSM contrasted with leading cutting-edge topology routing options. RSM’s primary benefit much-reduced calculations updating tables. contrast earlier algorithms, produces lower AQD. hence an appropriate real-time systems.