A Priority-Based Multi-Robot Search Algorithm for Indoor Source Searching

Abstract

It is extremely important to quickly locate the source of a hazardous substance leak in order to reduce damage to life and property. Multi-robot source localization faces challenges in unknown indoor environments, such as navigating through dense environments, encountering large areas without airflow or concentration clues, experiencing frequent changes in robot measurements, and managing clusters of robots in confined spaces. This study proposes a priority-based multi-robot search algorithm to tackle these challenges. The algorithm consists of a priority-based search strategy, an exploration method based on frontier and Voronoi diagram, an airflow tracking method based on Rapidly-exploring Random Trees Star (RRT*), and a multi-robot collaborative method. The algorithm was compared with three other state-of-the-art algorithms in simulated environments, assessing varying team sizes, airflow speeds, and diverse scenarios. The algorithm was also evaluated in real-robots experiments. The evaluation results demonstrate that the algorithm exhibits outstanding performance in both simulated and real-robots experiments. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i>—The aim of this study is to propose a multi-robot search algorithm designed to address the challenges encountered in source searching within unknown indoor environments. These challenges include navigating through dense environments, large areas without airflow and concentration clues, frequent changes in robot measurements, and the clusters of robots in confined spaces. This study proposes a priority-based multi-robot search algorithm. The core idea of the algorithm is to enable robots to adopt different search methods depending on their measurements. When lacking valid airflow and concentration measurements, robots engage in spatial exploration using an exploration method based on frontier and Voronoi diagram to increase the likelihood of encountering the plume. When robots are within a plume, they rely on an RRT*-based airflow tracking method to move towards the source. The RRT* also provides robots with reachable navigation goals in environments with dense obstacles. The multi-robot collaborative method operates based on the priority levels assigned to the robots. On one hand, it directs robots towards the global best to reduce search time. On the other hand, robots ignore global bests that are not of a higher priority than their own, preventing the clustering of multiple robots at the same location. The algorithm was compared with three other state-of-the-art algorithms in simulated environments. It was also evaluated in real-robots experiments. The evaluation results show that the algorithm exhibits outstanding performance in both simulated and real-robots experiments. To further aid research in this field, a dataset for multi-robot indoor source searching has been created and made available online to provide a benchmark for related research.