Chemical Plume Tracing Research Articles

Autonomous Underwater Vehicle Based Chemical Plume Tracing via Deep Reinforcement Learning Methods

This article presents two new chemical plume tracing (CPT) algorithms for using on autonomous underwater vehicles (AUVs) to locate hydrothermal vents. We aim to design effective CPT navigation algorithms that direct AUVs to trace emitted hydrothermal plumes to the hydrothermal vent. Traditional CPT algorithms can be grouped into two categories, including bio-inspired and engineering-based methods, but they are limited by either search inefficiency in turbulent flow environments or high computational costs. To approach this problem, we design a new CPT algorithm by fusing traditional CPT methods. Specifically, two deep reinforcement learning (RL) algorithms, including double deep Q-network (DDQN) and deep deterministic policy gradient (DDPG), are employed to train a customized deep neural network that dynamically combines two traditional CPT algorithms during the search process. Simulation experiments show that both DDQN- and DDPG-based CPT algorithms achieve a high success rate (>90%) in either laminar or turbulent flow environments. Moreover, compared to traditional moth-inspired method, the averaged search time is improved by 67% for the DDQN- and 44% for the DDPG-based CPT algorithms in turbulent flow environments.

Data-Driven Modeling of Spatially Dependent Chemical Plume Tracing Behavior of Insect

Data-Driven Modeling of Spatially Dependent Chemical Plume Tracing Behavior of Insect

Three-degree-of-freedom sampling strategy for chemical plume tracing in the two-dimensional plane

Three-degree-of-freedom sampling strategy for chemical plume tracing in the two-dimensional plane

A novel framework based on a data-driven approach for modelling the behaviour of organisms in chemical plume tracing

We propose a data-driven approach for modelling an organism's behaviour instead of conventional model-based strategies in chemical plume tracing (CPT). CPT models based on this approach show promise in faithfully reproducing organisms' CPT behaviour. To construct the data-driven CPT model, a training dataset of the odour stimuli input toward the organism is needed, along with an output of the organism's CPT behaviour. To this end, we constructed a measurement system comprising an array of alcohol sensors for the measurement of the input and a camera for tracking the output in a real scenario. Then, we determined a transfer function describing the input-output relationship as a stochastic process by applying Gaussian process regression, and established the data-driven CPT model based on measurements of the organism's CPT behaviour. Through CPT experiments in simulations and a real environment, we evaluated the performance of the data-driven CPT model and compared its success rate with those obtained from conventional model-based strategies. As a result, the proposed data-driven CPT model demonstrated a better success rate than those obtained from conventional model-based strategies. Moreover, we considered that the data-driven CPT model could reflect the aspect of an organism's adaptability that modulated its behaviour with respect to the surrounding environment. However, these useful results came from the CPT experiments conducted in simple settings of simulations and a real environment. If making the condition of the CPT experiments more complex, we confirmed that the data-driven CPT model would be less effective for locating an odour source. In this way, this paper not only poses major contributions toward the development of a novel framework based on a data-driven approach for modelling an organism's CPT behaviour, but also displays a research limitation of a data-driven approach at this stage.

Modified Simple Chemical Plume Tracing Algorithm

A simple chemical plume tracing algorithm has been modified and presented in this paper. This algorithm traces the plume based on maximum concentration of chemical/odor cues received by simulated omnidirectional gas sensors. Herein it is proposed to place the sensors in two different arrangements to facilitate the functioning of algorithm. These arrangements of sensors are namely, circular (rosette) type and rectangular, have been proposed to explore its effect on success rate of algorithm. A differential drive mobile robot has been considered around which these sensors are placed to trace the plume. Arena in which plume tracing algorithm has been implemented is a simulated environment based on time averaged Gaussian plume model. Variable wind conditions have been generated to validate the implementation of algorithm. In addition to success rate, number of steps taken to reach the source is taken as an underlying factor of performance measure.

Implementation of the variable capture-angle sampling system to the chemical plume tracing robot

Implementation of the variable capture-angle sampling system to the chemical plume tracing robot

Flicking: variable sampling method for chemical plume tracing by an autonomous robot

Flicking: variable sampling method for chemical plume tracing by an autonomous robot

Experimental Analysis of the Influence of Olfactory Property on Chemical Plume Tracing Performance

In this letter, we investigated the relationship between the performance of chemical plume tracing (CPT) and odor sensing property. Tracking of chemical plumes plays an important role because it facilitates the identification of an odor source. Conventional research has focused on the development of CPT algorithms, whereas the influence of the performance odor sensors on CPT performance has not been investigated. Therefore, in this letter, we first compared the olfactory characteristics of an insect (silkworm moth, Bombyx mori ), which has high CPT performance and the characteristics of an artificial odor sensor. In particular, we focused on and compared the recovery times of the two types of sensors, which plays an important role in the acquisition of odor information. As a result, it was determined that the recovery time for the insect olfactory sensor was ten times faster than that of the artificial odor sensor. We also experimentally evaluated the effect of this difference in the recovery time on CPT performance. CPT experiments using silkworm moths and a robot revealed that there was a correlation between the CPT performance and sensor recovery time. As such, it was demonstrated that it is necessary to improve not only the algorithm but also the sensor recovery time to improve the CPT performance.

Analysis of the role of wind information for efficient chemical plume tracing based on optogenetic silkworm moth behavior

Many animals use olfactory information to search for feeding areas and other individuals in real time and with high efficiency. We focus on the chemical plume tracing (CPT) ability of male silkworm moths and investigate an efficient CPT strategy for an autonomous robot. In the case of flying insects, the wind direction is an important factor in CPT, because the wind carries odors amongst other environmental information. However, whether the same phenomenon occurs in the walking silkworm moth has not been investigated. Therefore, we examine how the silkworm moth uses wind information during CPT. To accurately investigate the response to the wind direction, we introduce an optogenetic approach that replaces the odor stimulation with light stimulation, allowing us to separate the ‘wind stimulus’ from the ‘odor stimulus’. We examine how the moth uses wind direction information in a biological experiment, and find that the movement speed is significantly reduced when the wind speed is relatively fast (1.0 m s−1). By implementing this phenomenon in an autonomous robot, we can improve the successful search rate over that of the conventional moth-inspired algorithm. Regarding the search time, the proposed algorithm finds the odor source faster in a low-frequency odorant emission environment, whereas the search is slower than the conventional method when the odor frequency is higher. Therefore, switching from the use of wind direction information to odor information according to the frequency with which the odor is encountered leads to efficient CPT performance.

Modeling of the Adaptive Chemical Plume Tracing Algorithm of an Insect Using Fuzzy Inference

In this paper, we focus on the chemical plume tracing (CPT) problem, which is a known engineering challenge. In nature, animals solve the CPT by adaptively modifying their behavior according to the environment. Therefore, we propose a CPT solution method with high engineering value by modeling the CPT algorithm of an animal. In this paper, we consider a male silkworm moth as a model. To perform CPT in a turbulent environment, the adaptive selection of the behavior plays an important role. Therefore, we performed simultaneous measurement experiments involving CPT behavior of the brain and analyzed the links between the brain's neural activity and behavioral patterns. We measured the brain's neural response in the lateral accessory lobe (LAL), which generates motion commands. We employed fuzzy inference to analyze the relationship between CPT behavior and LAL neural activity. As a result of analyzing the relationship between CPT behavior and LAL, we found that the moth modulates the behavior of the state transition probability depending on the odor frequency. We modeled the obtained phenomenon and verified its effectiveness through a constructive method. As a result, the search performance was improved compared to the conventional moth algorithm.

Underwater chemical plume tracing based on partially observable Markov decision process

Chemical plume tracing based on autonomous underwater vehicle uses chemical as a guidance to navigate and search in the unknown environments. To solve the key issue of tracing and locating the source, this article proposes a path-planning strategy based on partially observable Markov decision process algorithm and artificial potential field algorithm. The partially observable Markov decision process algorithm is used to construct a source likelihood map and update it in real time with environmental information from the sensors on autonomous underwater vehicle in search area. The artificial potential field algorithm uses the source likelihood map for accurately planning tracing path and guiding the autonomous underwater vehicle to track along the path until the source is detected. This article carries out simulation experiments on the proposed algorithm. The experimental results show that the algorithms have good performance, which is suitable for chemical plume tracing via autonomous underwater vehicle. Compared with the bionic method, the simulation results show that the proposed method has higher success rate and better stability than the bionic method.

High-fidelity simulations of Chemical Plume Tracing in the planetary boundary layer

The localization of a source emitting gas to the outdoor atmosphere has many applications for air quality monitoring and chemical hazard mitigation. Chemical Plume Tracing (CPT) refers to the utilization of autonomous robotic platforms equipped with chemical and anemometry sensors to preform this task. Numerical simulations are an important tool in the development of CPT algorithms. Here, for the first time, high fidelity environmental simulations of the Atmospheric Boundary Layer (ABL) are utilized for the study of CPT algorithm. Large Eddy Simulation (LES) modeling using the PALM model is applied for producing high-resolution three-dimensional flow and dispersion simulations of the outdoor ABL. A CPT bio-inspired algorithm is simulated on top of the modeled environment. Multiple tests are conducted, following statistical analysis of the overall performance. The advantage of the suggested modeling framework over former used methods is exemplified through the study of two issues. First, a theoretical optimal velocity of the robotic platform is quantitatively estimated. Secondly, based on the three-dimensional nature of LES modeling, an extension of the CPT algorithm, aimed for the localization of elevated sources, is presented and tested. The comparison of CPT performance for elevated versus ground sources reveals the considerable difficulty involved in tracing an elevated plume.

Design and Experimental Evaluation of an Odor Sensing Method for a Pocket-Sized Quadcopter.

In this study, we design and verify an intake system using the wake of a pocket-sized quadcopter for the chemical plume tracing (CPT) problem. Solving CPT represents an important technique in the field of engineering because it can be used to perform rescue operations at the time of a disaster and to identify sources of harmful substances. An appropriate intake of air when sensing odors plays an important role in performing CPT. Hence, we used the air flow generated by a quadcopter itself to intake chemical particles into two alcohol sensors. By experimental evaluation, we verified that the quadcopter wake intake method has good directivity and can be used to realize CPT. Concretely, even at various odor source heights, the quadcopter had a three-dimensional CPT success rate of at least 70%. These results imply that, although a further development of three-dimensional CPT is necessary in order to conduct it in unknown and cluttered environments, the intake method proposed in this paper enables a pocket-sized quadcopter to perform three-dimensional CPT.

Time-Varying Moth-Inspired Algorithm for Chemical Plume Tracing in Turbulent Environment

In this study, we propose a robust chemical plume tracing (CPT) algorithm for various environments. We developed this algorithm based on the behavior of insects that have CPT abilities globally. However, it is difficult to accurately estimate the behavior pattern from trajectory data because the trajectory contains a high level of measurement noise. We employed flight muscle electromyograms, whose motor commands were assumed to be generated in the same ganglion as the behavior, to estimate the behavior pattern using a support vector machine. By using the estimated results, we modeled the time-varying behavioral change of insects and verified the effectiveness of the phenomenon for CPT using the constructive approach. We named this time-varying CPT algorithm as time-varying moth-inspired algorithm. From the CPT experiment results using the robot, we found that the localization success rate improved by changing the behavior in a time-variant manner.

Chemical plume tracing

Most of the reported three-dimensional chemical plume tracing methods use stereo sensing method to determine the next tracing step direction. For example, multiple sensors are used for detection in the left, right, up and down directions. Left and right detections are feasible for stereo sniffing; unfortunately, the same approach is infeasible for the up/down sensing of the quadrotor platforms because the propellers of the quadrotor continuously draw the air from the top and bring it down, which affects the sensing of the upper and lower sensors, and fails to determine the subsequent tracing step of up/down direction. Therefore, up/down sensing in the surging stage of chemical plume tracing is ineffective for quadrotor platforms (chemical plume tracing has two stages: surging and casting). To solve the problem, we propose an alternative that is not in the surging but in the casting stage of chemical plume tracing, by designing a new three-dimensional chemical plume tracing technique with variations of altitude ( z-axis) control during the casting stage, which has never been considered in the previous works. Besides, we use a computational fluid dynamics software to study the airflow pattern of quadrotor platform. Subsequently, a fuzzy-based stereo-sniffing algorithm is developed by considering the quadrotor propeller’s air intake stream angle associated with the environmental wind direction angle, so as to improve the accuracy of stereo sensing. The results of the proposed solutions are verified and validated via both experimental and simulation approaches.

A novel method for full locomotion compensation of an untethered walking insect

In this study, we developed a novel unfixed-type experimental system that we call a ‘3-DOF servosphere.’ This system comprises one sphere and three omniwheels that support the sphere. The measurement method is very simple. An experimental animal is placed on top of the sphere. The position and heading angle of the animal are observed by using a high-speed camera installed above the sphere. Because the system can rotate the sphere with three degrees of freedom (DOFs) independently, the position and heading angle at the origin can be maintained without fixing the body. This system can be used to measure an animal's natural behavior while simultaneously providing it with precise stimuli. Moreover, electrodes can be inserted at specific sites to measure biosignals with locomotion. Therefore, this system can simultaneously measure the stimulus input-internal state-locomotion output of an animal. In this study, we focused on the chemical plume tracing (CPT) behavior of the Bombyx mori male silkworm moth in order to identify its CPT algorithm for mounting on a robot. In an experiment, we simultaneously measured the stimulus input, flight muscle electromyogram (EMG), and CPT behavior by using the 3-DOF servosphere to verify the system. We elucidated the relationship between the CPT behavior and flight muscle EMG.

Learning to rapidly re-contact the lost plume in chemical plume tracing.

Maintaining contact between the robot and plume is significant in chemical plume tracing (CPT). In the time immediately following the loss of chemical detection during the process of CPT, Track-Out activities bias the robot heading relative to the upwind direction, expecting to rapidly re-contact the plume. To determine the bias angle used in the Track-Out activity, we propose an online instance-based reinforcement learning method, namely virtual trail following (VTF). In VTF, action-value is generalized from recently stored instances of successful Track-Out activities. We also propose a collaborative VTF (cVTF) method, in which multiple robots store their own instances, and learn from the stored instances, in the same database. The proposed VTF and cVTF methods are compared with biased upwind surge (BUS) method, in which all Track-Out activities utilize an offline optimized universal bias angle, in an indoor environment with three different airflow fields. With respect to our experimental conditions, VTF and cVTF show stronger adaptability to different airflow environments than BUS, and furthermore, cVTF yields higher success rates and time-efficiencies than VTF.

2A1-G01 匂い源探索のためのマルチコプタシステムの構築

Chemical Plume Tracing (CPT), locating the distant odor source in a turbulent environment, has been studied for robotics odor source search. Many robots moving on the ground are designed. In real-world, however, obstacles on the ground prevent the robots from approaching odor source. Flying CPT robots can approach odor source even if obstacles are on the ground, so design of the the flying robots are needed. We propose flying CPT robot system which can inhale odor ahead of the robot and detect the odor. This system is installed CPT algorithm of 2-dimension and the flying robot can locate the distant odor source under constant altitude condition.

Autonomous tracking of chemical plumes developed in both diffusive and turbulent airflow environments using Petri nets

This paper presents a Petri net model for autonomous tracking of chemical plumes developed in both diffusive and turbulent airflow environments. It has been challenging to develop a generalized algorithm to effectively trace both types of chemical plumes due to the significant differences of their kinetic and dynamic properties. Our idea is to utilize a Petri net to model the change relationships of chemical concentrations acquired by two sensors mounted on the both sides of a DaNI robot during a tracing process. Because the relationships imply the effects of flow variation on chemical puffs, a flow sensor is eliminated. To express and maintain the knowledge of chemical concentration changes using the Petri net, we design a mapping algorithm for generating the Petri net from production rules. The Petri net model is implemented on the robot using LabVIEW. The chemical plume tracing experiments achieve 93.8% and 87.5% of source localization rates under both turbulent and diffusive airflow environments, respectively.

Quantitative Analysis of the Silk Moth's Chemical Plume Tracing Locomotion Using a Hierarchical Classification Method

AbstractThe silk moth (Bombyx mori) exhibits efficient Chemical Plume Tracing (CPT), which is ideal for biomimetics. However, there is insufficient quantitative understanding of its CPT behavior. We propose a hierarchical classification method to segment its natural CPT locomotion and to build its inverse model for detecting stimulus input. This provides the basis for quantitative analysis. The Gaussian mixture model with expectation–maximization algorithm is used first for unsupervised classification to decompose CPT locomotion data into Gaussian density components that represent a set of quantified elemental motions. A heuristic behavioral rule is used to categorize these components to eliminate components that are descriptive of the same motion. Then, the echo state network is used for supervised classification to evaluate segmented elemental motions and to compare CPT locomotion among different moths. In this case, categorized elemental motions are used as the training data to estimate stimulus time. We successfully built the inverse CPT behavioral model of the silk moth to detect stimulus input with good accuracy. The quantitative analysis indicates that silk moths exhibit behavioral singularity and time dependency in their CPT locomotion, which is dominated by its singularity.