Multi-Robot Collaborative Source Searching Strategy in Large-Scale Chemical Clusters

In hazardous gas leakage accidents in chemical clusters, quickly and accurately locating the diffusion source via mobile sensors (searcher) can effectively speed up emergency response. While state-of-the-art researches designed kinds of autonomous sensing and searching techniques, e.g. cognitive-based strategies to complete the localization of the leakage source; the current studies cannot meet chemical cluster's requirements with complex road network constraints and spatial scales. To address this problem, we firstly combine a cognitive search strategy with great theoretical performance (Entrotaxis algorithm) with an intermittent search strategy and propose a fresh searching algorithm, namely Entrotaxis-Turn (ET). The hybrid algorithm can make use of the triggered turn motion to bypass obstacles to avoid falling into a loop, thereby improving the success rate and saving the searching time. Secondly, we extend the ET algorithm to the multi-robot collaboration pattern (Multi-ET algorithm), in which each mobile sensor shares the information collected along its route with each other. To reveal the performance of the proposed algorithms, a simulation scene is built in a typical chemical cluster scene and diffused gas is generated based on the advection-diffusion equation. Through Monte Carlo simulations, the optimal parameter combinations in the ET and Multi-ET algorithm are obtained. Finally, a verification experiment is carried out based on the simulation scene generated by the computational fluid dynamics (CFD) model. Results show that compared with the Entrotaxis algorithm, the efficiency and success rate of the ET algorithm are dramatically improved. Moreover, the Multi-ET algorithm obtains the optimal source searching performance.

Automated summary

This paper proposes a new search algorithm, ET, which combines cognitive search strategy with intermittent search strategy to address the complex road network and spatial scale requirements in chemical clusters. Through simulation and experimental validation, the results show that the ET algorithm significantly improves efficiency and success rate, and the Multi-ET algorithm achieves optimal source searching performance.