Autonomous Navigation with Route Opening Capability Based on Deep Reinforcement Learning by Material Recognition

In situations following catastrophic events such as fires and earthquakes, the deployment of autonomous robotic technology plays a crucial role in ensuring the success of exploration and assessment tasks. Despite significant progress in robot navigation, there remains a critical need for autonomous navigation systems adept at executing adaptive motion strategies, particularly in complex environments with obstacles of varying material properties. This study proposes an autonomous navigation system for disaster response scenarios, utilizing advanced deep reinforcement learning techniques. We develop a novel route opening policy that enhances the robot's ability to interact with and navigate around obstacles, thereby improving its route opening capabilities. Our method distinguishes between general navigation and collision-pushing scenarios to identify optimal routes. Experiments demonstrate the system's effectiveness in navigating and opening routes, as well as in locating victims, utilizing both the range and intensity data provided by Light Detection and Ranging (LiDAR) sensors.