Plume mapping via hidden Markov methods

A Comparison of Multiple Odor Source Localization Algorithms

There are two primary algorithms for autonomous multiple odor source localization (MOSL) in an environment with turbulent fluid flow: Independent Posteriors (IP) and Dempster-Shafer (DS) theory algorithms. Both of these algorithms use a form of occupancy grid mapping to map the probability that a given location is a source. They have potential applications to assist in locating emitting sources using mobile point sensors. However, the performance and limitations of these two algorithms is currently unknown, and a better understanding of their effectiveness under various conditions is required prior to application. To address this knowledge gap, we tested the response of both algorithms to different environmental and odor search parameters. The localization performance of the algorithms was measured using the earth mover's distance. Results indicate that the IP algorithm outperformed the DS theory algorithm by minimizing source attribution in locations where there were no sources, while correctly identifying source locations. The DS theory algorithm also identified actual sources correctly but incorrectly attributed emissions to many locations where there were no sources. These results suggest that the IP algorithm offers a more appropriate approach for solving the MOSL problem in environments with turbulent fluid flow.

Average search time bounds in cue-based searches

In this work we consider search problems that evaluate the probability distribution of finding the source at each step in the search. We start with a sample strategy where the movement at each time step is in the immediate neighborhood. The jump probability is taken to be proportional to the normalized difference between the probability of finding the source at the jump location with the probability of finding the source at the present location. We evaluate a lower bound on the average search time for a searcher using this strategy. We next consider the problem of evaluating the lower bound on the search time for a generic strategy which would utilize the source probability distribution to figure out the position of the source. We derive an expression for the lower bound on the search time. We present an analytic expression for this lower bound in a case in which the particles emitted by the source diffuse in a homogeneous manner. For a general probability distribution with entropy E, we find that the lower bound goes as e^{E/2}.

Navigation Along Windborne Plumes of Pheromone and Resource-Linked Odors

Many insects locate resources such as a mate, a host, or food by flying upwind along the odor plumes that these resources emit to their source. A windborne plume has a turbulent structure comprised of odor filaments interspersed with clean air. As it propagates downwind, the plume becomes more dispersed and dilute, but filaments with concentrations above the threshold required to elicit a behavioral response from receiving organisms can persist for long distances. Flying insects orient along plumes by steering upwind, triggered by the optomotor reaction. Sequential measurements of differences in odor concentration are unreliable indicators of distance to or direction of the odor source. Plume intermittency and the plume's fine-scale structure can play a role in setting an insect's upwind course. The prowess of insects in navigating to odor sources has spawned bioinspired virtual models and even odor-seeking robots, although some of these approaches use mechanisms that are unnecessarily complex and probably exceed an insect's processing capabilities.

Environmental chemical sensing using small drones: A review

Recent advances in miniaturization of chemical instrumentation and in low-cost small drones are catalyzing exponential growth in the use of such platforms for environmental chemical sensing applications. The versatility of chemically sensitive drones is reflected by their rapid adoption in scientific, industrial, and regulatory domains, such as in atmospheric research studies, industrial emission monitoring, and in enforcement of environmental regulations. As a result of this interdisciplinarity, progress to date has been reported across a broad spread of scientific and non-scientific databases, including scientific journals, press releases, company websites, and field reports. The aim of this paper is to assemble all of these pieces of information into a comprehensive, structured and updated review of the field of chemical sensing using small drones. We exhaustively review current and emerging applications of this technology, as well as sensing platforms and algorithms developed by research groups and companies for tasks such as gas concentration mapping, source localization, and flux estimation. We conclude with a discussion of the most pressing technological and regulatory limitations in current practice, and how these could be addressed by future research.

Braitenberg Vehicles as Computational Tools for Research in Neuroscience

Valentino Braitenberg reported his seminal thought experiment in 1984 using reactive automatons or vehicles with relatively simple sensorimotor connections as models for seemingly complex cognitive processes in biological brains. Braitenberg's work, meant as a metaphor for biological life encompassed a deep knowledge of and served as an analogy for the multitude of neural processes and pathways that underlie animal behavior, suggesting that seemingly complex behavior may arise from relatively simple designs. Braitenberg vehicles have been adopted in robotics and artificial life research for sensor-driven navigation behaviors in robots, such as localizing sound and chemical sources, orienting toward or away from current flow under water etc. The neuroscience community has benefitted from applying Braitenberg's bottom-up approach toward understanding analogous neural mechanisms underpinning his models of animal behavior. We present a summary of the latest studies of Braitenberg vehicles for bio-inspired navigation and relate the results to experimental findings on the neural basis of navigation behavior in animals. Based on these studies, we motivate the important role of Braitenberg vehicles as computational tools to inform research in behavioral neuroscience.

Consensus-Based Odor Source Localization by Multiagent Systems Under Resource Constraints

With advancements in mobile robot olfaction, networked multiagent systems (MASs) are used in odor source localization (OSL). These MASs are often equipped with small microprocessors that have limited computing capabilities, and they usually operate in a bandwidth and energy-constrained environment. The exigent need for a faster localizing algorithm under communication and computational resource constraints invites many design challenges. In this paper, we have designed a two-level hierarchical cooperative control strategy for heterogeneous nonlinear MASs for OSL. The agents are forced toward consensus expeditiously once the information on the whereabouts of the source is attained. The synthesis of the controller occurs in a hierarchical manner-obtaining a group decision, followed by resource-efficient robust control. Odor concentration and wind information have been used in a group decision-making layer to predict a probable location of the source as a tracking reference. This reference is then fed to the control layer that is synthesized using event-triggered sliding-mode control (SMC). The advantage of using event-triggered control scheduling in conjunction with the SMC is rooted in retaining the robustness of the SMC while lowering the resource utilization burden. Numerical simulations confirm the efficiency of the scheme put forth.

Dynamic Correlation Analysis Method of Air Pollutants in Spatio-Temporal Analysis

Pollutant analysis and pollution source tracing are critical issues in air quality management, in which correlation analysis is important for pollutant relation modeling. A dynamic correlation analysis method was proposed to meet the real-time requirement in atmospheric management. Firstly, the spatio-temporal analysis framework was designed, in which the process of data monitoring, correlation calculation, and result presentation were defined. Secondly, the core correlation calculation method was improved with an adaptive data truncation and grey relational analysis. Thirdly, based on the general framework and correlation calculation, the whole algorithm was proposed for various analysis tasks in time and space, providing the data basis for ranking and decision on pollutant effects. Finally, experiments were conducted with the practical data monitored in an industrial park of Hebei Province, China. The different pollutants in multiple monitoring stations were analyzed crosswise. The dynamic features of the results were obtained to present the variational correlation degrees from the proposed and contrast methods. The results proved that the proposed dynamic correlation analysis could quickly acquire atmospheric pollution information. Moreover, it can help to deduce the influence relation of pollutants in multiple locations.

Consensus-Based Odor Source Localization by Multiagent Systems

This paper presents an investigation of the task of localizing an unknown source of an odor by heterogeneous multiagent systems. A hierarchical cooperative control strategy has been proposed as a potential candidate to solve the problem. The agents are driven into consensus as soon as the information about the location of source is acquired. The controller has been designed in a hierarchical manner of group decision making, agent path planning, and robust control. In group decision making, the particle swarm optimization algorithm has been used along with the information of the movement of odor molecules to predict the odor source location. Next, a trajectory has been mapped using this predicted location of source, and the information is passed to the control layer. A variable structure control has been used in the control layer due to its inherent robustness and disturbance rejection capabilities. Cases of movement of agents toward the source under consensus and parallel formation have been discussed. The efficacy of the proposed scheme has been confirmed by simulations.

Event-Based Communication and Finite-Time Consensus Control of Mobile Sensor Networks for Environmental Monitoring

This paper deals with the problem of environmental monitoring by designing a cooperative control scheme for mobile sensor networks. The proposed cooperative control scheme includes three main modules: a wireless communication module, a direction decision module, and a motion control module. In the wireless communication module, an event-based communication rule is proposed, which means that mobile sensor nodes do not send their positions, velocities, and the data of environmental attributes to the other sensor nodes in real-time for the coordination and control of mobile sensor networks. Due to using the event-based communication rule, the communication bandwidth can be saved. In the direction decision module, a radial basis function network is used to model the monitored environment and is updated in terms of the sampled environmental data and the environmental data from the other sensor nodes by the wireless communication module. The updated environment model is used to guide the mobile sensor network to move towards the region of interest in order to efficiently model the distribution map of environmental attributes, such as temperature, salinity, and pH values for the monitored environment. In the motion control module, a finite-time consensus control approach is proposed to enable the sensor nodes to quickly change their movement directions in light of the gradient information from the environment model. As a result of using the event-based communication rule in the wireless communication module, the proposed control approach can also lower the updating times of the control signal. In particular, the proposed cooperative control scheme is still efficient under the directed wireless communication situation. Finally, the effectiveness of the proposed cooperative control scheme is illustrated for the problem of environmental monitoring.

Simulating Fine-Scale Marine Pollution Plumes for Autonomous Robotic Environmental Monitoring

Marine plumes exhibit characteristics such as intermittency, sinuous structure, shape and flow field coherency, and a time varying concentration profile. Due to the lack of experimental quantification of these characteristics for marine plumes, existing work often assumes marine plumes exhibit behavior similar to aerial plumes and are commonly modeled by filament based Lagrangian models. Our previous field experiments with Rhodamine dye plumes at Makai Research Pier at Oahu, Hawaii revealed that marine plumes show similar characteristics to aerial plumes qualitatively, but quantitatively they are disparate. Based on the field data collected, this paper presents a calibrated Eulerian plume model that exhibits the qualitative and quantitative characteristics exhibited by experimentally generated marine plumes. We propose a modified model with an intermittent source, and implement it in a Robot Operating System (ROS) based simulator. Concentration time series of stationary sampling points and dynamic sampling points across cross-sections and plume fronts are collected and analyzed for statistical parameters of the simulated plume. These parameters are then compared with statistical parameters from experimentally generated plumes. The comparison validates that the simulated plumes exhibit fine-scale qualitative and quantitative characteristics similar to experimental plumes. The ROS plume simulator facilitates future evaluations of environmental monitoring strategies by marine robots, and is made available for community use.

Cooperative Control of Mobile Sensor Networks for Environmental Monitoring: An Event-Triggered Finite-Time Control Scheme

This paper deals with the problem of environmental monitoring by developing an event-triggered finite-time control scheme for mobile sensor networks. The proposed control scheme can be executed by each sensor node independently and consists of two parts: one part is a finite-time consensus algorithm while the other part is an event-triggered rule. The consensus algorithm is employed to enable the positions and velocities of sensor nodes to quickly track the position and velocity of a virtual leader in finite time. The event-triggered rule is used to reduce the updating frequency of controllers in order to save the computational resources of sensor nodes. Some stability conditions are derived for mobile sensor networks with the proposed control scheme under both a fixed communication topology and a switching communication topology. Finally, simulation results illustrate the effectiveness of the proposed control scheme for the problem of environmental monitoring.

Different Ways to Apply a Measurement Instrument of E-Nose Type to Evaluate Ambient Air Quality with Respect to Odour Nuisance in a Vicinity of Municipal Processing Plants

This review paper presents different ways to apply a measurement instrument of e-nose type to evaluate ambient air with respect to detection of the odorants characterized by unpleasant odour in a vicinity of municipal processing plants. An emphasis was put on the following applications of the electronic nose instruments: monitoring networks, remote controlled robots and drones as well as portable devices. Moreover, this paper presents commercially available sensors utilized in the electronic noses and characterized by the limit of quantification below 1 ppm v/v, which is close to the odour threshold of some odorants. Additionally, information about bioelectronic noses being a possible alternative to electronic noses and their principle of operation and application potential in the field of air evaluation with respect to detection of the odorants characterized by unpleasant odour was provided.

Chemical Source Localization Fusing Concentration Information in the Presence of Chemical Background Noise

We present the estimation of a likelihood map for the location of the source of a chemical plume dispersed under atmospheric turbulence under uniform wind conditions. The main contribution of this work is to extend previous proposals based on Bayesian inference with binary detections to the use of concentration information while at the same time being robust against the presence of background chemical noise. For that, the algorithm builds a background model with robust statistics measurements to assess the posterior probability that a given chemical concentration reading comes from the background or from a source emitting at a distance with a specific release rate. In addition, our algorithm allows multiple mobile gas sensors to be used. Ten realistic simulations and ten real data experiments are used for evaluation purposes. For the simulations, we have supposed that sensors are mounted on cars which do not have among its main tasks navigating toward the source. To collect the real dataset, a special arena with induced wind is built, and an autonomous vehicle equipped with several sensors, including a photo ionization detector (PID) for sensing chemical concentration, is used. Simulation results show that our algorithm, provides a better estimation of the source location even for a low background level that benefits the performance of binary version. The improvement is clear for the synthetic data while for real data the estimation is only slightly better, probably because our exploration arena is not able to provide uniform wind conditions. Finally, an estimation of the computational cost of the algorithmic proposal is presented.

Highways in the sky: scales of atmospheric transport of plant pathogens

Many high-risk plant pathogens are transported over long distances (hundreds of meters to thousands of kilometers) in the atmosphere. The ability to track the movement of these pathogens in the atmosphere is essential for forecasting disease spread and establishing effective quarantine measures. Here, we discuss the scales of atmospheric dispersal of plant pathogens along a transport continuum (pathogen scale, farm scale, regional scale, and continental scale). Growers can use risk information at each of these dispersal scales to assist in making plant disease management decisions, such as the timely application of appropriate pesticides. Regional- and continental-scale atmospheric features known as Lagrangian coherent structures (LCSs) may shuffle plant pathogens along highways in the sky. A promising new method relying on overlapping turbulent back-trajectories of pathogen-laden parcels of air may assist in localizing potential inoculum sources, informing local and/or regional management efforts such as conservation tillage. The emergence of unmanned aircraft systems (UASs, or drones) to sample plant pathogens in the lower atmosphere, coupled with source localization efforts, could aid in mitigating the spread of high-risk plant pathogens.

Optimal swarm formation for odor plume finding

This paper presents an analytical approach to the problem of odor plume finding by a network of swarm robotic gas sensors, and finds an optimal configuration for them, given a set of assumptions. Considering cross-wind movement for the swarm, we found that the best spatial formation of robots in finding odor plumes is diagonal line configuration with equal distance between each pair of neighboring robots. We show that the distance between neighboring pairs in the line topology depends mainly on the wind speed and the environmental conditions, whereas, the number of robots and the swarm's crosswind movement distance do not show significant impact on optimal configurations. These solutions were analyzed and verified by simulations and experimentally validated in a reduced scale realistic environment using a set of mobile robots.

Reactive searching and infotaxis in odor source localization

Male moths aiming to locate pheromone-releasing females rely on stimulus-adapted search maneuvers complicated by a discontinuous distribution of pheromone patches. They alternate sequences of upwind surge when perceiving the pheromone and cross- or downwind casting when the odor is lost. We compare four search strategies: three reactive versus one cognitive. The former consist of pre-programmed movement sequences triggered by pheromone detections while the latter uses Bayesian inference to build spatial probability maps. Based on the analysis of triphasic responses of antennal lobe neurons (On, inhibition, Off), we propose three reactive strategies. One combines upwind surge (representing the On response to a pheromone detection) and spiral casting, only. The other two additionally include crosswind (zigzag) casting representing the Off phase. As cognitive strategy we use the infotaxis algorithm which was developed for searching in a turbulent medium. Detection events in the electroantennogram of a moth attached to a robot indirectly control this cyborg, depending on the strategy in use. The recorded trajectories are analyzed with regard to success rates, efficiency, and other features. In addition, we qualitatively compare our robotic trajectories to behavioral search paths. Reactive searching is more efficient (yielding shorter trajectories) for higher pheromone doses whereas cognitive searching works better for lower doses. With respect to our experimental conditions (2 m from starting position to pheromone source), reactive searching with crosswind zigzag yields the shortest trajectories (for comparable success rates). Assuming that the neuronal Off response represents a short-term memory, zigzagging is an efficient movement to relocate a recently lost pheromone plume. Accordingly, such reactive strategies offer an interesting alternative to complex cognitive searching.

A Framework for Prototyping of Autonomous Multi-Robot Systems for Search, Rescue, and Reconnaissance

Robots consistently help humans in dangerous and complex tasks by providing information about, and executing tasks in disaster areas that are highly unstructured, uncertain, possibly hostile, and sometimes not reachable to humans directly. Prototyping autonomous multi-robot systems in disaster scenarios both as hardware platforms and software can provide foundational infrastructure in comparing performance of different methodologies developed for search, rescue, monitoring and reconnaissance. In this chapter, the authors discuss prototyping modules of heterogeneous multi-robot networks and their design characteristics for two different scenarios, namely Search and Rescue in unstructured complex environments, and connectivity maintenance in Sycophant Wireless Sensor Networks which are static ecto-parasitic clandestine sensor networks mounted incognito on mobile agents using only the agent’s mobility without intervention, and are cooperating with sparse mobile robot sensor networks.

Decision Making and Finite-Time Motion Control for a Group of Robots

This paper deals with the problem of odor source localization by designing and analyzing a decision-control system (DCS) for a group of robots. In the decision level, concentration magnitude information and wind information detected by robots are used to predict a probable position of the odor source. Specifically, the idea of particle swarm optimization is introduced to give a probable position of the odor source in terms of concentration magnitude information. Moreover, an observation model of the position of the odor source is built according to wind information, and a Kalman filter is used to estimate the position of the odor source, which is combined with the position obtained by using concentration magnitude information in order to make a decision on the position of the odor source. In the control level, two types of the finite-time motion control algorithms are designed; one is a finite-time parallel motion control algorithm, while the other is a finite-time circular motion control algorithm. Precisely, a nonlinear finite-time consensus algorithm is first proposed, and a Lyapunov approach is used to analyze the finite-time convergence of the proposed consensus algorithm. Then, on the basis of the proposed finite-time consensus algorithm, a finite-time parallel motion control algorithm, which can control the group of robots to trace the plume and move toward the probable position of odor source, is derived. Next, a finite-time circular motion control algorithm, which can enable the robot group to circle the probable position of the odor source in order to search for odor clues, is also developed. Finally, the performance capabilities of the proposed DCS are illustrated through the problem of odor source localization.

Identifying rhodamine dye plume sources in near-shore oceanic environments by integration of chemical and visual sensors

This article presents a strategy for identifying the source location of a chemical plume in near-shore oceanic environments where the plume is developed under the influence of turbulence, tides and waves. This strategy includes two modules: source declaration (or identification) and source verification embedded in a subsumption architecture. Algorithms for source identification are derived from the moth-inspired plume tracing strategies based on a chemical sensor. The in-water test missions, conducted in November 2002 at San Clemente Island (California, USA) in June 2003 in Duck (North Carolina, USA) and in October 2010 at Dalian Bay (China), successfully identified the source locations after autonomous underwater vehicles tracked the rhodamine dye plumes with a significant meander over 100 meters. The objective of the verification module is to verify the declared plume source using a visual sensor. Because images taken in near shore oceanic environments are very vague and colors in the images are not well-defined, we adopt a fuzzy color extractor to segment the color components and recognize the chemical plume and its source by measuring color similarity. The source verification module is tested by images taken during the CPT missions.

Collective odor source estimation and search in time-variant airflow environments using mobile robots

This paper addresses the collective odor source localization (OSL) problem in a time-varying airflow environment using mobile robots. A novel OSL methodology which combines odor-source probability estimation and multiple robots' search is proposed. The estimation phase consists of two steps: firstly, the separate probability-distribution map of odor source is estimated via Bayesian rules and fuzzy inference based on a single robot's detection events; secondly, the separate maps estimated by different robots at different times are fused into a combined map by way of distance based superposition. The multi-robot search behaviors are coordinated via a particle swarm optimization algorithm, where the estimated odor-source probability distribution is used to express the fitness functions. In the process of OSL, the estimation phase provides the prior knowledge for the searching while the searching verifies the estimation results, and both phases are implemented iteratively. The results of simulations for large-scale advection-diffusion plume environments and experiments using real robots in an indoor airflow environment validate the feasibility and robustness of the proposed OSL method.

Locating a compact odor source using a four-channel insect electroantennogram sensor

Here we demonstrate the feasibility of using an array of live insects to detect concentrated packets of odor and infer the location of an odor source (∼15 m away) using a backward Lagrangian dispersion model based on the Langevin equation. Bayesian inference allows uncertainty to be quantified, which is useful for robotic planning. The electroantennogram (EAG) is the biopotential developed between the tissue at the tip of an insect antenna and its base, which is due to the massed response of the olfactory receptor neurons to an odor stimulus. The EAG signal can carry tens of bits per second of information with a rise time as short as 12 ms (K A Justice 2005 J. Neurophiol. 93 2233-9). Here, instrumentation including a GPS with a digital compass and an ultrasonic 2D anemometer has been integrated with an EAG odor detection scheme, allowing the location of an odor source to be estimated by collecting data at several downwind locations. Bayesian inference in conjunction with a Lagrangian dispersion model, taking into account detection errors, has been implemented resulting in an estimate of the odor source location within 0.2 m of the actual location.

'Infotaxis' as a strategy for searching without gradients

Chemotactic bacteria rely on local concentration gradients to guide them towards the source of a nutrient. Such local cues pointing towards the location of the source are not always available at macroscopic scales because mixing in a flowing medium breaks up regions of high concentration into random and disconnected patches. Thus, animals sensing odours in air or water detect them only intermittently as patches sweep by on the wind or currents. A macroscopic searcher must devise a strategy of movement based on sporadic cues and partial information. Here we propose a search algorithm, which we call 'infotaxis', designed to work under such conditions. Any search process can be thought of as acquisition of information on source location; for infotaxis, information plays a role similar to concentration in chemotaxis. The infotaxis strategy locally maximizes the expected rate of information gain. We demonstrate its efficiency using a computational model of odour plume propagation and experimental data on mixing flows. Infotactic trajectories feature 'zigzagging' and 'casting' paths similar to those observed in the flight of moths. The proposed search algorithm is relevant to the design of olfactory robots, but the general idea of infotaxis can be applied more broadly in the context of searching with sparse information.

Chemical plume source localization

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.