Odor Plume Research Articles

Temporal novelty detection and multiple timescale integration drive Drosophila orientation dynamics in temporally diverse olfactory environments.

To survive, insects must effectively navigate odor plumes to their source. In natural plumes, turbulent winds break up smooth odor regions into disconnected patches, so navigators encounter brief bursts of odor interrupted by bouts of clean air. The timing of these encounters plays a critical role in navigation, determining the direction, rate, and magnitude of insects' orientation and speed dynamics. Disambiguating the specific role of odor timing from other cues, such as spatial structure, is challenging due to natural correlations between plumes' temporal and spatial features. Here, we use optogenetics to isolate temporal features of odor signals, examining how the frequency and duration of odor encounters shape the navigational decisions of freely-walking Drosophila. We find that fly angular velocity depends on signal frequency and intermittency-the fraction of time signal can be detected-but not directly on durations. Rather than switching strategies when signal statistics change, flies smoothly transition between signal regimes, by combining an odor offset response with a frequency-dependent novelty-like response. In the latter, flies are more likely to turn in response to each odor hit only when the hits are sparse. Finally, the upwind bias of individual turns relies on a filtering scheme with two distinct timescales, allowing rapid and sustained responses in a variety of signal statistics. A quantitative model incorporating these ingredients recapitulates fly orientation dynamics across a wide range of environments and shows that temporal novelty detection, when combined with odor motion detection, enhances odor plume navigation.

Near-surface wind variability over spatiotemporal scales relevant to plume tracking insects.

Odor plume tracking is important for many organisms, and flying insects have served as popular model systems for studying this behavior both in field and laboratory settings. The shape and statistics of the airborne odor plumes that insects follow are largely governed by the wind that advects them. Prior atmospheric studies have investigated aspects of microscale wind patterns with an emphasis on characterizing pollution dispersion, enhancing weather prediction models, and for assessing wind energy potential. Here, we aim to characterize microscale wind dynamics through the lens of short-term ecological functions by focusing on spatial and temporal scales most relevant to insects actively searching for odor sources. We collected and compared near-surface wind data across three distinct environments (sage steppe, forest, and urban) in Northern Nevada. Our findings show that near-surface wind direction variability decreases with increasing wind speeds and increases in environments with greater surface complexity. Across environments, there is a strong correlation between the variability in the wind speed (i.e., turbulence intensity) and wind direction (i.e., standard deviation in wind direction). In some environments, the standard deviation in the wind direction varied as much as 15°-75° on time scales of 1-10 min. We draw insight between our findings and previous plume tracking experiments to provide a general intuition for future field research and guidance for wind tunnel design. Our analysis suggests a hypothesis that there may be an ideal range of wind speeds and environment complexity in which insects will be most successful when tracking odor plumes over long distances.

Odour Emissions and Dispersion from Digestate Spreading

Odour emissions from digestate applied on 21 October 2020 in a 2.4 ha field in the Po Valley (Casalino, 28060, Novara, Italy) were measured using dynamic olfactometry and a six-specialist odour panel, and two application techniques were compared. The measured odour emissions were 3024 and 1286 ou m−2 h−1, corresponding to the digestate application with surface spreading and direct injection, respectively. The odour dispersion for the different emission values was modeled to a distance of approx. 500 m from the center of the field and 15 m from the ground using a Lagrangian puff model (SCICHEM) in different meteorological conditions. The meteorological variables were measured at the closest station during the whole month in which the digestate application took place, mimicking a “worst-case scenario” characterized by the frequent applications along the considered period. The maximum odour concentrations within one square km area from the center of the field occurred in calm wind and stable atmospheric conditions. This study also evaluated the effect of a barrier downwind from the source. In the worst-case scenario (spreading technique with maximum emissions, no barriers), the average and maximum estimated odour concentrations were 3.2 and 18.9 ou m−3, respectively. The calculated probabilities of exceeding the threshold value of 1 ou m−3 were 36% and 47% for the whole period and the episodes of calm winds, respectively, and 14% on average for the episode of maximum wind gust. In the best emission scenario (direct injection), the average and maximum odour concentrations were 1.5 and 8.6 ou m−3, respectively, while the probabilities of exceeding 1 ou m−3 were 26% and 36% for the whole period and the episodes of calm winds, respectively, and 0.016% for the maximum wind gust episode. In the presence of a solid barrier downwind from the source and for the wind gust episode, the peak values of the concentrations and exceedance probabilities at the sampling height were found to be reduced by a factor close to 2.5 and 5 × 105, respectively. The study also evaluated the concentration field’s vertical distribution, showing that the odour plume’s vertical and horizontal dispersion slightly increased with the barrier. This is not a cause of concern unless the emitted substances causing odour nuisance are also atmospheric pollutants with potential harm to far-field ecosystems and human settlements at low concentration levels.

Active Sensing in Bees Through Antennal Movements Is Independent of Odor Molecule.

When sampling odors, many insects are moving their antennae in a complex but repeatable fashion. Previous studies with bees have tracked antennal movements in only two dimensions, with a low sampling rate and with relatively few odorants. A detailed characterization of the multimodal antennal movement patterns as function of olfactory stimuli is thus wanted. The aim of this study is to test for a relationship between the scanning movements and the properties of the odor molecule. We tracked several key locations on the antennae of bumblebees at high frequency and in three dimensions while stimulating the insect with puffs of 11 common odorants released in a low-speed continuous flow. Water and paraffin were used as negative controls. Movement analysis was done with the neural network Deeplabcut. Bees use a stereotypical oscillating motion of their antennae when smelling odors, similar across all bees, independently of the identity of the odors and hence their diffusivity and vapor pressure. The variability in the movement amplitude among odors is as large as between individuals. The main type of oscillation at low frequencies and large amplitude is triggered by the presence of an odor and is in line with previous work, as is the speed of movement. The second oscillation mode at higher frequencies and smaller amplitudes is constantly present. Antennae are quickly deployed when a stimulus is perceived, decorrelate their movement trajectories rapidly, and oscillate vertically with a large amplitude and laterally with a smaller one. The cone of airspace thus sampled was identified through the 3D understanding of the motion patterns. The amplitude and speed of antennal scanning movements seem to be function of the internal state of the animal, rather than determined by the odorant. Still, bees display an active olfactory sampling strategy. First, they deploy their antennae when perceiving an odor. Second, fast vertical scanning movements further increase the odorant capture rate. Finally, lateral movements might enhance the likelihood to locate the source of odor, similarly to the lateral scanning movement of insects at odor plume boundaries.

Dehydrated Drosophila melanogaster track a water plume in tethered flight

Perception of sensory stimuli can be modulated by changes in internal state to drive contextually appropriate behavior. For example, dehydration is a threat to terrestrial animals, especially to Drosophila melanogaster due to their large surface area to volume ratio, particularly under the energy demands of flight. While hydrated D.melanogaster avoid water cues, while walking, dehydration leads to water-seeking behavior. We show that in tethered flight, hydrated flies ignore a water stimulus, whereas dehydrated flies track a water plume. Antennal occlusions eliminate odor and water plume tracking, whereas inactivation of moist sensing neurons in the antennae disrupts water tracking only upon starvation and dehydration. Elimination of the olfactory coreceptor eradicates odor tracking while leaving water-seeking behavior intact in dehydrated flies. Our results suggest that while similar hygrosensory receptors may be used for walking and in-flight hygrotaxis, the temporal dynamics of modulating the perception of water vary with behavioral state.

Olfactory receptor neurons generate multiple response motifs, increasing coding space dimensionality.

Odorants binding to olfactory receptor neurons (ORNs) trigger bursts of action potentials, providing the brain with its only experience of the olfactory environment. Our recordings made in vivo from locust ORNs showed that odor-elicited firing patterns comprise four distinct response motifs, each defined by a reliable temporal profile. Different odorants could elicit different response motifs from a given ORN, a property we term motif switching. Further, each motif undergoes its own form of sensory adaptation when activated by repeated plume-like odor pulses. A computational model constrained by our recordings revealed that organizing responses into multiple motifs provides substantial benefits for classifying odors and processing complex odor plumes: each motif contributes uniquely to encode the plume's composition and structure. Multiple motifs and motif switching further improve odor classification by expanding coding dimensionality. Our model demonstrated that these response features could provide benefits for olfactory navigation, including determining the distance to an odor source.

Robust Moth-Inspired Algorithm for Odor Source Localization Using Multimodal Information.

Odor-source localization, by which one finds the source of an odor by detecting the odor itself, is an important ability to possess in order to search for leaking gases, explosives, and disaster survivors. Although many animals possess this ability, research on implementing olfaction in robotics is still developing. We developed a novel algorithm that enables a robot to localize an odor source indoors and outdoors by taking inspiration from the adult male silk moth, which we used as the target organism. We measured the female-localization behavior of the silk moth by using a virtual reality (VR) system to obtain the relationship between multiple sensory stimuli and behavior during the localization behavior. The results showed that there were two types of search active and inactive depending on the direction of odor and wind detection. In an active search, the silk moth moved faster as the odor-detection frequency increased, whereas in the inactive search, they always moved slower under all odor-detection frequencies. This phenomenon was constructed as a robust moth-inspired (RMI) algorithm and implemented on a ground-running robot. Experiments on odor-source localization in three environments with different degrees of environmental complexity showed that the RMI algorithm has the best localization performance among conventional moth-inspired algorithms. Analysis of the trajectories showed that the robot could move smoothly through the odor plume even when the environment became more complex. This indicates that switching and modulating behavior based on the direction of odor and wind detection contributes to the adaptability and robustness of odor-source localization.

Odor Source Localization in Obstacle Regions Using Switching Planning Algorithms with a Switching Framework.

Odor source localization (OSL) robots are essential for safety and rescue teams to overcome the problem of human exposure to hazardous chemical plumes. However, owing to the complicated geometry of environments, it is almost impossible to construct the dispersion model of the odor plume in practical situations to be used for probabilistic odor source search algorithms. Additionally, as time is crucial in OSL tasks, dynamically modifying the robot's balance of emphasis between exploration and exploitation is desired. In this study, we addressed both the aforementioned problems by simplifying the environment with an obstacle region into multiple sub-environments with different resolutions. Subsequently, a framework was introduced to switch between the Infotaxis and Dijkstra algorithms to navigate the agent and enable it to reach the source swiftly. One algorithm was used to guide the agent in searching for clues about the source location, whereas the other facilitated the active movement of the agent between sub-environments. The proposed algorithm exhibited improvements in terms of success rate and search time. Furthermore, the implementation of the proposed framework on an autonomous mobile robot verified its effectiveness. Improvements were observed in our experiments with a robot when the success rate increased 3.5 times and the average moving steps of the robot were reduced by nearly 35%.

A comparison of odor plume-tracking behavior of walking and flying insects in different turbulent environments.

Many animals locate food, mates and territories by following plumes of attractive odors. There are clear differences in the structure of this plume-tracking behavior depending on whether an animal is flying, swimming, walking or crawling. These differences could arise from different control rules used by the central nervous system during these different modes of locomotion or one set of rules interacting with the different environments while walking on the surface versus flying or swimming. Flow speeds and turbulence that characterize the environments where walking and flying insects track plumes may alter the structure of odor plumes in an environment-specific way that results in the same control rules generating behaviors that appear quite different. We tested these ideas by challenging walking male cockroaches, Periplaneta americana, and flying male moths, Manduca sexta, to track plumes of their species' sex pheromones in low wind speeds characteristic of cockroach experimental environments, higher wind speeds characteristic of moth experimental environments, and conditions ranging from low to high turbulence. Introducing a turbulence-generating structure into the flow significantly improved the flying plume tracker's ability to locate the odor source, and changed the structure of the behavior of both flying and walking plume trackers. Our results support the idea that plume trackers moving slowly along the substrate may use the spatial distribution of odor, while faster moving flying plume trackers may use the timing of odor encounters to steer to locate the source.

Robot odor source localization in indoor environments based on gradient adaptive extremum seeking search

When toxic or harmful chemicals or pollutants leak, it is of great significance to determine the leakage source(s) in a timely and autonomous manner to reduce casualties and property losses. This study focuses on the problem of robot-based odor source localization (OSL) in unknown indoor environments from a control perspective. A model-free gradient adaptive extremum seeking control (GA-ESC) algorithm was proposed to improve the searching efficiency and success rate of OSL. The GA-ESC algorithm plans the OSL through a controller, which estimates the gradient of the odor plume concentration, and guides the robot to approach the odor source according to the estimated gradient. A novel three-point gradient estimation method was proposed to obtain a more stable odor field gradient based on two historical points and one current point, where an adaptive feedback gain was applied to link the estimated gradient and the output control quantities. Subsequently, the perturbation amplitude adjustment (PAA) strategy was introduced to enhance the global searching ability. When compared to the traditional extremum seeking control (ESC), our algorithm outperforms it in terms of success rate and average searching time. Moreover, the performance of the proposed algorithm was validated through simulations by utilizing three different dispersion models. The real-robot experiments were also carried out in indoor environments. The results demonstrate the significance of the proposed control-based OSL algorithm in unknown indoor environments.

Emergent behaviour and neural dynamics in artificial agents tracking odour plumes

Tracking an odour plume to locate its source under variable wind and plume statistics is a complex task. Flying insects routinely accomplish such tracking, often over long distances, in pursuit of food or mates. Several aspects of this remarkable behaviour and its underlying neural circuitry have been studied experimentally. Here we take a complementary in silico approach to develop an integrated understanding of their behaviour and neural computations. Specifically, we train artificial recurrent neural network agents using deep reinforcement learning to locate the source of simulated odour plumes that mimic features of plumes in a turbulent flow. Interestingly, the agents’ emergent behaviours resemble those of flying insects, and the recurrent neural networks learn to compute task-relevant variables with distinct dynamic structures in population activity. Our analyses put forward a testable behavioural hypothesis for tracking plumes in changing wind direction, and we provide key intuitions for memory requirements and neural dynamics in odour plume tracking.

Odour motion sensing enhances navigation of complex plumes.

Odour plumes in the wild are spatially complex and rapidly fluctuating structures carried by turbulent airflows1-4. To successfully navigate plumes in search of food and mates, insects must extract and integrate multiple features of the odour signal, including odour identity5, intensity6 and timing6-12. Effective navigation requires balancing these multiple streams of olfactory information and integrating them with other sensory inputs, including mechanosensory and visual cues9,12,13. Studies dating back a century have indicated that, of these many sensory inputs, the wind provides the main directional cue in turbulent plumes, leading to the longstanding model of insect odour navigation as odour-elicited upwind motion6,8-12,14,15. Here we show that Drosophila melanogaster shape their navigational decisions using an additional directional cue-the direction of motion of odours-which they detect using temporal correlations in the odour signal between their two antennae. Using a high-resolution virtual-reality paradigm to deliver spatiotemporally complex fictive odours to freely walking flies, we demonstrate that such odour-direction sensing involves algorithms analogous to those in visual-direction sensing16. Combining simulations, theory and experiments, we show that odour motion contains valuable directional information that is absent from the airflow alone, and that both Drosophila and virtual agents are aided by that information in navigating naturalistic plumes. The generality of our findings suggests that odour-direction sensing may exist throughout the animal kingdom and could improve olfactory robot navigation in uncertain environments.

Experimental and theoretical probe on mechano- and chemosensory integration in the insect antennal lobe.

In nature, olfactory signals are delivered to detectors-for example, insect antennae-by means of turbulent air, which exerts concurrent chemical and mechanical stimulation on the detectors. The antennal lobe, which is traditionally viewed as a chemosensory module, sits downstream of antennal inputs. We review experimental evidence showing that, in addition to being a chemosensory structure, antennal lobe neurons also respond to mechanosensory input in the form of wind speed. Benchmarked with empirical data, we constructed a dynamical model to simulate bimodal integration in the antennal lobe, with model dynamics yielding insights such as a positive correlation between the strength of mechanical input and the capacity to follow high frequency odor pulses, an important task in tracking odor sources. Furthermore, we combine experimental and theoretical results to develop a conceptual framework for viewing the functional significance of sensory integration within the antennal lobe. We formulate the testable hypothesis that the antennal lobe alternates between two distinct dynamical regimes, one which benefits odor plume tracking and one which promotes odor discrimination. We postulate that the strength of mechanical input, which correlates with behavioral contexts such being mid-flight versus hovering near a flower, triggers the transition from one regime to the other.

A Neural Model for Insect Steering Applied to Olfaction and Path Integration.

Many animal behaviors require orientation and steering with respect to the environment. For insects, a key brain area involved in spatial orientation and navigation is the central complex. Activity in this neural circuit has been shown to track the insect's current heading relative to its environment and has also been proposed to be the substrate of path integration. However, it remains unclear how the output of the central complex is integrated into motor commands. Central complex output neurons project to the lateral accessory lobes (LAL), from which descending neurons project to thoracic motor centers. Here, we present a computational model of a simple neural network that has been described anatomically and physiologically in the LALs of male silkworm moths, in the context of odor-mediated steering. We present and analyze two versions of this network, one rate based and one based on spiking neurons. The modeled network consists of an inhibitory local interneuron and a bistable descending neuron (flip-flop) that both receive input in the LAL. The flip-flop neuron projects onto neck motor neurons to induce steering. We show that this simple computational model not only replicates the basic parameters of male silkworm moth behavior in a simulated odor plume but can also take input from a computational model of path integration in the central complex and use it to steer back to a point of origin. Furthermore, we find that increasing the level of detail within the model improves the realism of the model's behavior, leading to the emergence of looping behavior as an orientation strategy. Our results suggest that descending neurons originating in the LALs, such as flip-flop neurons, are sufficient to mediate multiple steering behaviors. This study is therefore a first step to close the gap between orientation circuits in the central complex and downstream motor centers.

Characterization of odorous industrial plumes by coupling fast and slow mass spectrometry techniques for volatile organic compounds.

This study aimed to develop a technique to chemically characterize odor issues in neighborhoods of designated industrial zones with pronounced emissions of volatile organic compounds (VOCs). Due to the elusive nature of odor plumes, speedy detection with sufficient sensitivity is required to capture the plumes. In this demonstration, proton-transfer-reaction mass spectrometry (PTR-MS) was used as the front-line detection tool in an industrial zone to guide sampling canisters for in-laboratory analysis of 106 VOCs by gas chromatography-mass spectrometry/flame ionization detector (GC-MS/FID). The fast but less accurate PTR-MS coupled with the slow but accurate GC-MS/FID method effectively eliminates the drawbacks of each instrument and fortifies the strength of both when combined. A 10-day PTR-MS field screening period was conducted to determine suitable trigger VOC species with exceedingly high mixing ratios that were likely the culprits of foul odors. Twenty canister samples were then collected, triggered by m/z 43, 61 (ethyl acetate, fragments, EA), m/z 73 (methyl ethyl ketone, MEK), or m/z 88 (morpholine) in all cases. Internal consistency was confirmed by the high correlation of critical species in the PTR-MS and trigger samples. Several long-lived halocarbons were exploited as the intrinsic internal reference for quality assurance. Oxygenated VOCs (OVOCs) accounted for 15%-75% of the total VOC mixing ratios in the triggered samples. However, EA and MEK, the most prominent OVOC species, did not appear to have common sources with morpholine, which presented with PTR-MS peaks incoherent with the other OVOCs. Nevertheless, these distinctive OVOC plumes were consistent with the multiple types of odor reported by the local residents. In contrast with the triggered sampling, random samples in the same industrial zone and roadside samples in a major metropolitan area were collected. The pronounced OVOC content in the triggered samples highlighted the advantage over random grab sampling to address odor issues.

A reinforcement learning fuzzy system for continuous control in robotic odor plume tracking

AbstractIn dynamic outdoor environments characterized by turbulent airflow and intermittent odor plumes, robotic odor plume tracking remains challenging, because existing algorithms heavily rely on manually tuning or learning from expert experience, which are hard to implement in an unknown environment. In this paper, a multi-continuous-output Takagi–Sugeno–Kang fuzzy system was designed and tuned with reinforcement learning to solve the robotic odor source localization problem in dynamic odor plumes. Based on the Lévy Taxis plume tracking controller, the proposed fuzzy system determined the parameters of the controller based on the robot’s observation and guided the robot to turn and move towards the odor source at each searching step. The trained fuzzy system was tested in simulated filament-based odor plumes dispersed by a changing wind field. The results showed that the performance of the proposed fuzzy system-based controller trained with reinforcement learning can achieve a similar success rate and higher efficiency compared with a manually tuned and well-designed fuzzy system-based controller. The fuzzy system-based plume tracking controller was also validated through real robotic experiments.

A central place foraging seabird flies at right angles to the wind to jointly optimize locomotor and olfactory search efficiency.

To increase the probability of detecting odour plumes, and so increase prey capture success, when winds are stable central place foraging seabirds should fly crosswind to maximize the round-trip distance covered. At present, however, there is no empirical evidence of this theoretical prediction. Here, using an extensive GPS tracking dataset, we investigate, for the first time, the foraging movements of Bulwer's petrels (Bulweria bulwerii) in the persistent North Atlantic trade winds. To test the hypotheses that, in stable winds, petrels use crosswind to maximize both the distance covered and the probability of detecting olfactory cues, we combine state-space models, generalized additive models and Gaussian plume models. Bulwer's petrels had the highest degree of selectivity for crosswinds documented to date, often leading to systematic zig-zag flights. Crosswinds maximized both the distance travelled and the probability of detecting odour plumes integrated across the round-trip (rather than at any given point along the route, which would result in energetically costly return flight). This evidence suggests that petrels plan round-trip flights at departure, integrating expected costs of homeward journeys. Our findings, which are probably true for other seabirds in similar settings, further highlight the critical role of wind in seabird foraging ecology.

Alternation emerges as a multi-modal strategy for turbulent odor navigation.

Foraging mammals exhibit a familiar yet poorly characterized phenomenon, 'alternation', a pause to sniff in the air preceded by the animal rearing on its hind legs or raising its head. Rodents spontaneously alternate in the presence of airflow, suggesting that alternation serves an important role during plume-tracking. To test this hypothesis, we combine fully resolved simulations of turbulent odor transport and Bellman optimization methods for decision-making under partial observability. We show that an agent trained to minimize search time in a realistic odor plume exhibits extensive alternation together with the characteristic cast-and-surge behavior observed in insects. Alternation is linked with casting and occurs more frequently far downwind of the source, where the likelihood of detecting airborne cues is higher relative to ground cues. Casting and alternation emerge as complementary tools for effective exploration with sparse cues. A model based on marginal value theory captures the interplay between casting, surging, and alternation.

Learning to predict target location with turbulent odor plumes.

Animal behavior and neural recordings show that the brain is able to measure both the intensity and the timing of odor encounters. However, whether intensity or timing of odor detections is more informative for olfactory-driven behavior is not understood. To tackle this question, we consider the problem of locating a target using the odor it releases. We ask whether the position of a target is best predicted by measures of timing vs intensity of its odor, sampled for a short period of time. To answer this question, we feed data from accurate numerical simulations of odor transport to machine learning algorithms that learn how to connect odor to target location. We find that both intensity and timing can separately predict target location even from a distance of several meters; however, their efficacy varies with the dilution of the odor in space. Thus, organisms that use olfaction from different ranges may have to switch among different modalities. This has implications on how the brain should represent odors as the target is approached. We demonstrate simple strategies to improve accuracy and robustness of the prediction by modifying odor sampling and appropriately combining distinct measures together. To test the predictions, animal behavior and odor representation should be monitored as the animal moves relative to the target, or in virtual conditions that mimic concentrated vs dilute environments.

A fuzzy control algorithm for tracing air pollution based on unmanned aerial vehicles

ABSTRACT The process of atmospheric pollutants traceability based on unmanned aerial vehicles (UAVs) is affected by many factors that can impact and increase the complexity of the traceability of atmospheric pollutants. In this study, we proposed a new algorithm called the fuzzy control traceability (FCT) to track odor plumes. Our proposed algorithm combined the characteristics and fuzzy control of the UAV and designed a controller based on the actual environment of the UAV. The fuzzy controller fuzzed the input gas concentration information, established fuzzy control rules by imitating human brain thinking, and outputted the turning angle and the move length according to rules, thus realizing intelligent tracking of the odor plume by the UAV. We compared the FCT algorithm with the bio-inspired “ZigZag” algorithm to validate its performance. Various concentration fields were constructed, and ten sets of experiments are performed using the two algorithms in different concentration fields. The average success rate of the FCT algorithm under different concentration fields was 95.4% higher than that of the ZigZag algorithm. Implications: Fuzzy control logic is applied to the field of air pollutant traceability of drones, and a single drone traceability algorithm based on fuzzy control is proposed; and in view of the shortcomings of a single traceability subject in the traceability, multiple traceability subjects are introduced to optimize fuzzy control.