Olfactory Search Research Articles

Research Progress of Using Canine Olfactory Search for Human Remains.

In the process of murder investigation, it is of great significance to find the discarded and buried human remains accurately. The main methods of searching for human remains include human visual search, aerial detection, geophysical technology, remote imaging technology and canine olfactory search technique. Canine olfactory search for human remains is a recognized time-effective and non-invasive search method, making dogs the most valuable search tool in forensic investigation. By systematically reviewing and summarizing relevant literature, and based on the theory of volatile organic compound produced by the decomposition of human remains, this paper explores the basic principle of the canine olfactory search technique for human remains. This paper also reviews the application of training canine search technique for human remains in forensic investigation by using human blood, tissue, cadaver putrefying fluid and odor substitutes as sniffing sources. The application prospect of canine olfactory search for human remains was prospected from the perspectives of detection of volatile organic compound during cadaver decay, development of odor substitutes and adsorption devices, and technology tactics used in canine training and use, to provide references for the relevant research of canine olfactory search for human remains in China.

Working with dogs in olfactory searches in the French Armed Forces and national Police Forces

ABSTRACT This study aims to investigate the relationships and cooperation within human-dog teams working in olfactory search in the French Armed Forces and in national Police Forces. Twenty-six semi-structured interviews were carried out with canine handlers. The main topic concerned working with their dog during olfactory searches. Among the themes that stand out are relationships with their dog and canine knowledge and practices. Minor differences and similarities between dyads of the two institutions are identified in human-dog relationships. Regarding relationships with dogs, two main differences are linked to the long missions abroad experienced by Armed Forces handlers and the possibility of welcoming dogs into their homes for Police Forces. The constitution of canine knowledge and practices is rooted in experiences and transactions with dogs, but also in social transmission. Even though there is no major differences between the two institutions, this study highlights the interest of having a situated approach.

Behavioral discrimination and olfactory bulb encoding of odor plume intermittency.

In order to survive, animals often need to navigate a complex odor landscape where odors can exist in airborne plumes. Several odor plume properties change with distance from the odor source, providing potential navigational cues to searching animals. Here, we focus on odor intermittency, a temporal odor plume property that measures the fraction of time odor is above a threshold at a given point within the plume and decreases with increasing distance from the odor source. We sought to determine if mice can use changes in intermittency to locate an odor source. To do so, we trained mice on an intermittency discrimination task. We establish that mice can discriminate odor plume samples of low and high intermittency and that the neural responses in the olfactory bulb can account for task performance and support intermittency encoding. Modulation of sniffing, a behavioral parameter that is highly dynamic during odor-guided navigation, affects both behavioral outcome on the intermittency discrimination task and neural representation of intermittency. Together, this work demonstrates that intermittency is an odor plume property that can inform olfactory search and more broadly supports the notion that mammalian odor-based navigation can be guided by temporal odor plume properties.

Seeking and sharing information in collective olfactory search

Searching for a target is a task of fundamental importance for many living organisms. Long-distance search guided by olfactory cues is a prototypical example of such a process. The searcher receives signals that are sparse and very noisy, making the task extremely difficult. Information-seeking strategies have thus been proven to be effective for individual olfactory search and their extension to collective search has been the subject of some exploratory studies. Here, we study in detail how sharing information among members of a group affects the search behavior when agents adopt information-seeking strategies as Infotaxis and its recently introduced variant, Space-Aware Infotaxis. Our results show that even in absence of explicit coordination, sharing information leads to an effective partitioning of the search space among agents that results in a significant decrease of mean search times.

Olfactory search with finite-state controllers

Long-range olfactory search is an extremely difficult task in view of the sparsity of odor signals that are available to the searcher and the complex encoding of the information about the source location. Current algorithmic approaches typically require a continuous memory space, sometimes of large dimensionality, which may hamper their optimization and often obscure their interpretation. Here, we show how finite-state controllers with a small set of discrete memory states are expressive enough to display rich, time-extended behavioral modules that resemble the ones observed in living organisms. Finite-state controllers optimized for olfactory search have an immediate interpretation in terms of approximate clocks and coarse-grained spatial maps, suggesting connections with neural models of search behavior.

Optimal policies for Bayesian olfactory search in turbulent flows.

In many practical scenarios, a flying insect must search for the source of an emitted cue which is advected by the atmospheric wind. On the macroscopic scales of interest, turbulence tends to mix the cue into patches of relatively high concentration over a background of very low concentration, so that the insect will detect the cue only intermittently and cannot rely on chemotactic strategies which simply climb the concentration gradient. In this work we cast this search problem in the language of a partially observable Markov decision process and use the Perseus algorithm to compute strategies that are near-optimal with respect to the arrival time. We test the computed strategies on a large two-dimensional grid, present the resulting trajectories and arrival time statistics, and compare these to the corresponding results for several heuristic strategies, including (space-aware) infotaxis, Thompson sampling, and QMDP. We find that the near-optimal policy found by our implementation of Perseus outperforms all heuristics we test by several measures. We use the near-optimal policy to study how the search difficulty depends on the starting location. We also discuss the choice of initial belief and the robustness of the policies to changes in the environment. Finally, we present a detailed and pedagogical discussion about the implementation of the Perseus algorithm, including the benefits-and pitfalls-of employing a reward-shaping function.

Deep reinforcement learning for the olfactory search POMDP: a quantitative benchmark.

The olfactory search POMDP (partially observable Markov decision process) is a sequential decision-making problem designed to mimic the task faced by insects searching for a source of odor in turbulence, and its solutions have applications to sniffer robots. As exact solutions are out of reach, the challenge consists in finding the best possible approximate solutions while keeping the computational cost reasonable. We provide a quantitative benchmarking of a solver based on deep reinforcement learning against traditional POMDP approximate solvers. We show that deep reinforcement learning is a competitive alternative to standard methods, in particular to generate lightweight policies suitable for robots.

Drivers of polar bear behavior and the possible effects of prey availability on foraging strategy

BackgroundChange in behavior is one of the earliest responses to variation in habitat suitability. It is therefore important to understand the conditions that promote different behaviors, particularly in areas undergoing environmental change. Animal movement is tightly linked to behavior and remote tracking can be used to study ethology when direct observation is not possible.MethodsWe used movement data from 14 polar bears (Ursus maritimus) in Hudson Bay, Canada, during the foraging season (January–June), when bears inhabit the sea ice. We developed an error-tolerant method to correct for sea ice drift in tracking data. Next, we used hidden Markov models with movement and orientation relative to wind to study three behaviors (stationary, area-restricted search, and olfactory search) and examine effects of 11 covariates on behavior.ResultsPolar bears spent approximately 47% of their time in the stationary drift state, 29% in olfactory search, and 24% in area-restricted search. High energy behaviors occurred later in the day (around 20:00) compared to other populations. Second, olfactory search increased as the season progressed, which may reflect a shift in foraging strategy from still-hunting to active search linked to a shift in seal availability (i.e., increase in haul-outs from winter to the spring pupping and molting seasons). Last, we found spatial patterns of distribution linked to season, ice concentration, and bear age that may be tied to habitat quality and competitive exclusion.ConclusionsOur observations were generally consistent with predictions of the marginal value theorem, and differences between our findings and other populations could be explained by regional or temporal variation in resource availability. Our novel movement analyses and finding can help identify periods, regions, and conditions of critical habitat.

Ectophagous folivores do not profit from rich resources on phylogenetically isolated trees.

Resource use by consumers across patches is often proportional to the quantity or quality of the resource within these patches. In folivores, such proportional use of resources is likely to be more efficient when plants are spatially proximate, such as trees forming a forest canopy. However, resources provided by forest-trees are often not used proportionally. We hypothesised that proportional use of resources is reduced when host trees are isolated among phylogenetically distant neighbours that mask olfactory and visual search cues, and reduce folivore movement between trees. Such phylogenetically distant neighbourhoods might sort out species that are specialists, poor dispersers, or have poor access to information about leaf quality. We studied individual oaks, their leaf size and quality, their folivory and abundance of folivores (mostly Lepidopteran ectophages, gallers and miners), and parasitism of folivores. We found that leaf consumption by ectophages hardly increased with increasing leaf size when host trees were phylogenetically isolated. We found a similar effect on host use by parasitoids in 1year. In contrast, we found no consistent effects in other folivore guilds. Relative abundances of specialists and species with wingless females declined with phylogenetic isolation. However, resource use within each of these groups was inconsistently affected by phylogenetic isolation. We suggest that phylogenetic isolation prevents ectophages from effectively choosing trees with abundant resources, and also sorts out species likely to recruit in situ on their host tree. Trees in phylogenetically distant neighbourhoods may be selected for larger leaves and greater reliance on induced defences.

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.

A discrete learning fruit fly algorithm based on knowledge for the distributed no-wait flow shop scheduling with due windows

The distributed no-wait flow shop scheduling problem with due windows (DNWFSPDW) is a novel and considerable model for modern production chain and large manufacturing industry. The object of total weighted earliness and tardiness (TWETdw) is a common cost indicator in application. A discrete knowledge-guided learning fruit fly optimization algorithm (DKLFOA) is proposed in this study to minimize TWET in DNWFSPDW. A knowledge-based structural initialization method (KNEHdw) is proposed to construct an effective initial solution. In the KNEHdw, the property that the job has no waiting time between processing machines in the no-wait flow shop scheduling problem is abstracted as knowledge to instruct jobs to be placed in possible positions. The swarm center expands from a single individual to an elitist swarm in the vision search stage. A probability knowledge model is established based on the sequence relationship of jobs in the elite population. The feedback information in the iterative process using the probabilistic knowledge model leads the population to search in the direction with a high success rate. The inferior individuals are allocated to the corresponding elite individuals for the local search in the olfactory search stage. The knowledge of weight in due windows is utilized to avoid invalid search during the iteration process. The variable neighborhood descent (VND) strategy is adopted in the local search to enhance the accuracy of the proposed algorithm and jump out of the local optimal. The design of experimental method (DOE) is introduced to calibrate the parameters in the algorithm. The simulation results show that DKLFOA has advantages for solving DNWFSPDW problems comparing with the state-of-the-art algorithms.

Learning a Generic Olfactory Search Strategy From Silk Moths by Deep Inverse Reinforcement Learning

Despite their simple nervous systems, insects efficiently search for and find sources of odorants. Hence, it is necessary to model and implement such behavior in artificial agents (robots), to enable them to detect dangerous substances such as drugs, gas leaks, and explosives. Previous studies have approached behavioral modeling with either statistical or machine-learning methods. In this study, we determined the behavior trajectories of male silk moths using a virtual reality (VR) system. We then modeled these trajectories as a Markov decision process (MDP) and employed inverse reinforcement learning (IRL) to learn their reward function. Furthermore, we estimated the optimal policy from the learned reward function. We then conducted olfactory search simulations and determined that the IRL-based policy could locate odor sources with a high success rate. This was also investigated under environmental conditions different from those faced by real moths on the VR system. The obtained results indicate that IRL can generically represent olfactory search strategies that are adaptable to various environments.

Sniff-synchronized, gradient-guided olfactory search by freely moving mice.

For many organisms, searching for relevant targets such as food or mates entails active, strategic sampling of the environment. Finding odorous targets may be the most ancient search problem that motile organisms evolved to solve. While chemosensory navigation has been well characterized in microorganisms and invertebrates, spatial olfaction in vertebrates is poorly understood. We have established an olfactory search assay in which freely moving mice navigate noisy concentration gradients of airborne odor. Mice solve this task using concentration gradient cues and do not require stereo olfaction for performance. During task performance, respiration and nose movement are synchronized with tens of milliseconds precision. This synchrony is present during trials and largely absent during inter-trial intervals, suggesting that sniff-synchronized nose movement is a strategic behavioral state rather than simply a constant accompaniment to fast breathing. To reveal the spatiotemporal structure of these active sensing movements, we used machine learning methods to parse motion trajectories into elementary movement motifs. Motifs fall into two clusters, which correspond to investigation and approach states. Investigation motifs lock precisely to sniffing, such that the individual motifs preferentially occur at specific phases of the sniff cycle. The allocentric structure of investigation and approach indicates an advantage to sampling both sides of the sharpest part of the odor gradient, consistent with a serial-sniff strategy for gradient sensing. This work clarifies sensorimotor strategies for mouse olfactory search and guides ongoing work into the underlying neural mechanisms.

Olfactory tracking strategies in a neotropical fruit bat.

ABSTRACTMany studies have characterized olfactory-tracking behaviors in animals, and it has been proposed that search strategies may be generalizable across a wide range of species. Olfaction is important for fruit- and nectar-feeding bats, but it is uncertain whether existing olfactory search models can predict the strategies of flying mammals that emit echolocation pulses through their nose. Quantitative assessments of how well echolocating bats track and localize odor sources are lacking, so we developed a behavioral assay to characterize the olfactory detection and tracking behavior of crawling northern yellow-shouldered bats (Sturnira parvidens), a common neotropical frugivore. Trained bats were presented with a choice between control and banana-odor-infused solutions in a series of experiments that confirmed that bats are able to locate a reward based on odor cues alone and examined the effect of odor concentration on olfactory search behaviors. Decision distance (the distance from which bats made their change in direction before directly approaching the target) was distinctly bimodal, with an observed peak that coincided with an inflection point in the odor concentration gradient. We observed two main search patterns that are consistent with both serial sampling and learned route-following strategies. These results support the hypothesis that bats can combine klinotaxis with spatial awareness of experimental conditions to locate odor sources, similar to terrestrial mammals. Contrary to existing models, bats did not display prominent head-scanning behaviors during their final approach, which may be due to constraints of nasal-emitted biosonar for orientation.

Identification of Exploration and Exploitation Balance in the Silkmoth Olfactory Search Behavior by Information-Theoretic Modeling.

Insects search for and find odor sources as their basic behaviors, such as when looking for food or a mate. This has motivated research to describe how they achieve such behavior under turbulent odor plumes with a small number of neurons. Among different insects, the silk moth has been studied owing to its clear motor response to olfactory input. In past studies, the “programmed behavior” of the silk moth has been modeled as the average duration of a sequence of maneuvers based on the duration of periods without odor hits. However, this model does not fully represent the fine variations in their behavior. In this study, we used silk moth olfactory search trajectories from an experimental virtual reality device. We achieved an accurate input by using optogenetic silk moths that react to blue light. We then modeled such trajectories as a probabilistic learning agent with a belief of possible source locations. We found that maneuvers mismatching the programmed behavior are related to larger entropy decrease, that is, they are more likely to increase the certainty of the belief. This implies that silkmoths include some stochasticity in their search policy to balance the exploration and exploitation of olfactory information by matching or mismatching the programmed behavior model. We believe that this information-theoretic representation of insect behavior is important for the future implementation of olfactory searches in artificial agents such as robots.

The Dynamics of Bilateral Olfactory Search and Navigation

Animals use stereo sampling of odor concentration to localize sources and follow odor trails. We analyze the dynamics of a bilateral model that depends on the simultaneous comparison between odor concentrations detected by left and right sensors. The general model consists of three differential equations for the positions in the plane and the heading. When the odor landscape is an infinite trail, then we reduce the dynamics to a planar system whose dynamics have just two fixed points. Using an integrable approximation (for short sensors) we estimate the basin of attraction. In the case of a radially symmetric landscape, we again can reduce the dynamics to a planar system, but the behavior is considerably richer with multi-stability, isolas, and limit cycles. As in the linear trail case, there is also an underlying integrable system when the sensors are short. In odor landscapes that consist of multiple spots and trail segments, we find periodic and chaotic dynamics and characterize the behavior on trails with gaps and that turn corners.

Research Spotlights

We have two Research Spotlights papers in the lineup this issue. The first of these, “Persistent Homology of Geospatial Data: A Case Study with Voting," written by Michelle Feng and Mason Porter, is quite timely. The article begins with a gentle introduction into the what and why of persistent homology (PH): after converting the point cloud of data into a series of simplicial complexes at different scales, the homology groups are computed at each scale, with homological features tracked across scales. It is generally assumed that persistence of features can then be used to distinguish signal (important patterns) from noise. Of concern is the method of converting the data into the simplicial complexes. The authors argue that PH using the simplicial complexes computed via the distance-based approach can lead to erroneous assumptions or missed characteristics about the voting data they consider. They propose to counter these deficiencies by developing two new methods, one based on adjacency networks and one that utilizes level sets to capture the manifold nature of the data, for building a filtered simplicial complex. The dramatic differences achieved by varying the complexes on the resulting PH are colorfully illustrated and interpreted for real voter data. Readers may appreciate not only the tools used and results obtained, but also the broad range of problems---transportation networks, spatial demography, granular materials, biological structures---identified by the authors for which the new approaches may prove relevant. An animal's ability to localize odor sources and follow trails of odor is key to many aspects of its survival. Authors Nour Riman, Jonathan D. Victor, Sebastian D. Boie, and Bard Ermentrout focus their article, “The Dynamics of Bilateral Olfactory Search and Navigation," on providing a comprehensive mathematical analysis of the dynamics of the differential equations that model the way that animals use bilateral information to navigate odor sources. When odor sensors are located at two different positions of the body, it enables comparison of odor concentrations and thus allows the animal to determine whether to move toward or away from the stimulus. Tropotaxis in the present context refers to the movement of the animal in response to an odor directly toward or away from the source of the odor. The sensor angles, which the animal can control, play an important role in enabling the animal to find and follow a trail. The other model parameters include sensor length and sensitivity to concentration change. The authors investigate tropotaxis as functions of the model parameters under varying conditions for the odor sources and trails. In some cases, the authors are able to reduce the dynamics to a planar system. Graphical illustrations compliment the discussion of the behavior. The paper concludes with a discussion of future research, particularly with respect to exploring the effects of noise on the models. It is the hope that this new “understanding [of] the underlying dynamics of the bilateral model will help in building models that use bilateral information together with other strategies."

Collective olfactory search in a turbulent environment.

Finding the source of an odor dispersed by a turbulent flow is a vital task for many organisms. When many individuals concurrently perform the same olfactory search task, sharing information about other members' decisions can potentially boost the performance. But how much of this information is actually exploitable for the collective task? Here we show, in a model of a swarm of agents inspired by moth behavior, that there is an optimal way to blend the private information about odor and wind detections with the public information about other agents' heading direction. Our results suggest an efficient multiagent olfactory search algorithm that could prove useful in robotics, e.g., in the identification of sources of harmful volatile compounds.

Interpreting the Spatial-Temporal Structure of Turbulent Chemical Plumes Utilized in Odor Tracking by Lobsters

Olfactory systems in animals play a major role in finding food and mates, avoiding predators, and communication. Chemical tracking in odorant plumes has typically been considered a spatial information problem where individuals navigate towards higher concentration. Recent research involving chemosensory neurons in the spiny lobster, Panulirus argus, show they possess rhythmically active or ‘bursting’ olfactory receptor neurons that respond to the intermittency in the odor signal. This suggests a possible, previously unexplored olfactory search strategy that enables lobsters to utilize the temporal variability within a turbulent plume to track the source. This study utilized computational fluid dynamics to simulate the turbulent dispersal of odorants and assess a number of search strategies thought to aid lobsters. These strategies include quantification of concentration magnitude using chemosensory antennules and leg chemosensors, simultaneous sampling of water velocities using antennule mechanosensors, and utilization of antennules to quantify intermittency of the odorant plume. Results show that lobsters can utilize intermittency in the odorant signal to track an odorant plume faster and with greater success in finding the source than utilizing concentration alone. However, the additional use of lobster leg chemosensors reduced search time compared to both antennule intermittency and concentration strategies alone by providing spatially separated odorant sensors along the body.

Hybrid Mutation Fruit Fly Optimization Algorithm for Solving the Inverse Kinematics of a Redundant Robot Manipulator

The inverse kinematics of redundant manipulators is one of the most important and complicated problems in robotics. Simultaneously, it is also the basis for motion control, trajectory planning, and dynamics analysis of redundant manipulators. Taking the minimum pose error of the end-effector as the optimization objective, a fitness function was constructed. Thus, the inverse kinematics problem of the redundant manipulator can be transformed into an equivalent optimization problem, and it can be solved using a swarm intelligence optimization algorithm. Therefore, an improved fruit fly optimization algorithm, namely, the hybrid mutation fruit fly optimization algorithm (HMFOA), was presented in this work for solving the inverse kinematics of a redundant robot manipulator. An olfactory search based on multiple mutation strategies and a visual search based on the dynamic real-time updates were adopted in HMFOA. The former has a good balance between exploration and exploitation, which can effectively solve the premature convergence problem of the fruit fly optimization algorithm (FOA). The latter makes full use of the successful search experience of each fruit fly and can improve the convergence speed of the algorithm. The feasibility and effectiveness of HMFOA were verified by using 8 benchmark functions. Finally, the HMFOA was tested on a 7-degree-of-freedom (7-DOF) manipulator. Then the results were compared with other algorithms such as FOA, LGMS-FOA, AE-LGMS-FOA, IFOA, and SFOA. The pose error of end-effector corresponding to the optimal inverse solution of HMFOA is 10−14 mm, while the pose errors obtained by FOA, LGMS-FOA, AE-LGMS-FOA, IFOA, and SFOA are 102 mm, 10−1 mm, 10−2 mm, 102 mm, and 102 mm, respectively. The experimental results show that HMFOA can be used to solve the inverse kinematics problem of redundant manipulators effectively.