A novel plume tracking method in partial 3D diffusive environments using multi-sensor fusion

Abstract

In recent years, dangerous gas leakage has led to serious consequences in social security. Pervious strategies for locating the odor sources were carried out in 2D environments or the gas sensors were installed at a fixed height, ignoring the characteristics of plume distribution in 3D environments. In some cases, strategies developed in 2D environments may be completely ineffective when be applied to 3D environments. To enhance the success rate and rapidity, a novel method of odor source location in partial 3D diffusive environments based on multi-sensor fusion is proposed. Considering the suspected source existed and weak wind environments, machine olfactory and vision information are both employed to track the plumes and identify its source. To enhance the speed of plume searching and the reliability of plume tracking in the early stages, an autonomous mobile robot (AMR) simulates the social mechanism and hunting behaviors of the gray wolf population and tracks the plumes. A technical strategy is also designed to enable the AMR to complete the task of gas source location successfully and the subsumption architecture is adopted to define and arbitrate behavior priorities to coordinate different behaviors in this paper. The on-site test results show that the average positioning error is 0.13 m, the average running time is 147.7 s and the average distance traveled is 21.35 m. The results show that the proposed method is competent to accomplish the task of leakage gas source localization in partial 3D diffusive environments.