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.
Highlights
There has been a growing interest to apply a robotbased chemical plume tracer in homeland security and environmental monitoring
× (i = 1, 2, . . . , Nall) from the priority queue, where a superscript f indicates that the last chemical detection points (LCDPs) are sorted in the order of the most recent up flow direction and Nall is the total number of LCDPs detected during a chemical plume tracing (CPT) mission
We abstract the two source identification zones (SIZs) source identification algorithms from the moth-inspired plume tracing strategies reported by Li et al (2001) and use Monte
Summary
There has been a growing interest to apply a robotbased chemical plume tracer in homeland security and environmental monitoring. This paper systemically discusses a process of designing source identification algorithms, which are abstracted from the moth-inspired plume tracing strategies based on a single chemical sensor (Li et al 2001). We introduce the concept of the last chemical detection points (LCDPs) to construct source identification zones (SIZs) (Li 2006) and use chemical detection events to develop two variations of SIZ algorithms for the source identification along with the measured tracer locations and fluid flow directions. The first algorithm, which we call SIZ T, maintains the constant number of the most recent LCDPs in a priority queue in the time sequence. We adopt Monte Carlo methods to optimise the algorithms using a simulated plume with significant filament intermittency and meander (Farrell et al 2002), and evaluate the performance of source identification manoeuvres in three aspects: reliability, identification time, and accuracy.
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