Utilizing a Rapidly Exploring Random Tree for Hazardous Gas Exploration in a Large Unknown Area

The use of robotics olfaction for gas source localization or mapping has become a concern given the issues of terrorism or industrial accidents that may cause damage to the environment. A typical scenario is to send a robot to a place where a dangerous gas leak has just occurred. The robot’s task is to map gas concentrations in the region of interest as effectively as possible. This paper addresses how the robot performs gas exploration in a large and unknown environment. One of the issues that needs to be addressed is the fact that the computation time of the path planning, frontier detection, goal decision making and gas distribution mapping is slower if all cells in the occupancy grid map are involved in a large environment. Consequently, the Rapidly-exploring Random Tree (RRT) algorithm is chosen as the main algorithm. The RRT graph guides the robot’s navigation, utilizes the vertices as goal candidates, gas mean and variance value, and searches for a new frontier. A new strategy is proposed to address the frontier exploration and gas exploitation trade-off. Finally, a Robot Operating System (ROS), Gazebo, and a 3D gas simulator are used to compare the proposed strategy performance with the others in a large outdoor environment.