Insects locate many resources important to survival by tracking along wind-borne odor plumes to their source. It is well known that plumes are patchy distributions of high concentration packets of odor interspersed with clean air, not smooth Gaussian distributions of odor intensity. This realization has been crucial to our understanding of plume-tracking behavior, because insect locomotory movements and sensory processing typically take place in the range of tens to hundreds of milliseconds, permitting them to respond to the rapid changes in odor concentration they experience in plumes. Because odor plumes are not comprised of smooth concentration gradients, they cannot provide the directional information necessary to allow plume-tracking insects to steer toward the source. Many experiments have shown that, in the species examined, successful source location requires two sensory inputs: the presence of the attractive odor and the detection of the direction of the wind bearing that odor. All plume-tracking insects use the wind direction as the primary directional cue that enables them to steer their movements toward the odor source. Experimental manipulations of the presence and absence of the odor, and the presence, absence, or direction of the wind during plume tracking, have begun to resolve the relationship between these two sensory inputs and how they shape the maneuvers we observe. Experiments, especially those undertaken in the natural wind and odor environments of the organisms in question and those directed at understanding the neural processing that underlie plume tracking, promise to enhance our understanding of this remarkable behavior.

Navigational Strategies Used by Insects to Find Distant, Wind-Borne Sources of Odor

Robotic odor localization has become a prominent research area in recent years. It promises many valuable practical applications, and contributes to the knowledge of biological odor localization, which has in many cases been the source of inspiration. There have been a diversity of approaches, implemented in both simulated and practical experiments, with a wide variety of platforms, and in a number of environments. This article presents a survey of the existing methods, which have been organized into taxonomic classifications. This provides a framework in which to evaluate the methods, view how they relate to each other, and make qualitative comparisons. The methods are grouped at the highest level by environmental conditions, and then by the localization method which in most cases is closely associated with the type of sensors used.

Robot Odor Localization: A Taxonomy and Survey

This paper presents a behavior-based adaptive mission planner (AMP)to trace a chemical plume to its source and reliably declare the source location. The proposed AMP is implemented on a REMUS autonomous underwater vehicle (AUV)equipped with multiple types of sensors that measure chemical concentration,the flow velocity vector, and AUV position, depth, altitude, attitude, and speed. This paper describes the methods and results from experiments conducted in November 2002 on San Clemente Island, CA, using a plume of Rhodamine dye developed in a turbulent fluid flow (i.e., near-shore ocean conditions). These experiments demonstrated chemical plume tracing over 100 m and source declaration accuracy relative to the nominal source location on the order of tens of meters. The designed maneuvers are divided into four behavior types: finding a plume,tracing the plume, reacquiring the plume, and declaring the source location. The tracing and reacquiring behaviors are inspired by male moths flying up wind along a pheromone plume to locate a sexually receptive female. All behaviors are formulated by perception and action modules and translated into chemical plume-tracing algorithms suitable for implementation on a REMUS AUV. To coordinate the different behaviors, the subsumption architecture is adopted to define and arbitrate the behavior priorities. AUVs capable of such feats would have applicability in searching for environmentally interesting phenomena, unexploded ordnance, undersea wreckage, and sources of hazardous chemicals or pollutants.

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Moth-inspired chemical plume tracing on an autonomous underwater vehicle

Olfactory-based mechanisms have been hypothesized for biological behaviors including foraging, mate-seeking, homing, and host-seeking. Autonomous underwater vehicles (AUVs) capable of such chemical plume tracing feats would have applicability in searching for environmentally interesting phenomena, unexploded ordinance, undersea wreckage, and sources of hazardous chemicals or pollutants. This article presents an approach and experimental results using a REMUS AUV to find a chemical plume, trace the chemical plume to its source, and maneuver to reliably declare the source location. The experimental results are performed using a plume of Rhodamine dye developed in a turbulent, near-shore, oceanic fluid flow.

Chemical plume tracing via an autonomous underwater vehicle

This paper addresses the problem of estimating a likelihood map for the location of the source of a chemical plume using an autonomous vehicle as a sensor probe in a fluid flow. The fluid flow is assumed to have a high Reynolds number. Therefore, the dispersion of the chemical is dominated by turbulence, resulting in an intermittent chemical signal. The vehicle is capable of detecting above-threshold chemical concentration and sensing the fluid flow velocity at the vehicle location. This paper reviews instances of biological plume tracing and reviews previous strategies for a vehicle-based plume tracing. The main contribution is a new source-likelihood mapping approach based on Bayesian inference methods. Using this Bayesian methodology, the source-likelihood map is propagated through time and updated in response to both detection and nondetection events. Examples are included that use data from in-water testing to compare the mapping approach derived herein with the map derived using a previously existing technique

Chemical Plume Source Localization

This article presents the theoretical motivation, implementation approach, and example validation results for a computationally efficient plume simulation model, designed to replicate both the short-term time signature and long-term exposure statistics of a chemical plume evolving in a turbulent flow. Within the resulting plume, the odor concentration is intermittent with rapidly changing spatial gradient. The model includes a wind field defined over the region of interest that is continuous, but which varies with location and time in both magnitude and direction. The plume shape takes a time varying sinuous form that is determined by the integrated effect of the wind field. Simulated and field data are compared. The motivation for the development of such a simulation model was the desire to evaluate various strategies for tracing odor plumes to their source, under identical conditions. The performance of such strategies depends in part on the instantaneous response of target receptors; therefore, the sequence of events is of considerable consequence and individual exemplar plume realizations are required. Due to the high number of required simulations, computational efficiency was critically important.

Xuezhu Long

Papers

Filament-based atmospheric dispersion model to achieve short time-scale structure of odor plumes