Odor Source Localization Algorithm Research Articles

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.

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.

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization

Small drones with chemical or biosensor devices that can detect airborne odorant molecules have attracted considerable attention owing to their applicability in environmental and security monitoring and search-and-rescue operations. Small drones with commercial metal-oxide-semiconductor (MOX) gas sensors have been developed for odor source localization; however, their real-time-odor-detection performance has proven inadequate. However, biosensing technologies based on insect olfactory systems exhibit relatively high sensitivity, selectivity, and real-time response with respect to odorant molecules compared to commercial MOX gas sensors. In such devices, excised insect antennae function as portable odorant biosensor elements and have been found to deliver excellent sensing performance. This study presents experimental protocols for odorant-molecule detection in the air using a small autonomous bio-hybrid drone based on a mountable electroantennography (EAG) device incorporating silkmoth antennae. We developed a mountable EAG device including sensing/processing parts with a Wi-Fi module. The device was equipped with a simple sensor enclosure to enhance the sensor directivity. Thus, odor source localization was conducted using the spiral-surge algorithm, which does not assume an upwind direction. The experimental bio-hybrid odor-detecting drone identified real-time odorant-concentration differences in a pseudo-open environment (outside a wind tunnel) and localized the source. The developed drone and associated system can serve as an efficient odorant molecule-detection tool and a suitable flight platform for developing odor source localization algorithms owing to its high programmability.

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization

Small drones with chemical or biosensor devices that can detect airborne odorant molecules have attracted considerable attention owing to their applicability in environmental and security monitoring and search-and-rescue operations. Small drones with commercial metal-oxide-semiconductor (MOX) gas sensors have been developed for odor source localization; however, their real-time-odor-detection performance has proven inadequate. However, biosensing technologies based on insect olfactory systems exhibit relatively high sensitivity, selectivity, and real-time response with respect to odorant molecules compared to commercial MOX gas sensors. In such devices, excised insect antennae function as portable odorant biosensor elements and have been found to deliver excellent sensing performance. This study presents experimental protocols for odorant-molecule detection in the air using a small autonomous bio-hybrid drone based on a mountable electroantennography (EAG) device incorporating silkmoth antennae. We developed a mountable EAG device including sensing/processing parts with a Wi-Fi module. The device was equipped with a simple sensor enclosure to enhance the sensor directivity. Thus, odor source localization was conducted using the spiral-surge algorithm, which does not assume an upwind direction. The experimental bio-hybrid odor-detecting drone identified real-time odorant-concentration differences in a pseudo-open environment (outside a wind tunnel) and localized the source. The developed drone and associated system can serve as an efficient odorant molecule-detection tool and a suitable flight platform for developing odor source localization algorithms owing to its high programmability.

A Deep Q-Network for robotic odor/gas source localization: Modeling, measurement and comparative study

Robotic odor/gas source localization is a widely studied field, but most of the existing works are about rule-based algorithms. In this paper, the Deep Q-Network algorithm is applied to solve the odor source localization problem. An odor hits distribution model is proposed to model the odor concentration distribution in indoor environments, taking the dispersion by airflow, the odor molecular random walk, and the obstacles into account. The Deep Q-Network takes the stacked historic measurement data as the input and outputs the expected cumulative future reward of actions of the robots. The network is trained through 35,000 repeated episodes of random odor source localization tasks. The Deep Q-Network method is evaluated under four different environment settings in a simplified simulator and compared with two widely used odor source localization algorithms. The evaluation results demonstrate the advantages of the proposed algorithm. The algorithm is also validated in more complex indoor environments.

Particle Source Localization With a Low-Cost Robotic Sensor System: Algorithmic Design and Performance Evaluation

In this paper we tackle the problem of finding the source of particulate matter with a mobile robot equipped with a low-cost multi-channel optical particle counting sensor. The proposed method is based on the Infotaxis odor source localization algorithm and makes multiple modifications to adapt it to particle plumes. In particular, we propose three simple but efficient ways to fuse multiple probability maps associated with various particle sizes and use the resulted integrated map to guide the robot’s movements. A refined measurement data collection is conducted in a wind tunnel to fit the particle plume model. The method with three proposed integration strategies is evaluated in simulation and in the wind tunnel emulating realistic environmental conditions in a repeatable fashion. In particular, we have investigated the impact of two environmental parameters - the wind speed and source release rate on the algorithm performance. The proposed algorithm with the weighted multi-modality map integration strategy outperforms the original Infotaxis and the other two variants. In high wind speed, the proposed algorithm is able on average to estimate the source location with less than 1 meter error in the $80~{m}^{2}$ wind tunnel arena.

Leakage source location based on Gaussian plume diffusion model using a near-infrared sensor

Developing sensitive sensors to measure trace gas concentration levels is important for environmental monitoring. Leakage source location based on measured concentration levels to prevent potential danger is of significance. A near-infrared methane (CH4) sensor system with a modulation frequency of 4 kHz targeting the absorption line at 6046.95 cm−1 and an Allan deviation of 850 parts-per-billion by volume (ppbv) was employed for leakage source location. A static odor source localization algorithm based on the Gauss plume diffusion model and a least square method is proposed. Formulations and equations of leakage location were deduced. An iterative search program was used to obtain the optimal solution. Simulation result showed the synthetic location error of 7.91%, which was close to the experimental result of 8.93%. Feasibility of the location algorithm was verified through the comparison.

Veco-Taxis as a Novel Engineered Algorithm for Odor Source Localization

Algorithms with limited intelligence are unable to localize an odor source in an indoor environment with weak or no airflow. Stage wise solutions to odor source localization has been provided with a novel engineered algorithm called veco-taxis for plume traversal. It finds turn angles by calculating concentration gradients using vector algebra-based search algorithms. Levy walk is used in the plume finding phase. The concept of last chemical detection points (LCDPs) has been adopted for source declaration. The success rate of implemented algorithms is quantified using minimum and maximum move lengths—a key parameter—during source localization. A unified success and performance index (SPI) of the search algorithm is presented for the first time. SPI uncovers implicit parameters accountable for success in locating source and considers a qualitative performance. Higher SPIs are observed when the move length in plume finding is minimum and kept smaller than the plume traversal move length by some factor. It has been also demonstrated through simulations that veco-taxis is superior to the E. coli algorithm.

Scope of Improvement in Algorithm for Odor Source Localization in an Indoor Dynamic Environment: A Preliminary Study

Scope of Improvement in Algorithm for Odor Source Localization in an Indoor Dynamic Environment: A Preliminary Study

A Robot Odor Source Localization Strategy Based on Bionic Behavior

In this paper, a robot bionic odor source localization algorithm based on kinesis response and tropotaxis behavior of flatworm is proposed, the algorithm does not need to detect wind direction information and odor absolute concentration information. Experiments show that the proposed algorithm is not affected by both wind direction and wind speed, only by judging the rate of change of odor concentration, which improves the searching efficiency, has good stability and environmental adaptability. it is a reliable initiative olfactory strategy with simple, search speed, high efficiency advantages.

Odor source localization algorithms on mobile robots: A review and future outlook

When applied in some harsh environments (e.g. in poisonous atmosphere or underwater), odor source localization robots are able to perform better than animals without being hurt. During the past three decades, robotic odor source localization has become a popular research field with various algorithms being proposed. These algorithms can be roughly divided into four categories: gradient-based algorithms, bio-inspired algorithms, multi-robot algorithms and probabilistic and map-based algorithms. In this paper, we present a literature review of these four categories and discuss their pros and cons. We also discuss the current trends and some future challenges according to some research papers published in recent years.

Synthesis of odor tracking algorithms with genetic programming

At the moment, smell sensors for odor source localization in mobile robotics represent a topic of interest for researchers around the world. In particular, we introduce in this paper the idea of developing biologically inspired sniffing robots in combination with bioinspired techniques such as evolutionary computing. The aim is to approach the problem of creating an artificial nose that can be incorporated into a real working system, while considering the environmental model and odor behavior, the perception system, and algorithm for tracking the odor plume. Current algorithms try to emulate animal behavior in an attempt to replicate their capability to follow odors. Nevertheless, odor perception systems are still in their infancy and far from their biological counterpart. This paper presents a proposal in which a real-working artificial nose is tested as a perception system within a mobile robot. Genetic programming is used as the learning technique platform to develop odor source localization algorithms. Experiments in simulation and with an actual working robot are presented and the results compared with two algorithms. The quality of results demonstrates that genetic programming is able to recreate chemotaxis behavior by considering mathematical models for odor propagation and perception system.

A finite-time particle swarm optimization algorithm for odor source localization

This paper is concerned with a finite-time particle swarm optimization algorithm for odor source localization. First, a continuous-time finite-time particle swarm optimization (FPSO) algorithm is developed based on the continuous-time model of the particle swarm optimization (PSO) algorithm. Since the introduction of a nonlinear damping item, the proposed continuous-time FPSO algorithm can converge over a finite-time interval. Furthermore, in order to enhance its exploration capability, a tuning parameter is introduced into the proposed continuous-time FPSO algorithm. The algorithm’s finite-time convergence is analyzed by using the Lyapunov approach. Second, the discrete-time FPSO algorithm is obtained by using a given dicretization scheme. The corresponding convergence condition is derived by using a linear matrix inequality (LMI) approach. Finally, the features and performance of the proposed FPSO algorithm are illustrated by using two ill-posed functions and twenty-five benchmark functions, respectively. In numerical simulation results, the problem of odor source localization is presented to validate the effectiveness of the proposed FPSO algorithm.

Odor Source Localization Research of Mobile Robots in Indoor Environments

Aiming at the odor source localization in an obstacle-filled wind-varying indoor environment, a new method based odor source localization algorithm for a single mobile robot is proposed. With the information of the wind and the concentration gradient, Wasps can find odor source in a short time. However, it is very difficult for mobile robots to mimic the behaviors of wasps exactly. So, besides the bionics, BP neural network is adopted for the mobile robot to find the odor source. The control strategies for the plume-tracing mobile robot are proposed which include the intelligent plume-tracing algorithm and the collision avoidance algorithm based on improved potential grid method. The algorithms were integrated to control the robot trace plumes in obstructed indoor environments. Experimental results have demonstrated the capability of this kind of plume-tracing mobile robot.

Single-Robot Odor Source Localization in a Ventilated Indoor Environment Using Simulated Annealing Algorithm

Aiming at the odor source localization in a ventilated indoor environment,a simulated annealing based odor source localization algorithm for a single robot is proposed.Dominated by the flow field,the plume released from an odor source in the room is meandering and intermittent.Moreover,the local concentration maxima may appear in the eddy areas. In this paper,the odor source localization is regarded as a dynamic function optimization problem.The simulated annealing strategy is used to obtain the optimal solution of the concentration distribution function,namely the location of the odor source.The algorithm doesn't need the wind information,so the influence of the flow-field fluctuation is reduced.The relationship between odor concentration and odor-source distance is also studied,and an objective function for simulated annealing that is approximately linear with the odor source distance is presented.The experiments in an actual ventilated indoor environment show that the robot using the proposed method can trace the dynamic odor plume and eventually declare the odor source in an 8 m×6 m region,and the average localization time is about 10 min.In the search process,the robot is able to jump out of the local maxima effectively.

A learning particle swarm optimization algorithm for odor source localization

This paper is concerned with the problem of odor source localization using multi-robot system. A learning particle swarm optimization algorithm, which can coordinate a multi-robot system to locate the odor source, is proposed. First, in order to develop the proposed algorithm, a source probability map for a robot is built and updated by using concentration magnitude information, wind information, and swarm information. Based on the source probability map, the new position of the robot can be generated. Second, a distributed coordination architecture, by which the proposed algorithm can run on the multi-robot system, is designed. Specifically, the proposed algorithm is used on the group level to generate a new position for the robot. A consensus algorithm is then adopted on the robot level in order to control the robot to move from the current position to the new position. Finally, the effectiveness of the proposed algorithm is illustrated for the odor source localization problem.

Odor source localization using a mobile robot in outdoor airflow environments with a particle filter algorithm

This paper discusses odor source localization (OSL) using a mobile robot in an outdoor time-variant airflow environment. A novel OSL algorithm based on particle filters (PF) is proposed. When the odor plume clue is found, the robot performs an exploratory behavior, such as a plume-tracing strategy, to collect more information about the previously unknown odor source. In parallel, the information collected by the robot is exploited by the PF-based OSL algorithm to estimate the location of the odor source in real time. The process of the OSL is terminated if the estimated source locations converge within a given small area. The Bayesian-inference-based method is also performed for comparison. Experimental results indicate that the proposed PF-based OSL algorithm performs better than the Bayesian-inference-based OSL method.

An Insect-sized Atmospheric Ion Source Localization Robot for the Evaluation of Odor Source Localization Algorithms of Insects

An Insect-sized Atmospheric Ion Source Localization Robot for the Evaluation of Odor Source Localization Algorithms of Insects