Transport route planning for AGVs under fire hazard based on LSTM and improved A* algorithm

Abstract

In response to the challenges of temperature and smoke sensor errors in AGV transportation routes under complex fire scenarios, aiming to reduce economic losses and minimize the risk of firefighting and rescue operations, this paper proposes a method for AGV transportation route planning under fire conditions based on LSTM and an improved A* algorithm. By utilizing LSTM to establish predictive models for temperature and smoke concentration in fire scenarios, this method addresses the issue of AGV sensor errors induced by high temperatures. The spread of fire model is employed to determine the radius of fire influence, serving as constraints for route planning. Subsequently, an enhanced A* algorithm is applied to plan routes, ensuring the safe transportation of AGVs in fire scenarios. Experimental results demonstrate that the proposed method can effectively predict fire conditions, plan safe routes, and enhance the safety of AGV transportation in practical applications.