Affiliations: Department of Computer Science, California State University, Bakersfield, CA US | Robotics State Key Laboratory, Shenyang Institute of Automation, Chinese Academy of Sciences Shenyang, Liaoning China
Abstract: This paper presents algorithms for identifying the odour source of a chemical plume with significant filament intermittency and meander developed in fluid-advected environments. The algorithms are abstracted from moth-inspired chemical plume tracing strategies in two steps. First, we introduce the concept of the last chemical detection points that leads to construction of a source identification zone and development of two variations in the source identification algorithms. Second, we use Monte Carlo methods to optimise the proposed algorithms in a simulated environment. The evaluation results demonstrate that the optimised algorithm achieves a success rate of over 90% in identifying the source location, the average identification time is 3–4 min and the average error is 1–2 m surrounding the source location.