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Mobile Robot Olfaction for Real-World Applications–From Disaster Response to Environmental Monitoring

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (10 December 2018) | Viewed by 67113

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Prof. Dr. Achim J. Lilienthal

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AASS Mobile Robotics and Olfaction Lab, Örebro University, 702 81 Örebro, Sweden
Interests: robot perception; human–robot interaction; maps of dynamics; long-term human motion prediction; mobile robot olfaction
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Special Issue Information

Dear Colleagues,

Mobile Robot Olfaction, the research of combining intelligent mobile robots with an artificial sense of smell, has made tremendous progress in the last decade. Important developments in sensor and robot technology, as well as intelligent data processing (fueled by the promising progress in AI/Machine Learning) present the prospect of a wide variety of practical applications. In such applications, gas sensors may be used as one modality on a single robot, a robot team, or as a mobile (robotic) node in a heterogeneous sensor network. On one end of the spectrum are applications in immediate disaster response, where a high degree of mobility, fast operation, and highly-efficient collaboration with human operators and decision makers is crucial and only ad hoc sensor networks are available, if at all. On the other end of the spectrum are long-term environmental monitoring campaigns where response times are often less critical and stationary sensor networks and other permanent infrastructure may exist.

This Special Issue focuses on contributions towards real-world applications of “Mobile Robot Olfaction”. Papers should address how robotic systems, perceptual algorithms, chemical sensors, or approaches to sensor fusion, decision support, human-robot interaction or adaptive sensor planning deal with real-world conditions, e.g., with limited control of the environment, open sampling processes, continuous measurements, rapidly fluctuating concentration levels, turbulent gas dispersal, etc. Papers including prototype demonstrations in relevant real-world scenarios are particularly welcome.

Prof. Dr. Achim J. Lilienthal
Guest Editor

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Keywords

Mobile Robot Olfaction
Chemical sensors
Electronic Nose or e-nose
MOX
Open sampling system
Gas sensing
In situ gas sensing
Remote gas sensing
Sensor networks
Gas dispersal
Gas detection
Gas discrimination
Gas distribution mapping
Gas source localization
Robot exploration
Sensor planning
Artificial Intelligence
Machine Learning

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Published Papers (11 papers)

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Research

34 pages, 2022 KiB

Open AccessArticle

A Comparative Study of Bio-Inspired Odour Source Localisation Strategies from the State-Action Perspective

by João Macedo, Lino Marques and Ernesto Costa

Sensors 2019, 19(10), 2231; https://doi.org/10.3390/s19102231 - 14 May 2019

Cited by 29 | Viewed by 3418

Abstract

Locating odour sources with robots is an interesting problem with many important real-world applications. In the past years, the robotics community has adapted several bio-inspired strategies to search for odour sources in a variety of environments. This work studies and compares some of the most common strategies from a behavioural perspective with the aim of knowing: (1) how different are the behaviours exhibited by the strategies for the same perceptual state; and (2) which are the most consensual actions for each perceptual state in each environment. The first step of this analysis consists of clustering the perceptual states, and building histograms of the actions taken for each cluster. In case of (1), a histogram is made for each strategy separately, whereas for (2), a single histogram containing the actions of all strategies is produced for each cluster of states. Finally, statistical hypotheses tests are used to find the statistically significant differences between the behaviours of the strategies in each state. The data used for performing this study was gathered from a purpose-built simulator which accurately simulates the real-world phenomena of odour dispersion and air flow, whilst being sufficiently fast to be employed in learning and evolutionary robotics experiments. This paper also proposes an xml-inspired structure for the generated datasets that are used to store the perceptual information of the robots over the course of the simulations. These datasets may be used in learning experiments to estimate the quality of a candidate solution or for measuring its novelty. Full article

(This article belongs to the Special Issue Mobile Robot Olfaction for Real-World Applications–From Disaster Response to Environmental Monitoring)

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21 pages, 3528 KiB

Open AccessArticle

Scalable Gas Sensing, Mapping, and Path Planning via Decentralized Hilbert Maps

by Pingping Zhu, Silvia Ferrari, Julian Morelli, Richard Linares and Bryce Doerr

Sensors 2019, 19(7), 1524; https://doi.org/10.3390/s19071524 - 28 Mar 2019

Cited by 11 | Viewed by 4584

Abstract

This paper develops a decentralized approach to gas distribution mapping (GDM) and information-driven path planning for large-scale distributed sensing systems. Gas mapping is performed using a probabilistic representation known as a Hilbert map, which formulates the mapping problem as a multi-class classification task and uses kernel logistic regression to train a discriminative classifier online. A novel Hilbert map information fusion method is presented for rapidly merging the information from individual robot maps using limited data communication. A communication strategy that implements data fusion among many robots is also presented for the decentralized computation of GDMs. New entropy-based information-driven path-planning methods are developed and compared to existing approaches, such as particle swarm optimization (PSO) and random walks (RW). Numerical experiments conducted in simulated indoor and outdoor environments show that the information-driven approaches proposed in this paper far outperform other approaches, and avoid mutual collisions in real time. Full article

(This article belongs to the Special Issue Mobile Robot Olfaction for Real-World Applications–From Disaster Response to Environmental Monitoring)

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21 pages, 6309 KiB

Open AccessArticle

Multi-Domain Airflow Modeling and Ventilation Characterization Using Mobile Robots, Stationary Sensors and Machine Learning

by Victor Hernandez Bennetts, Kamarulzaman Kamarudin, Thomas Wiedemann, Tomasz Piotr Kucner, Sai Lokesh Somisetty and Achim J. Lilienthal

Sensors 2019, 19(5), 1119; https://doi.org/10.3390/s19051119 - 5 Mar 2019

Cited by 3 | Viewed by 4563

Abstract

Ventilation systems are critically important components of many public buildings and workspaces. Proper ventilation is often crucial for preventing accidents, such as explosions in mines and avoiding health issues, for example, through long-term exposure to harmful respirable matter. Validation and maintenance of ventilation systems is thus of key interest for plant operators and authorities. However, methods for ventilation characterization, which allow us to monitor whether the ventilation system in place works as desired, hardly exist. This article addresses the critical challenge of ventilation characterization—measuring and modelling air flow at micro-scales—that is, creating a high-resolution model of wind speed and direction from airflow measurements. Models of the near-surface micro-scale flow fields are not only useful for ventilation characterization, but they also provide critical information for planning energy-efficient paths for aerial robots and many applications in mobile robot olfaction. In this article we propose a heterogeneous measurement system composed of static, continuously sampling sensing nodes, complemented by localized measurements, collected during occasional sensing missions with a mobile robot. We introduce a novel, data-driven, multi-domain airflow modelling algorithm that estimates (1) fields of posterior distributions over wind direction and speed (“ventilation maps”, spatial domain); (2) sets of ventilation calendars that capture the evolution of important airflow characteristics at measurement positions (temporal domain); and (3) a frequency domain analysis that can reveal periodic changes of airflow in the environment. The ventilation map and the ventilation calendars make use of an improved estimation pipeline that incorporates a wind sensor model and a transition model to better filter out sporadic, noisy airflow changes. These sudden changes may originate from turbulence or irregular activity in the surveyed environment and can, therefore, disturb modelling of the relevant airflow patterns. We tested the proposed multi-domain airflow modelling approach with simulated data and with experiments in a semi-controlled environment and present results that verify the accuracy of our approach and its sensitivity to different turbulence levels and other disturbances. Finally, we deployed the proposed system in two different real-world industrial environments (foundry halls) with different ventilation regimes for three weeks during full operation. Since airflow ground truth cannot be obtained, we present a qualitative discussion of the generated airflow models with plant operators, who concluded that the computed models accurately depicted the expected airflow patterns and are useful to understand how pollutants spread in the work environment. This analysis may then provide the basis for decisions about corrective actions to avoid long-term exposure of workers to harmful respirable matter. Full article

(This article belongs to the Special Issue Mobile Robot Olfaction for Real-World Applications–From Disaster Response to Environmental Monitoring)

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28 pages, 5949 KiB

Open AccessArticle

Towards Gas Discrimination and Mapping in Emergency Response Scenarios Using a Mobile Robot with an Electronic Nose

by Han Fan, Victor Hernandez Bennetts, Erik Schaffernicht and Achim J. Lilienthal

Sensors 2019, 19(3), 685; https://doi.org/10.3390/s19030685 - 7 Feb 2019

Cited by 40 | Viewed by 6305

Abstract

Emergency personnel, such as firefighters, bomb technicians, and urban search and rescue specialists, can be exposed to a variety of extreme hazards during the response to natural and human-made disasters. In many of these scenarios, a risk factor is the presence of hazardous airborne chemicals. The recent and rapid advances in robotics and sensor technologies allow emergency responders to deal with such hazards from relatively safe distances. Mobile robots with gas-sensing capabilities allow to convey useful information such as the possible source positions of different chemicals in the emergency area. However, common gas sampling procedures for laboratory use are not applicable due to the complexity of the environment and the need for fast deployment and analysis. In addition, conventional gas identification approaches, based on supervised learning, cannot handle situations when the number and identities of the present chemicals are unknown. For the purpose of emergency response, all the information concluded from the gas detection events during the robot exploration should be delivered in real time. To address these challenges, we developed an online gas-sensing system using an electronic nose. Our system can automatically perform unsupervised learning and update the discrimination model as the robot is exploring a given environment. The online gas discrimination results are further integrated with geometrical information to derive a multi-compound gas spatial distribution map. The proposed system is deployed on a robot built to operate in harsh environments for supporting fire brigades, and is validated in several different real-world experiments of discriminating and mapping multiple chemical compounds in an indoor open environment. Our results show that the proposed system achieves high accuracy in gas discrimination in an online, unsupervised, and computationally efficient manner. The subsequently created gas distribution maps accurately indicate the presence of different chemicals in the environment, which is of practical significance for emergency response. Full article

(This article belongs to the Special Issue Mobile Robot Olfaction for Real-World Applications–From Disaster Response to Environmental Monitoring)

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21 pages, 4286 KiB

Open AccessArticle

Design and Performance Evaluation of an Algorithm Based on Source Term Estimation for Odor Source Localization

by Faezeh Rahbar, Ali Marjovi and Alcherio Martinoli

Sensors 2019, 19(3), 656; https://doi.org/10.3390/s19030656 - 5 Feb 2019

Cited by 17 | Viewed by 4061

Abstract

Finding sources of airborne chemicals with mobile sensing systems finds applications across safety, security, environmental monitoring, and medical domains. In this paper, we present an algorithm based on Source Term Estimation for odor source localization that is coupled with a navigation method based on partially observable Markov decision processes. We propose a novel strategy to balance exploration and exploitation in navigation. Moreover, we study two variants of the algorithm, one exploiting a global and the other one a local framework. The method was evaluated through high-fidelity simulations and in a wind tunnel emulating a quasi-laminar air flow in a controlled environment, in particular by systematically investigating the impact of multiple algorithmic and environmental parameters (wind speed and source release rate) on the overall performance. The outcome of the experiments showed that the algorithm is robust to different environmental conditions in the global framework, but, in the local framework, it is only successful in relatively high wind speeds. In the local framework, on the other hand, the algorithm is less demanding in terms of energy consumption as it does not require any absolute positioning information from the environment and the robot travels less distance compared to the global framework. Full article

(This article belongs to the Special Issue Mobile Robot Olfaction for Real-World Applications–From Disaster Response to Environmental Monitoring)

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24 pages, 1376 KiB

Open AccessArticle

Analysis of Model Mismatch Effects for a Model-Based Gas Source Localization Strategy Incorporating Advection Knowledge

by Thomas Wiedemann, Achim J. Lilienthal and Dmitriy Shutin

Sensors 2019, 19(3), 520; https://doi.org/10.3390/s19030520 - 26 Jan 2019

Cited by 14 | Viewed by 4093

Abstract

In disaster scenarios, where toxic material is leaking, gas source localization is a common but also dangerous task. To reduce threats for human operators, we propose an intelligent sampling strategy that enables a multi-robot system to autonomously localize unknown gas sources based on gas concentration measurements. This paper discusses a probabilistic, model-based approach for incorporating physical process knowledge into the sampling strategy. We model the spatial and temporal dynamics of the gas dispersion with a partial differential equation that accounts for diffusion and advection effects. We consider the exact number of sources as unknown, but assume that gas sources are sparsely distributed. To incorporate the sparsity assumption we make use of sparse Bayesian learning techniques. Probabilistic modeling can account for possible model mismatch effects that otherwise can undermine the performance of deterministic methods. In the paper we evaluate the proposed gas source localization strategy in simulations using synthetic data. Compared to real-world experiments, a simulated environment provides us with ground truth data and reproducibility necessary to get a deeper insight into the proposed strategy. The investigation shows that (i) the probabilistic model can compensate imperfect modeling; (ii) the sparsity assumption significantly accelerates the source localization; and (iii) a-priori advection knowledge is of advantage for source localization, however, it is only required to have a certain level of accuracy. These findings will help in the future to parameterize the proposed algorithm in real world applications. Full article

(This article belongs to the Special Issue Mobile Robot Olfaction for Real-World Applications–From Disaster Response to Environmental Monitoring)

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25 pages, 10669 KiB

Open AccessEditor’s ChoiceArticle

Smelling Nano Aerial Vehicle for Gas Source Localization and Mapping

by Javier Burgués, Victor Hernández, Achim J. Lilienthal and Santiago Marco

Sensors 2019, 19(3), 478; https://doi.org/10.3390/s19030478 - 24 Jan 2019

Cited by 105 | Viewed by 16439

Abstract

This paper describes the development and validation of the currently smallest aerial platform with olfaction capabilities. The developed Smelling Nano Aerial Vehicle (SNAV) is based on a lightweight commercial nano-quadcopter (27 g) equipped with a custom gas sensing board that can host up to two in situ metal oxide semiconductor (MOX) gas sensors. Due to its small form-factor, the SNAV is not a hazard for humans, enabling its use in public areas or inside buildings. It can autonomously carry out gas sensing missions of hazardous environments inaccessible to terrestrial robots and bigger drones, for example searching for victims and hazardous gas leaks inside pockets that form within the wreckage of collapsed buildings in the aftermath of an earthquake or explosion. The first contribution of this work is assessing the impact of the nano-propellers on the MOX sensor signals at different distances to a gas source. A second contribution is adapting the ‘bout’ detection algorithm, proposed by Schmuker et al. (2016) to extract specific features from the derivative of the MOX sensor response, for real-time operation. The third and main contribution is the experimental validation of the SNAV for gas source localization (GSL) and mapping in a large indoor environment (160 m²) with a gas source placed in challenging positions for the drone, for example hidden in the ceiling of the room or inside a power outlet box. Two GSL strategies are compared, one based on the instantaneous gas sensor response and the other one based on the bout frequency. From the measurements collected (in motion) along a predefined sweeping path we built (in less than 3 min) a 3D map of the gas distribution and identified the most likely source location. Using the bout frequency yielded on average a higher localization accuracy than using the instantaneous gas sensor response (1.38 m versus 2.05 m error), however accurate tuning of an additional parameter (the noise threshold) is required in the former case. The main conclusion of this paper is that a nano-drone has the potential to perform gas sensing tasks in complex environments. Full article

(This article belongs to the Special Issue Mobile Robot Olfaction for Real-World Applications–From Disaster Response to Environmental Monitoring)

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21 pages, 5139 KiB

Open AccessArticle

Chemical Source Searching by Controlling a Wheeled Mobile Robot to Follow an Online Planned Route in Outdoor Field Environments

by Ji-Gong Li, Meng-Li Cao and Qing-Hao Meng

Sensors 2019, 19(2), 426; https://doi.org/10.3390/s19020426 - 21 Jan 2019

Cited by 20 | Viewed by 4757

Abstract

In this paper, we present an estimation-based route planning (ERP) method for chemical source searching using a wheeled mobile robot and validate its effectiveness with outdoor field experiments. The ERP method plans a dynamic route for the robot to follow to search for a chemical source according to time-varying wind and an estimated chemical-patch path (C-PP), where C-PP is the historical trajectory of a chemical patch detected by the robot, and normally different from the chemical plume formed by the spatial distribution of all chemical patches previously released from the source. Owing to the limitations of normal gas sensors and actuation capability of ground mobile robots, it is quite hard for a single robot to directly trace the intermittent and rapidly swinging chemical plume resulting from the frequent and random changes of wind speed and direction in outdoor field environments. In these circumstances, tracking the C-PP originating from the chemical source back could help the robot approach the source. The proposed ERP method was tested in two different outdoor fields using a wheeled mobile robot. Experimental results indicate that the robot adapts to the time-varying airflow condition, arriving at the chemical source with an average success rate and approaching effectiveness of about 90% and 0.4~0.6, respectively. Full article

(This article belongs to the Special Issue Mobile Robot Olfaction for Real-World Applications–From Disaster Response to Environmental Monitoring)

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20 pages, 10620 KiB

Open AccessArticle

A Wind Estimation Method with an Unmanned Rotorcraft for Environmental Monitoring Tasks

by Jia-Ying Wang, Bing Luo, Ming Zeng and Qing-Hao Meng

Sensors 2018, 18(12), 4504; https://doi.org/10.3390/s18124504 - 19 Dec 2018

Cited by 36 | Viewed by 5391

Abstract

Wind velocity (strength and direction) is an important parameter for unmanned aerial vehicle (UAV)-based environmental monitoring tasks. A novel wind velocity estimation method is proposed for rotorcrafts. Based on an extended state observer, this method derives the wind disturbance from rotors’ speeds and rotorcraft’s acceleration and position. Then the wind disturbance is scaled to calculate the airspeed vector, which is substituted into a wind triangle to obtain the wind velocity. Easy-to-implement methods for calculating the rotorcraft’s thrust and drag coefficient are also proposed, which are important parameters to obtain the wind drag and the airspeed, respectively. Simulations and experiments using a quadrotor in both hovering and flight conditions have validated the proposed method. Full article

(This article belongs to the Special Issue Mobile Robot Olfaction for Real-World Applications–From Disaster Response to Environmental Monitoring)

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12 pages, 3885 KiB

Open AccessArticle

Application of Convolutional Long Short-Term Memory Neural Networks to Signals Collected from a Sensor Network for Autonomous Gas Source Localization in Outdoor Environments

by Christian Bilgera, Akifumi Yamamoto, Maki Sawano, Haruka Matsukura and Hiroshi Ishida

Sensors 2018, 18(12), 4484; https://doi.org/10.3390/s18124484 - 18 Dec 2018

Cited by 58 | Viewed by 6405

Abstract

Convolutional Long Short-Term Memory Neural Networks (CNN-LSTM) are a variant of recurrent neural networks (RNN) that can extract spatial features in addition to classifying or making predictions from sequential data. In this paper, we analyzed the use of CNN-LSTM for gas source localization (GSL) in outdoor environments using time series data from a gas sensor network and anemometer. CNN-LSTM is used to estimate the location of a gas source despite the challenges created from inconsistent airflow and gas distribution in outdoor environments. To train CNN-LSTM for GSL, we used temporal data taken from a 5 × 6 metal oxide semiconductor (MOX) gas sensor array, spaced 1.5 m apart, and an anemometer placed in the center of the sensor array in an open area outdoors. The output of the CNN-LSTM is one of thirty cells approximating the location of a gas source. We show that by using CNN-LSTM, we were able to determine the location of a gas source from sequential data. In addition, we compared several artificial neural network (ANN) architectures as well as trained them without wind vector data to estimate the complexity of the task. We found that ANN is a promising prospect for GSL tasks. Full article

(This article belongs to the Special Issue Mobile Robot Olfaction for Real-World Applications–From Disaster Response to Environmental Monitoring)

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20 pages, 2462 KiB

Open AccessArticle

A Semantic-Based Gas Source Localization with a Mobile Robot Combining Vision and Chemical Sensing

by Javier Monroy, Jose-Raul Ruiz-Sarmiento, Francisco-Angel Moreno, Francisco Melendez-Fernandez, Cipriano Galindo and Javier Gonzalez-Jimenez

Sensors 2018, 18(12), 4174; https://doi.org/10.3390/s18124174 - 28 Nov 2018

Cited by 40 | Viewed by 5479

Abstract

This paper addresses the localization of a gas emission source within a real-world human environment with a mobile robot. Our approach is based on an efficient and coherent system that fuses different sensor modalities (i.e., vision and chemical sensing) to exploit, for the first time, the semantic relationships among the detected gases and the objects visually recognized in the environment. This novel approach allows the robot to focus the search on a finite set of potential gas source candidates (dynamically updated as the robot operates), while accounting for the non-negligible uncertainties in the object recognition and gas classification tasks involved in the process. This approach is particularly interesting for structured indoor environments containing multiple obstacles and objects, enabling the inference of the relations between objects and between objects and gases. A probabilistic Bayesian framework is proposed to handle all these uncertainties and semantic relations, providing an ordered list of candidates to be the source. This candidate list is updated dynamically upon new sensor measurements to account for objects not previously considered in the search process. The exploitation of such probabilities together with information such as the locations of the objects, or the time needed to validate whether a given candidate is truly releasing gases, is delegated to a path planning algorithm based on Markov decision processes to minimize the search time. The system was tested in an office-like scenario, both with simulated and real experiments, to enable the comparison of different path planning strategies and to validate its efficiency under real-world conditions. Full article

(This article belongs to the Special Issue Mobile Robot Olfaction for Real-World Applications–From Disaster Response to Environmental Monitoring)

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