Design of an autonomous navigation system for emergency disposal robots at radioactive waste sites under extreme climatic conditions

Abstract

There are many factors of environmental hazards that endanger the lives of personnel at radioactive waste sites after sudden extreme weather disasters, and the use of robotic substitutes can effectively safeguard the lives of personnel. For the situation where the environmental information of radioactive waste sites is unknown and there are many variables, an autonomous navigation system of emergency disposal robots with strong resilience and stability is designed. First, an algorithm based on the Rao-Blackwellized particle filtering theory is used in the autonomous navigation system to achieve more accurate robot positioning and map building through four processes: prediction, correction, resampling, and map building. Second, the traditional path planning algorithm combined with road information is added to Hopfield neural network to achieve deep analysis of complex road conditions. Finally, the intelligent voice interaction function is added to the navigation system to realize the robot’s interaction and real-time monitoring and feedback. The experimental results show that the emergency disposal robot has high accuracy in positioning, navigation, obstacle avoidance, etc., can respond quickly in unexpected situations and can carry out effective voice interaction.