Low-to-High Resolution Path Planner for Robotic Gas Distribution Mapping

Robotic gas distribution mapping improves the understanding of a hazardous gas dispersion while putting the human operator out of danger. Generating an accurate gas distribution map quickly is of utmost importance in situations such as gas leaks and industrial incidents, so that the efficient use of resources in response to incidents can be facilitated. In this paper, to incorporate the operational requirement on map granularity, we propose a low-to-high resolution path planner that first guides a single robots to quickly and sparsely sample the region of interest to generate a low resolution gas distribution map, followed by high resolution sampling informed by the low resolution map as a prior. The low resolution prior acts as a coverage survey allowing the algorithm to perform a relatively exploitative search of high concentration regions, resulting in overall shorter mission times. The proposed framework is designed to iteratively identify the next best T locations to sample, which prioritises the potentially high reward locations, while ensuring that the robot can travel to and sample the chosen locations within a user specified map update cycle. We present a simulation study to demonstrate the alternating exploration-exploitation like behaviour along with bench-marking its performance in contrast to the traditional sampling path planners and various reward functions.