| 1. |
- Arain, Muhammad Asif, 1983-
(author)
-
Efficient Remote Gas Inspection with an Autonomous Mobile Robot
- 2020
-
Doctoral thesis (other academic/artistic)abstract
- Human-caused greenhouse gas emissions are one of the major sources of global warming, which is threatening to reach a tipping point. Inspection systems that can provide direct information about critical factors causing global warming, such as systems for gas detection and location of gas sources, are urgently needed to analyze the fugitive emissions and take necessary actions.This thesis presents an autonomous robotic system capable of performing efficient exploration by selecting informative sampling positions for gas detection and gas distribution mapping – the Autonomous Remote Methane Explorer (ARMEx). In the design choice of ARMEx, a ground robot carries a spectroscopybased remote gas sensor, such as a Remote Methane Leak Detector (RMLD), that collects integral gas measurements along up to 30 m long optical-beams. The sensor is actuated to sample a large area inside an adjustable field of view, and with the mobility of the robot, adaptive sampling for high spatial resolution in the areas of interest is made possible to inspect large environments.In a typical gas sampling mission, the robot needs to localize itself and plan a traveling path to visit different locations in the area, which is a largely solved problem. However, the state-of-the-art prior to this thesis fell short of providing the capability to select informative sampling positions autonomously. This thesis introduces efficient measurement strategies to bring autonomy to mobile remote gas sensing. The strategies are based on sensor planning algorithms that minimize the number of measurements and distance traveled while optimizing the inspection criteria: full sensing coverage of the area for gas detection, and suitably overlapping sensing coverage of different viewpoints around areas of interest for gas distribution mapping.A prototype implementation of ARMEx was deployed in a large, real-world environment where inspection missions performed by the autonomous system were compared with runs teleoperated by human experts. In six experimental trials, the autonomous system created better gas maps, located more gas sources correctly, and provided better sensing coverage with fewer sensing positions than human experts.
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| 2. |
- Arain, Muhammad Asif, 1983-, et al.
(author)
-
Improving Gas Tomography With Mobile Robots : An Evaluation of Sensing Geometries in Complex Environments
- 2017
-
In: 2017 ISOCS/IEEE International Symposium on Olfaction andElectronic Nose (ISOEN 2017) Proceedings. - : IEEE. - 9781509023929 - 9781509023936
-
Conference paper (peer-reviewed)abstract
- An accurate model of gas emissions is of high importance in several real-world applications related to monitoring and surveillance. Gas tomography is a non-intrusive optical method to estimate the spatial distribution of gas concentrations using remote sensors. The choice of sensing geometry, which is the arrangement of sensing positions to perform gas tomography, directly affects the reconstruction quality of the obtained gas distribution maps. In this paper, we present an investigation of criteria that allow to determine suitable sensing geometries for gas tomography. We consider an actuated remote gas sensor installed on a mobile robot, and evaluated a large number of sensing configurations. Experiments in complex settings were conducted using a state-of-the-art CFD-based filament gas dispersal simulator. Our quantitative comparison yields preferred sensing geometries for sensor planning, which allows to better reconstruct gas distributions.
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| 3. |
- Arain, Muhammad Asif, 1983-, et al.
(author)
-
Sniffing out fugitive methane emissions : autonomous remote gas inspection with a mobile robot
- 2021
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In: The international journal of robotics research. - : Sage Publications. - 0278-3649 .- 1741-3176. ; 40:4-5, s. 782-814
-
Journal article (peer-reviewed)abstract
- Air pollution causes millions of premature deaths every year, and fugitive emissions of, e.g., methane are major causes of global warming. Correspondingly, air pollution monitoring systems are urgently needed. Mobile, autonomous monitoring can provide adaptive and higher spatial resolution compared with traditional monitoring stations and allows fast deployment and operation in adverse environments. We present a mobile robot solution for autonomous gas detection and gas distribution mapping using remote gas sensing. Our ‘‘Autonomous Remote Methane Explorer’’ (ARMEx) is equipped with an actuated spectroscopy-based remote gas sensor, which collects integral gas measurements along up to 30 m long optical beams. State-of-the-art 3D mapping and robot localization allow the precise location of the optical beams to be determined, which then facilitates gas tomography (tomographic reconstruction of local gas distributions from sets of integral gas measurements). To autonomously obtain informative sampling strategies for gas tomography, we reduce the search space for gas inspection missions by defining a sweep of the remote gas sensor over a selectable field of view as a sensing configuration. We describe two different ways to find sequences of sensing configurations that optimize the criteria for gas detection and gas distribution mapping while minimizing the number of measurements and distance traveled. We evaluated anARMExprototype deployed in a large, challenging indoor environment with eight gas sources. In comparison with human experts teleoperating the platform from a distant building, the autonomous strategy produced better gas maps with a lower number of sensing configurations and a slightly longer route.
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| 4. |
- Arain, Muhammad Asif, 1983-, et al.
(author)
-
The Right Direction to Smell : Efficient Sensor Planning Strategies for Robot Assisted Gas Tomography
- 2016
-
In: 2016 IEEE International Conference on Robotics and Automation (ICRA). - New York, USA : IEEE Robotics and Automation Society. ; , s. 4275-4281
-
Conference paper (peer-reviewed)abstract
- Creating an accurate model of gas emissions is an important task in monitoring and surveillance applications. A promising solution for a range of real-world applications are gas-sensitive mobile robots with spectroscopy-based remote sensors that are used to create a tomographic reconstruction of the gas distribution. The quality of these reconstructions depends crucially on the chosen sensing geometry. In this paper we address the problem of sensor planning by investigating sensing geometries that minimize reconstruction errors, and then formulate an optimization algorithm that chooses sensing configurations accordingly. The algorithm decouples sensor planning for single high concentration regions (hotspots) and subsequently fuses the individual solutions to a global solution consisting of sensing poses and the shortest path between them. The proposed algorithm compares favorably to a template matching technique in a simple simulation and in a real-world experiment. In the latter, we also compare the proposed sensor planning strategy to the sensing strategy of a human expert and find indications that the quality of the reconstructed map is higher with the proposed algorithm.
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| 5. |
- Bennetts, Victor Hernandez, 1980-, et al.
(author)
-
Robot Assisted Gas Tomography - Localizing Methane Leaks in Outdoor Environments
- 2014
-
In: 2014 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA). - : IEEE conference proceedings. - 9781479936854 ; , s. 6362-6367
-
Conference paper (peer-reviewed)abstract
- In this paper we present an inspection robot to produce gas distribution maps and localize gas sources in large outdoor environments. The robot is equipped with a 3D laser range finder and a remote gas sensor that returns integral concentration measurements. We apply principles of tomography to create a spatial gas distribution model from integral gas concentration measurements. The gas distribution algorithm is framed as a convex optimization problem and it models the mean distribution and the fluctuations of gases. This is important since gas dispersion is not an static phenomenon and furthermore, areas of high fluctuation can be correlated with the location of an emitting source. We use a compact surface representation created from the measurements of the 3D laser range finder with a state of the art mapping algorithm to get a very accurate localization and estimation of the path of the laser beams. In addition, a conic model for the beam of the remote gas sensor is introduced. We observe a substantial improvement in the gas source localization capabilities over previous state-of-the-art in our evaluation carried out in an open field environment.
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| 6. |
- Fan, Han, 1989-, et al.
(author)
-
A cluster analysis approach based on exploiting density peaks for gas discrimination with electronic noses in open environments
- 2018
-
In: Sensors and actuators. B, Chemical. - Amsterda, Netherlands : Elsevier. - 0925-4005 .- 1873-3077. ; 259, s. 183-203
-
Journal article (peer-reviewed)abstract
- Gas discrimination in open and uncontrolled environments based on smart low-cost electro-chemical sensor arrays (e-noses) is of great interest in several applications, such as exploration of hazardous areas, environmental monitoring, and industrial surveillance. Gas discrimination for e-noses is usually based on supervised pattern recognition techniques. However, the difficulty and high cost of obtaining extensive and representative labeled training data limits the applicability of supervised learning. Thus, to deal with the lack of information regarding target substances and unknown interferents, unsupervised gas discrimination is an advantageous solution. In this work, we present a cluster-based approach that can infer the number of different chemical compounds, and provide a probabilistic representation of the class labels for the acquired measurements in a given environment. Our approach is validated with the samples collected in indoor and outdoor environments using a mobile robot equipped with an array of commercial metal oxide sensors. Additional validation is carried out using a multi-compound data set collected with stationary sensor arrays inside a wind tunnel under various airflow conditions. The results show that accurate class separation can be achieved with a low sensitivity to the selection of the only free parameter, namely the neighborhood size, which is used for density estimation in the clustering process.
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| 7. |
- Fan, Han, 1989-, et al.
(author)
-
Improving Gas Dispersal Simulation For Mobile Robot Olfaction : Using Robot-Created Occupancy Maps And Remote Gas Sensors In The Simulation Loop
- 2017
-
In: 2017 ISOCS/IEEE International Symposium on Olfaction andElectronic Nose (ISOEN 2017) Proceedings. - : IEEE conference proceedings. - 9781509023929 - 9781509023936
-
Conference paper (peer-reviewed)abstract
- Mobile robot platforms equipped with olfaction systems have been used in many gas sensing applications. However, in-field validation of mobile robot olfaction systems is time consuming, expensive, cumbersome and lacks repeatability. In order to address these issues, simulation tools are used. However, the available mobile robot olfaction simulations lack models for remote gas sensors, and the possibility to import geometrical representations of actual real-world environments in a convenient way. In this paper, we describe extensions to an open-source CFD-based filament gas dispersal simulator. These improvements arrow to use robot-created occupancy maps and offer remote sensing capabilities in the simulation loop. We demonstrate the novel features in an example application: we created a 3D map a complex indoor environment, and performed a gas emission monitoring task with a Tunable Diode Laser Absorption Spectroscopy based remote gas sensor in a simulated version of the environment.
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| 8. |
- Fan, Han, 1989-, et al.
(author)
-
Semi-supervised Gas Detection Using an Ensemble of One-class Classifiers
- 2019
-
In: 18th ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN). - : IEEE.
-
Conference paper (peer-reviewed)abstract
- Detecting chemical compounds using electronic noses is important in many gas sensing related applications. Existing gas detection methods typically use prior knowledge of the target analytes. However, in some scenarios, the analytes to be detected are not fully known in advance, and preparing a dedicated model is not possible. To address this issue, we propose a gas detection approach using an ensemble of one-class classifiers. The proposed approach is initialized by learning a Mahalanobis-based and a Gaussian based model using clean air only. During the sampling process, the presence of chemicals is detected by the initialized system, which allows to learn a one-class nearest neighbourhood model without supervision. From then on the gas detection considers the predictions of the three one-class models. The proposed approach is validated with real-world experiments, in which a mobile robot equipped with an e-nose was remotely controlled to interact with different chemical analytes in an open environment.
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| 9. |
- Fan, Han, 1989-, et al.
(author)
-
Towards Gas Discrimination and Mapping in Emergency Response Scenarios Using a Mobile Robot with an Electronic Nose
- 2019
-
In: Sensors. - : MDPI. - 1424-8220. ; 19:3
-
Journal article (peer-reviewed)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.
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| 10. |
- Fan, Han, 1989-, et al.
(author)
-
Unsupervised gas discrimination in uncontrolled environments by exploiting density peaks
- 2016
-
In: 2016 IEEE SENSORS. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781479982875
-
Conference paper (peer-reviewed)abstract
- Gas discrimination with Open Sampling Systems based on low-cost electro-chemical sensor arrays is of great interest in several applications, such as exploration of hazardous areas and environmental monitoring. Due to the lack of labeled training data or the high costs of obtaining them, as well as the presence of unknown interferents in the target environments, supervised learning is often not applicable and thus, unsupervised learning is an interesting alternative. In this work, we present a cluster analysis approach that can infer the number of different chemical compounds and label the measurements in a given uncontrolled environment without relying on previously acquired training data. Our approach is validated with data collected in indoor and outdoor environments by a mobile robot equipped with an array of metal oxide sensors. The results show that high classification accuracy can be achieved with a rather low sensitivity to the selection of the only functional parameter of our proposed algorithm.
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| 11. |
- Hernandez Bennetts, Victor, 1980-, et al.
(author)
-
A Novel Approach for Gas Discrimination in Natural Environments with Open Sampling Systems
- 2014
-
In: Proceedings of the IEEE Sensors Conference 2014. - : IEEE conference proceedings. ; , s. -2049
-
Conference paper (peer-reviewed)abstract
- This work presents a gas discrimination approachfor Open Sampling Systems (OSS), composed of non-specificmetal oxide sensors only. In an OSS, as used on robots or insensor networks, the sensors are exposed to the dynamics of theenvironment and thus, most of the data corresponds to highlydiluted samples while high concentrations are sparse. In addition,a positive correlation between class separability and concentra-tion level can be observed. The proposed approach computes theclass posteriors by coupling the pairwise probabilities betweenthe compounds to a confidence model based on an estimation ofthe concentration. In this way a rejection posterior, analogous tothe detection limit of the human nose, is learned. Evaluation wasconducted in indoor and outdoor sites, with an OSS equippedrobot, in the presence of two gases. The results show that theproposed approach achieves a high classification performancewith a low sensitivity to the selection of meta parameters.
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| 12. |
- Hernandez Bennetts, Victor, 1980-, et al.
(author)
-
Combining Non Selective Gas Sensors on a Mobile Robot for Identification and Mapping of Multiple Chemical Compounds
- 2014
-
In: Sensors. - : MDPI AG. - 1424-8220. ; 14:9, s. 17331-17352
-
Journal article (peer-reviewed)abstract
- In this paper, we address the task of gas distribution modeling in scenarios where multiple heterogeneous compounds are present. Gas distribution modeling is particularly useful in emission monitoring applications where spatial representations of the gaseous patches can be used to identify emission hot spots. In realistic environments, the presence of multiple chemicals is expected and therefore, gas discrimination has to be incorporated in the modeling process. The approach presented in this work addresses the task of gas distribution modeling by combining different non selective gas sensors. Gas discrimination is addressed with an open sampling system, composed by an array of metal oxide sensors and a probabilistic algorithm tailored to uncontrolled environments. For each of the identified compounds, the mapping algorithm generates a calibrated gas distribution model using the classification uncertainty and the concentration readings acquired with a photo ionization detector. The meta parameters of the proposed modeling algorithm are automatically learned from the data. The approach was validated with a gas sensitive robot patrolling outdoor and indoor scenarios, where two different chemicals were released simultaneously. The experimental results show that the generated multi compound maps can be used to accurately predict the location of emitting gas sources.
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| 13. |
- Hernandez Bennetts, Victor, 1980-, et al.
(author)
-
Integrated Simulation of Gas Dispersion and Mobile Sensing Systems
- 2015
-
In: Workshop on Realistic, Rapid and Repeatable Robot Simulation.
-
Conference paper (peer-reviewed)abstract
- Accidental or intentional releases of contaminants into the atmosphere pose risks to human health, the environment, the economy, and national security. In some cases there may be a single release from an unknown source, while in other cases there are fugitive emissions from multiple sources. The need to locate and characterize the sources efficiently - whether it be the urgent need to evacuate or the systematic need to cover broad geographical regions with limited resources - is shared among all cases. Efforts have begun to identify leaks with gas analyzers mounted on Mobile Robot Olfaction (MRO) systems, road vehicles, and networks of fixed sensors, such as may be based in urban environments. To test and compare approaches for gas-sensitive robots a truthful gas dispersion simulator is needed. In this paper, we present a unified framework to simulate gas dispersion and to evaluate mobile robotics and gas sensing technologies using ROS. This framework is also key to developing and testing optimization and planning algorithms for determining sensor placement and sensor motion, as well as for fusing and connecting the sensor measurements to the leak locations.
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| 14. |
- Hernandez Bennetts, Victor, 1980-, et al.
(author)
-
Online parameter selection for gas distribution mapping
- 2014
-
In: Sensor Letters. - : American Scientific Publishers. - 1546-198X .- 1546-1971. ; 12:6-7, s. 1147-1151
-
Journal article (peer-reviewed)abstract
- The ability to produce truthful maps of the distribution of one or more gases is beneficial for applications ranging from environmental monitoring to mines and industrial plants surveillance. Realistic environments are often too complicated for applying analytical gas plume models or performing reliable CFD simulations, making data-driven statistical gas distribution models the most attractive alternative. However, statistical models for gas distribution modelling, often rely on a set of meta-parameters that need to be learned from the data through Cross Validation (CV) techniques. CV techniques are computationally expensive and therefore need to be computed offline. As a faster alternative, we propose a parameter selection method based on Virtual Leave-One-Out Cross Validation (VLOOCV) that enables online learning of meta-parameters. In particular, we consider the Kernel DM+V, one of the most well studied algorithms for statistical gas distribution mapping, which relies on a meta-parameter, the kernel bandwidth. We validate the proposed VLOOCV method on a set of indoor and outdoor experiments where a mobile robot with a Photo Ionization Detector (PID) was collecting gas measurements. The approximation provided by the proposed VLOOCV method achieves very similar results to plain Cross Validation at a fraction of the computational cost. This is an important step in the development of on-line statistical gas distribution modelling algorithms.
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| 15. |
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| 16. |
- Hernandez Bennetts, Victor, 1980-, et al.
(author)
-
Probabilistic Air Flow Modelling Using Turbulent and Laminar Characteristics for Ground and Aerial Robots
- 2017
-
In: IEEE Robotics and Automation Letters. - : Institute of Electrical and Electronics Engineers (IEEE). - 2377-3766. ; 2:2, s. 1117-1123
-
Journal article (peer-reviewed)abstract
- For mobile robots that operate in complex, uncontrolled environments, estimating air flow models can be of great importance. Aerial robots use air flow models to plan optimal navigation paths and to avoid turbulence-ridden areas. Search and rescue platforms use air flow models to infer the location of gas leaks. Environmental monitoring robots enrich pollution distribution maps by integrating the information conveyed by an air flow model. In this paper, we present an air flow modelling algorithm that uses wind data collected at a sparse number of locations to estimate joint probability distributions over wind speed and direction at given query locations. The algorithm uses a novel extrapolation approach that models the air flow as a linear combination of laminar and turbulent components. We evaluated the prediction capabilities of our algorithm with data collected with an aerial robot during several exploration runs. The results show that our algorithm has a high degree of stability with respect to parameter selection while outperforming conventional extrapolation approaches. In addition, we applied our proposed approach in an industrial application, where the characterization of a ventilation system is supported by a ground mobile robot. We compared multiple air flow maps recorded over several months by estimating stability maps using the Kullback–Leibler divergence between the distributions. The results show that, despite local differences, similar air flow patterns prevail over time. Moreover, we corroborated the validity of our results with knowledge from human experts.
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| 17. |
- Hernandez Bennetts, Victor, 1980-, et al.
(author)
-
Robot assisted gas tomography : an alternative approach for the detection of fugitive methane emissions
- 2014
-
In: Workshop on Robot Monitoring.
-
Conference paper (peer-reviewed)abstract
- Methane (CH4) based combustibles, such as Natural Gas (NG) and BioGas (BG), are considered bridge fuels towards a decarbonized global energy system. NG emits less CO2 during combustion than other fossil fuels and BG can be produced from organic waste. However, at BG production sites, leaks are common and CH4 can escape through fissures in pipes and insulation layers. While by regulation BG producers shall issue monthly CH4 emission reports, measurements are sparsely collected, only at a few predefined locations. Due to the high global warming potential of CH4, efficient leakage detection systems are critical. We present a robotics approach to localize CH4 leaks. In Robot assisted Gas Tomography (RGT), a mobile robot is equipped with remote gas sensors to create gas distribution maps, which can be used to infer the location of emitting sources. Spectroscopy based remote gas sensors report integral concentrations, which means that the measurements are spatially unresolved, with neither information regarding the gas distribution over the optical path nor the length of the s beam. Thus, RGT fuses different sensing modalities, such as range sensors for robot localization and ray tracing, in order to infer plausible gas distribution models that explain the acquired integral concentration measurements.
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| 18. |
- Hernandez Bennetts, Victor, 1980-, et al.
(author)
-
Towards occupational health improvement in foundries through dense dust and pollution monitoring using a complementary approach with mobile and stationary sensing nodes
- 2016
-
In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781509037629 ; , s. 131-136
-
Conference paper (peer-reviewed)abstract
- In industrial environments, such as metallurgic facilities, human operators are exposed to harsh conditions where ambient air is often polluted with quartz, dust, lead debris and toxic fumes. Constant exposure to respirable particles can cause irreversible health damages and thus it is of high interest for occupational health experts to monitor the air quality on a regular basis. However, current monitoring procedures are carried out sparsely, with data collected in single day campaigns limited to few measurement locations. In this paper we explore the use and present first experimental results of a novel heterogeneous approach that uses a mobile robot and a network of low cost sensing nodes. The proposed system aims to address the spatial and temporal limitations of current monitoring techniques. The mobile robot, along with standard localization and mapping algorithms, allows to produce short term, spatially dense representations of the environment where dust, gas, ambient temperature and airflow information can be modelled. The sensing nodes on the other hand, can collect temporally dense (and usually spatially sparse) information during long periods of time, allowing in this way to register for example, daily variations in the pollution levels. Using data collected with the proposed system in an steel foundry, we show that a heterogeneous approach provides dense spatio-temporal information that can be used to improve the working conditions in industrial facilities.
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| 19. |
- Ishida, Hiroshi, et al.
(author)
-
Using Chemical Sensors as 'Noses' for Mobile Robots
- 2016
-
In: Essentials of Machine Olfaction and Taste. - Singapore : John Wiley & Sons. - 9781118768488 - 9781118768518 ; , s. 219-246
-
Book chapter (peer-reviewed)abstract
- Gas sensors detect the presence of gaseous chemical compounds in air. They are often used in the form of gas alarms for detecting dangerous or hazardous gases. However, a limited number of stationary gas alarms may not be always sufficient to cover a large industrial facility. Human workers having a portable gas detector in their hand needs to be sent to thoroughly check gas leaks in the areas not covered by stationary gas alarms. However, making repetitive measurements with a gas detector at a number of different locations is laborious. Moreover, the places where the gas concentration level needs to be checked are often potentially dangerous for human workers. If a portable gas detector is mounted on a mobile robot, the task of patrolling in an industrial facility for checking a gas leak can be automated. Robots are good at doing repetitive tasks, and can be sent into harsh environments.
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| 20. |
- Khaliq, Ali, 1986-, et al.
(author)
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Bringing Artificial Olfaction and Mobile Robotics Closer Together : An Integrated 3D Gas Dispersion Simulator in ROS
- 2015
-
In: Proceedings of the 16th International Symposium on Olfaction and Electronic Noses.
-
Conference paper (peer-reviewed)abstract
- Despite recent achievements, the potential of gas-sensitive mobile robots cannot be realized due to the lack of research on fundamental questions. A key limitation is the difficulty to carry out evaluations against ground truth. To test and compare approaches for gas-sensitive robots a truthful gas dispersion simulator is needed. In this paper we present a unified framework to simulate gas dispersion and to evaluate mobile robotics and gas sensing algorithms using ROS. Gas dispersion is modeled as a set of particles affected by diffusion, turbulence, advection and gravity. Wind information is integrated as time snapshots computed with any fluid dynamics computation tool. In addition, response models for devices such as Metal Oxide (MOX) sensors can be integrated in the framework.
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| 21. |
- Kucner, Tomasz Piotr, 1988-, et al.
(author)
-
Enabling Flow Awareness for Mobile Robots in Partially Observable Environments
- 2017
-
In: IEEE Robotics and Automation Letters. - : Institute of Electrical and Electronics Engineers (IEEE). - 2377-3766. ; 2:2, s. 1093-1100
-
Journal article (peer-reviewed)abstract
- Understanding the environment is a key requirement for any autonomous robot operation. There is extensive research on mapping geometric structure and perceiving objects. However, the environment is also defined by the movement patterns in it. Information about human motion patterns can, e.g., lead to safer and socially more acceptable robot trajectories. Airflow pattern information allow to plan energy efficient paths for flying robots and improve gas distribution mapping. However, modelling the motion of objects (e.g., people) and flow of continuous media (e.g., air) is a challenging task. We present a probabilistic approach for general flow mapping, which can readily handle both of these examples. Moreover, we present and compare two data imputation methods allowing to build dense maps from sparsely distributed measurements. The methods are evaluated using two different data sets: one with pedestrian data and one with wind measurements. Our results show that it is possible to accurately represent multimodal, turbulent flow using a set of Gaussian Mixture Models, and also to reconstruct a dense representation based on sparsely distributed locations.
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| 22. |
- Kucner, Tomasz, 1988-, et al.
(author)
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Tell me about dynamics! : Mapping velocity fields from sparse samples with Semi-Wrapped Gaussian Mixture Models
- 2016
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In: Robotics.
-
Conference paper (peer-reviewed)abstract
- Autonomous mobile robots often require informa-tion about the environment beyond merely the shape of thework-space. In this work we present a probabilistic method formappingdynamics, in the sense of learning and representingstatistics about the flow of discrete objects (e.g., vehicles, people)as well as continuous media (e.g., air flow). We also demonstratethe capabilities of the proposed method with two use cases. Onerelates to motion planning in populated environments, whereinformation about the flow of people can help robots to followsocial norms and to learn implicit traffic rules by observingthe movements of other agents. The second use case relates toMobile Robot Olfaction (MRO), where information about windflow is crucial for most tasks, including e.g. gas detection, gasdistribution mapping and gas source localisation. We representthe underlying velocity field as a set of Semi-Wrapped GaussianMixture Models (SWGMM) representing the learnt local PDF ofvelocities. To estimate the parameters of the PDF we employ aformulation of Expectation Maximisation (EM) algorithm specificfor SWGMM. We also describe a data augmentation methodwhich allows to build a dense dynamic map based on a sparseset of measurements. In case only a small set of observations isavailable we employ a hierarchical sampling method to generatevirtual observations from existing mixtures.
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| 23. |
- Mosberger, Rafael, 1980-, et al.
(author)
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Inferring human body posture information from reflective patterns of protective work garments
- 2016
-
In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781509037629 ; , s. 4131-4136
-
Conference paper (peer-reviewed)abstract
- We address the problem of extracting human body posture labels, upper body orientation and the spatial location of individual body parts from near-infrared (NIR) images depicting patterns of retro-reflective markers. The analyzed patterns originate from the observation of humans equipped with protective high-visibility garments that represent common safety equipment in the industrial sector. Exploiting the shape of the observed reflectors we adopt shape matching based on the chamfer distance and infer one of seven discrete body posture labels as well as the approximate upper body orientation with respect to the camera. We then proceed to analyze the NIR images on a pixel scale and estimate a figure-ground segmentation together with human body part labels using classification of densely extracted local image patches. Our results indicate a body posture classification accuracy of 80% and figure-ground segmentations with 87% accuracy.
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| 24. |
- Schaffernicht, Erik, 1980-, et al.
(author)
-
Mobile robots for learning spatio-temporal interpolation models in sensor networks - The Echo State map approach : The Echo State map approach
- 2017
-
In: 2017 IEEE International Conference on Robotics and Automation (ICRA). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 2659-2665
-
Conference paper (peer-reviewed)abstract
- Sensor networks have limited capabilities to model complex phenomena occuring between sensing nodes. Mobile robots can be used to close this gap and learn local interpolation models. In this paper, we utilize Echo State Networks in order to learn the calibration and interpolation model between sensor nodes using measurements collected by a mobile robot. The use of Echo State Networks allows to deal with temporal dependencies implicitly, while the spatial mapping with a Gaussian Process estimator exploits the fact that Echo State Networks learn linear combinations of complex temporal dynamics. The resulting Echo State Map elegantly combines spatial and temporal cues into a single representation. We showcase the method in the exposure modeling task of building dust distribution maps for foundries, a challenge which is of great interest to occupational health researchers. Results from simulated data and real world experiments highlight the potential of Echo State Maps. While we focus on particulate matter measurements, the method can be applied for any other environmental variables like temperature or gas concentration.
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| 25. |
- Vuka, Mikel, et al.
(author)
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Exploration and Localization of a Gas Source with MOX Gas Sensorson a Mobile Robot : A Gaussian Regression Bout Amplitude Approach
- 2017
-
In: 2017 ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN 2017). - : IEEE. ; , s. 164-166
-
Conference paper (peer-reviewed)abstract
- Mobile robot olfaction systems combine gas sensorswith mobility provided by robots. They relief humansof dull, dirty and dangerous tasks in applications such assearch & rescue or environmental monitoring. We address gassource localization and especially the problem of minimizingexploration time of the robot, which is a key issue due toenergy constraints. We propose an active search approach forrobots equipped with MOX gas sensors and an anemometer,given an occupancy map. Events of rapid change in the MOXsensor signal (“bouts”) are used to estimate the distance to agas source. The wind direction guides a Gaussian regression,which interpolates distance estimates. The contributions of thispaper are two-fold. First, we extend previous work on gassource distance estimation with MOX sensors and propose amodification to cope better with turbulent conditions. Second,we introduce a novel active search gas source localizationalgorithm and validate it in a real-world environment.
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| 26. |
- Wiedemann, Thomas, 1988-, et al.
(author)
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Bayesian Gas Source Localization and Exploration with a Multi-Robot System Using Partial Differential Equation Based Modeling
- 2017
-
In: 2017 ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN 2017). - : IEEE. - 9781509023936 - 9781509023929 ; , s. 122-124
-
Conference paper (peer-reviewed)abstract
- Here we report on active water sampling devices forunderwater chemical sensing robots. Crayfish generate jetlikewater currents during food search by waving theflagella of their maxillipeds. The jets generated toward theirsides induce an inflow from the surroundings to the jets.Odor sample collection from the surroundings to theirolfactory organs is promoted by the generated inflow.Devices that model the jet discharge of crayfish have beendeveloped to investigate the effectiveness of the activechemical sampling. Experimental results are presented toconfirm that water samples are drawn to the chemicalsensors from the surroundings more rapidly by using theaxisymmetric flow field generated by the jet discharge thanby centrosymmetric flow field generated by simple watersuction. Results are also presented to show that there is atradeoff between the angular range of chemical samplecollection and the sample collection time.
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| 27. |
- Xing, Yuxin, et al.
(author)
-
FireNose on Mobile Robot in Harsh Environments
- 2019
-
In: IEEE Sensors Journal. - : IEEE. - 1530-437X .- 1558-1748. ; 19:24, s. 12418-12431
-
Journal article (peer-reviewed)abstract
- In this work we present a novel multi-sensor unit, a.k.a. FireNose, to detect and discriminate both known and unknown gases in uncontrolled conditions to aid firefighters under harsh conditions. The unit includes three metal oxide (MOX) gas sensors with CMOS micro heaters, a plasmonic enhanced non-dispersive infrared (NDIR) sensor optimized for the detection of CO2, a commercial temperature humidity sensor, and a flow sensor. We developed custom film coatings for the MOX sensors (SnO2, WO3 and NiO) which greatly improved the gas sensitivity, response time and lifetime of the miniature devices. Our proposed system exhibits promising performance for gas sensing in harsh environments, in terms of power consumption (∼ 35 mW at 350°C per MOX sensor), response time (<10 s), robustness and physical size. The sensing unit was evaluated with plumes of gases in both, a laboratory setup on a gas testing rig and on-board a mobile robot operating indoors. These high sensitivity, high-bandwidth sensors, together with online unsupervised gas discrimination algorithms, are able to detect and generate their spatial distribution maps accordingly. In the robotic experiments, the resulting gas distribution maps corresponded well to the actual location of the sources. Therefore, we verified its ability to differentiate gases and generate gas maps in real-world experiments.
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| 28. |
- Xing, Yuxin, et al.
(author)
-
Mobile robot multi-sensor unit for unsupervised gas discrimination in uncontrolled environments
- 2017
-
In: IEEE SENSORS 2017. - New York : Institute of Electrical and Electronics Engineers (IEEE). - 9781509010127 - 9781509010134 ; , s. 1691-1693
-
Conference paper (peer-reviewed)abstract
- In this work we present a novel multi-sensor unit to detect and discriminate unknown gases in uncontrolled environments. The unit includes three metal oxide (MOX) sensors with CMOS micro heaters, a plasmonic enhanced non-dispersive infra-red (NDIR) sensor, a commercial temperature humidity sensor, and a flow sensor. The proposed sensing unit was evaluated with plumes of gases (propanol, ethanol and acetone) in both, a laboratory setup on a gas testing bench and on-board a mobile robot operating in an indoor workshop. It offers significantly improved performance compared to commercial systems, in terms of power consumption, response time and physical size. We verified the ability to discriminate gases in an unsupervised manner, with data collected on the robot and high accuracy was obtained in the classification of propanol versus acetone (96%), and ethanol versus acetone (90%).
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| 29. |
- Zhang, Ye, 1984-, et al.
(author)
-
Reconstructing gas distribution maps via an adaptive sparse regularization algorithm
- 2016
-
In: Inverse Problems in Science and Engineering. - : Taylor & Francis. - 1741-5977 .- 1741-5985. ; 24:7, s. 1186-1204
-
Journal article (peer-reviewed)abstract
- In this paper, we present an algorithm to be used by an inspectionrobot to produce a gas distribution map and localize gas sources ina large complex environment. The robot, equipped with a remotegas sensor, measures the total absorption of a tuned laser beam andreturns integral gas concentrations. A mathematical formulation ofsuch measurement facility is a sequence of Radon transforms,which isa typical ill-posed problem. To tackle the ill-posedness, we developa new regularization method based on the sparse representationproperty of gas sources and the adaptive finite-element method. Inpractice, only a discrete model can be applied, and the quality ofthe gas distributionmap depends on a detailed 3-D world model thatallows us to accurately localize the robot and estimate the paths of thelaser beam. In this work, using the positivity ofmeasurements and theprocess of concentration, we estimate the lower and upper boundsof measurements and the exact continuous model (mapping fromgas distribution to measurements), and then create a more accuratediscrete model of the continuous tomography problem. Based onadaptive sparse regularization, we introduce a new algorithm thatgives us not only a solution map but also a mesh map. The solutionmap more accurately locates gas sources, and the mesh map providesthe real gas distribution map. Moreover, the error estimation of theproposed model is discussed. Numerical tests for both the syntheticproblem and practical problem are given to show the efficiency andfeasibility of the proposed algorithm.
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| 30. |
- Arain, Muhammad Asif, 1983-, et al.
(author)
-
Global coverage measurement planning strategies for mobile robots equipped with a remote gas sensor
- 2015
-
In: Sensors. - Basel, Switzerland : MDPI. - 1424-8220. ; 15:3, s. 6845-6871
-
Journal article (peer-reviewed)abstract
- The problem of gas detection is relevant to many real-world applications, such as leak detection in industrial settings and landfill monitoring. In this paper, we address the problem of gas detection in large areas with a mobile robotic platform equipped with a remote gas sensor. We propose an algorithm that leverages a novel method based on convex relaxation for quickly solving sensor placement problems, and for generating an efficient exploration plan for the robot. To demonstrate the applicability of our method to real-world environments, we performed a large number of experimental trials, both on randomly generated maps and on the map of a real environment. Our approach proves to be highly efficient in terms of computational requirements and to provide nearly-optimal solutions.
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| 31. |
- Banaee, Hadi, 1986-, et al.
(author)
-
Data-Driven Conceptual Spaces : Creating Semantic Representations for Linguistic Descriptions of Numerical Data
- 2018
-
In: The journal of artificial intelligence research. - : AAAI Press. - 1076-9757 .- 1943-5037. ; 63, s. 691-742
-
Journal article (peer-reviewed)abstract
- There is an increasing need to derive semantics from real-world observations to facilitate natural information sharing between machine and human. Conceptual spaces theory is a possible approach and has been proposed as mid-level representation between symbolic and sub-symbolic representations, whereby concepts are represented in a geometrical space that is characterised by a number of quality dimensions. Currently, much of the work has demonstrated how conceptual spaces are created in a knowledge-driven manner, relying on prior knowledge to form concepts and identify quality dimensions. This paper presents a method to create semantic representations using data-driven conceptual spaces which are then used to derive linguistic descriptions of numerical data. Our contribution is a principled approach to automatically construct a conceptual space from a set of known observations wherein the quality dimensions and domains are not known a priori. This novelty of the approach is the ability to select and group semantic features to discriminate between concepts in a data-driven manner while preserving the semantic interpretation that is needed to infer linguistic descriptions for interaction with humans. Two data sets representing leaf images and time series signals are used to evaluate the method. An empirical evaluation for each case study assesses how well linguistic descriptions generated from the conceptual spaces identify unknown observations. Furthermore, comparisons are made with descriptions derived on alternative approaches for generating semantic models.
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| 32. |
- Banaee, Hadi, 1986-
(author)
-
From Numerical Sensor Data to Semantic Representations : A Data-driven Approach for Generating Linguistic Descriptions
- 2018
-
Doctoral thesis (other academic/artistic)abstract
- In our daily lives, sensors recordings are becoming more and more ubiquitous. With the increased availability of data comes the increased need of systems that can represent the data in human interpretable concepts. In order to describe unknown observations in natural language, an artificial intelligence system must deal with several issues involving perception, concept formation, and linguistic description. These issues cover various subfields within artificial intelligence, such as machine learning, cognitive science, and natural language generation.The aim of this thesis is to address the problem of semantically modelling and describing numerical observations from sensor data. This thesis introduces data-driven approaches to perform the tasks of mining numerical data and creating semantic representations of the derived information in order to describe unseen but interesting observations in natural language.The research considers creating a semantic representation using the theory of conceptual spaces. In particular, the central contribution of this thesis is to present a data-driven approach that automatically constructs conceptual spaces from labelled numerical data sets. This constructed conceptual space then utilises semantic inference techniques to derive linguistic interpretations for novel unknown observations. Another contribution of this thesis is to explore an instantiation of the proposed approach in a real-world application. Specifically, this research investigates a case study where the proposed approach is used to describe unknown time series patterns that emerge from physiological sensor data. This instantiation first presents automatic data analysis methods to extract time series patterns and temporal rules from multiple channels of physiological sensor data, and then applies various linguistic description approaches (including the proposed semantic representation based on conceptual spaces) to generate human-readable natural language descriptions for such time series patterns and temporal rules.The main outcome of this thesis is the use of data-driven strategies that enable the system to reveal and explain aspects of sensor data which may otherwise be difficult to capture by knowledge-driven techniques alone. Briefly put, the thesis aims to automate the process whereby unknown observations of data can be 1) numerically analysed, 2) semantically represented, and eventually 3) linguistically described.
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| 33. |
- Canelhas, Daniel R., 1983-, et al.
(author)
-
Compressed Voxel-Based Mapping Using Unsupervised Learning
- 2017
-
In: Robotics. - Basel, Switzerland : MDPI AG. - 2218-6581. ; 6:3
-
Journal article (peer-reviewed)abstract
- In order to deal with the scaling problem of volumetric map representations, we propose spatially local methods for high-ratio compression of 3D maps, represented as truncated signed distance fields. We show that these compressed maps can be used as meaningful descriptors for selective decompression in scenarios relevant to robotic applications. As compression methods, we compare using PCA-derived low-dimensional bases to nonlinear auto-encoder networks. Selecting two application-oriented performance metrics, we evaluate the impact of different compression rates on reconstruction fidelity as well as to the task of map-aided ego-motion estimation. It is demonstrated that lossily reconstructed distance fields used as cost functions for ego-motion estimation can outperform the original maps in challenging scenarios from standard RGB-D (color plus depth) data sets due to the rejection of high-frequency noise content.
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| 34. |
- Dominguez, David Caceres, 1993-, et al.
(author)
-
A Stack-of-Tasks Approach Combined With Behavior Trees : A New Framework for Robot Control
- 2022
-
In: IEEE Robotics and Automation Letters. - : IEEE Press. - 2377-3766. ; 7:4, s. 12110-12117
-
Journal article (peer-reviewed)abstract
- Stack-of-Tasks (SoT) control allows a robot to simultaneously fulfill a number of prioritized goals formulated in terms of (in)equality constraints in error space. Since this approach solves a sequence of Quadratic Programs (QP) at each time-step, without taking into account any temporal state evolution, it is suitable for dealing with local disturbances. However, its limitation lies in the handling of situations that require non-quadratic objectives to achieve a specific goal, as well as situations where countering the control disturbance would require a locally suboptimal action. Recent works address this shortcoming by exploiting Finite State Machines (FSMs) to compose the tasks in such a way that the robot does not get stuck in local minima. Nevertheless, the intrinsic trade-off between reactivity and modularity that characterizes FSMs makes them impractical for defining reactive behaviors in dynamic environments. In this letter, we combine the SoT control strategy with Behavior Trees (BTs), a task switching structure that addresses some of the limitations of the FSMs in terms of reactivity, modularity and re-usability. Experimental results on a Franka Emika Panda 7-DOF manipulator show the robustness of our framework, that allows the robot to benefit from the reactivity of both SoT and BTs.
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| 35. |
- Fan, Han, 1989-, et al.
(author)
-
Ensemble Learning-Based Approach for Gas Detection Using an Electronic Nose in Robotic Applications
- 2022
-
In: Frontiers in Chemistry. - : Frontiers Media S.A.. - 2296-2646. ; 10
-
Journal article (peer-reviewed)abstract
- Detecting chemical compounds using electronic noses is important in many gas sensing related applications. A gas detection system is supposed to indicate a significant event, such as the presence of new chemical compounds or a noteworthy change of concentration levels. Existing gas detection methods typically rely on prior knowledge of target analytes to prepare a dedicated, supervised learning model. However, in some scenarios, such as emergency response, not all the analytes of concern are a priori known and their presence are unlikely to be controlled. In this paper, we take a step towards addressing this issue by proposing an ensemble learning-based approach (ELBA) that integrates several one-class classifiers and learns online. The proposed approach is initialized by training several one-class models using clean air only. During the sampling process, the initialized system detects the presence of chemicals, allowing to learn another one-class model and update existing models with self-labelled data. We validated the proposed approach with real-world experiments, in which a mobile robot equipped with an e-nose was remotely controlled to interact with different chemical analytes in an uncontrolled environment. We demonstrated that the ELBA algorithm not only can detect gas exposures but also recognize baseline responses under a suspect short-term sensor drift condition. Depending on the problem setups in practical applications, the present work can be easily hybridized to integrate other supervised learning models when the prior knowledge of target analytes is partially available.
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| 36. |
- Fan, Han, 1989-
(author)
-
Robot-aided Gas Sensing for Emergency Responses
- 2022
-
Doctoral thesis (other academic/artistic)abstract
- Emergency response personnel can be exposed to various extreme hazards during the response to natural and human-made disasters. In many of the scenarios, one of the risk factors is the presence of hazardous airborne chemicals. Addressing this risk factor requires typical tiring, taxing and toxic operations that are suitable to be aided by Mobile Robot Olfaction (MRO) techniques. MRO is the research domain combining intelligent mobile robots with an artificial sense of smell. It presents the prospect of practical applications for emergency response as it allows to convey useful information on-site and online without risking the safety of human responders. However, standard gas sampling procedures for laboratory use are not directly applicable to MRO due to the complexity of uncontrolled environments and the need for fast deployment and analysis. Besides, state-of-the-art gas sensing approaches have difficulties handling A Priori Unknown Gases (APUG). In APUG situations, the number or/and identities of the present chemicals are unknown, posing challenges in recognizing the underlying risks with conventional solutions such as supervised learning-based electronic noses or dedicated gas sensors targeting known analytes.This dissertation focuses on contributions toward real-world applications of robot-aided gas sensing with an APUG problem setup. The dissertation starts with a requirement analysis of Gas Sensing for Emergency Response (GSER) to identify the key tasks in ad hoc applications. Considering that not all analytes of interest in a field application may be known in advance, a pipeline incorporating non-supervised detection and discrimination of multiple chemicals and consequent distribution modelling is found to be important for GSER. The remainder of the thesis fills this pipeline with three steps: 1) An ensemble learning-based gas detection approach is proposed to recognize significant changes from sensor signals as well as model the baseline response pattern. 2) A clustering analysis-based gas discrimination approach is developed to perform online analysis that automatically learns the number of different chemical compounds from the acquired measurements and provides a probabilistic representation of their class labels. 3) The integration of the proposed non-supervised gas detection and gas discrimination approaches with gas distribution modelling allows prototyping of a GSER system, which can enhance emergency responders’ situational awareness in the target environment. This GSER system demonstrates the concept of discriminating and mapping multiple unknown chemical compounds in uncontrolled environments with validation and evaluation using real-world data sets.During the research on the GSER system, gas dispersal simulation is also investigated to facilitate MRO algorithm development and validation in general. In-field experiments of MRO algorithms are often time-consuming, expensive, cumber some, and lack repeatability, while most of the available simulation tools are limited to insitu gas sensors and simple environments. These issues were addressed by improving a simulation framework to replicate geometrical representations of actual real-world environments and support a variety of gas sensor models. The potential applicability of the resulting work is demonstrated by simulating a gas emission monitoring task and facilitating the development process of a state-of-the-art time-dependent gas distribution modelling algorithm.
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| 37. |
- Fan, Han, 1989-, et al.
(author)
-
Towards Gas Identification in Unknown Mixtures Using an Electronic Nose with One-Class Learning
- 2022
-
In: 2022 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN). - : IEEE. - 9781665458610 - 9781665458603
-
Conference paper (peer-reviewed)abstract
- Gas identification using an electronic nose (e-nose) typically relies on a multi-class classifier trained with extensive data of a limited set of target analytes. Usually, classification performance degrades in the presence of mixtures that include interferents not represented in the training data. This issue limits the applicability of e-noses in real-world scenarios where interferents are a priori unknown. This paper investigates the feasibility of tackling this particular gas identification problem using one-class learning. We propose several training strategies for a one-class support vector machine to deal with gas mixtures composed of a target analyte and an interferent at different concentration levels. Our evaluation indicates that accurate identification of the presence of a target analyte is achievable if it is dominant in a mixture. For interferent-dominant mixtures, extensive training is required, which implies that an improvement in the generalization ability of the one-class model is needed.
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| 38. |
- Gugliermo, Simona, 1995-, et al.
(author)
-
Evaluating behavior trees
- 2024
-
In: Robotics and Autonomous Systems. - : Elsevier. - 0921-8890 .- 1872-793X. ; 178
-
Journal article (peer-reviewed)abstract
- Behavior trees (BTs) are increasingly popular in the robotics community. Yet in the growing body of published work on this topic, there is a lack of consensus on what to measure and how to quantify BTs when reporting results. This is not only due to the lack of standardized measures, but due to the sometimes ambiguous use of definitions to describe BT properties. This work provides a comprehensive overview of BT properties the community is interested in, how they relate to each other, the metrics currently used to measure BTs, and whether the metrics appropriately quantify those properties of interest. Finally, we provide the practitioner with a set of metrics to measure, as well as insights into the properties that can be derived from those metrics. By providing this holistic view of properties and their corresponding evaluation metrics, we hope to improve clarity when using BTs in robotics. This more systematic approach will make reported results more consistent and comparable when evaluating BTs.
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| 39. |
- Gugliermo, Simona, 1995-, et al.
(author)
-
Extracting Planning Domains from Execution Traces : a Progress Report
- 2023
-
Conference paper (peer-reviewed)abstract
- One of the difficulties of using AI planners in industrial applications pertains to the complexity of writing planning domain models. These models are typically constructed by domain planning experts and can become increasingly difficult to codify for large applications. In this paper, we describe our ongoing research on a novel approach to automatically learn planning domains from previously executed traces using Behavior Trees as an intermediate human-readable structure. By involving human planning experts in the learning phase, our approach can benefit from their validation. This paper outlines the initial steps we have taken in this research, and presents the challenges we face in the future.
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| 40. |
- Gugliermo, Simona, 1995-, et al.
(author)
-
Learning Behavior Trees From Planning Experts Using Decision Tree and Logic Factorization
- 2023
-
In: IEEE Robotics and Automation Letters. - : IEEE. - 2377-3766. ; 8:6, s. 3534-3541
-
Journal article (peer-reviewed)abstract
- The increased popularity of Behavior Trees (BTs) in different fields of robotics requires efficient methods for learning BTs from data instead of tediously handcrafting them. Recent research in learning from demonstration reported encouraging results that this letter extends, improves and generalizes to arbitrary planning domains. We propose BT-Factor as a new method for learning expert knowledge by representing it in a BT. Execution traces of previously manually designed plans are used to generate a BT employing a combination of decision tree learning and logic factorization techniques originating from circuit design. We test BT-Factor in an industrially-relevant simulation environment from a mining scenario and compare it against a state-of-the-art BT learning method. The results show that our method generates compact BTs easy to interpret, and capable to capture accurately the relations that are implicit in the training data.
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| 41. |
- Gutiérrez Maestro, Eduardo, 1994-, et al.
(author)
-
Wearable-Based Intelligent Emotion Monitoring in Older Adults during Daily Life Activities
- 2023
-
In: Applied Sciences. - : MDPI. - 2076-3417. ; 13:9
-
Journal article (peer-reviewed)abstract
- We present a system designed to monitor the well-being of older adults during their daily activities. To automatically detect and classify their emotional state, we collect physiological data through a wearable medical sensor. Ground truth data are obtained using a simple smartphone app that provides ecological momentary assessment (EMA), a method for repeatedly sampling people's current experiences in real time in their natural environments. We are making the resulting dataset publicly available as a benchmark for future comparisons and methods. We are evaluating two feature selection methods to improve classification performance and proposing a feature set that augments and contrasts domain expert knowledge based on time-analysis features. The results demonstrate an improvement in classification accuracy when using the proposed feature selection methods. Furthermore, the feature set we present is better suited for predicting emotional states in a leave-one-day-out experimental setup, as it identifies more patterns.
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| 42. |
|
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| 43. |
- Iannotta, Marco, 1993-, et al.
(author)
-
Heterogeneous Full-body Control of a Mobile Manipulator with Behavior Trees
- 2022
-
In: IROS 2022 Workshop on Mobile Manipulation and Embodied Intelligence (MOMA): Challenges and Opportunities.
-
Conference paper (peer-reviewed)abstract
- Integrating the heterogeneous controllers of a complex mechanical system, such as a mobile manipulator, within the same structure and in a modular way is still challenging. In this work we extend our framework based on Behavior Trees for the control of a redundant mechanical system to the problem of commanding more complex systems that involve multiple low-level controllers. This allows the integrated systems to achieve non-trivial goals that require coordination among the sub-systems.
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| 44. |
- Kucner, Tomasz Piotr, et al.
(author)
-
Survey of maps of dynamics for mobile robots
- 2023
-
In: The international journal of robotics research. - : Sage Publications. - 0278-3649 .- 1741-3176. ; 42:11, s. 977-1006
-
Journal article (peer-reviewed)abstract
- Robotic mapping provides spatial information for autonomous agents. Depending on the tasks they seek to enable, the maps created range from simple 2D representations of the environment geometry to complex, multilayered semantic maps. This survey article is about maps of dynamics (MoDs), which store semantic information about typical motion patterns in a given environment. Some MoDs use trajectories as input, and some can be built from short, disconnected observations of motion. Robots can use MoDs, for example, for global motion planning, improved localization, or human motion prediction. Accounting for the increasing importance of maps of dynamics, we present a comprehensive survey that organizes the knowledge accumulated in the field and identifies promising directions for future work. Specifically, we introduce field-specific vocabulary, summarize existing work according to a novel taxonomy, and describe possible applications and open research problems. We conclude that the field is mature enough, and we expect that maps of dynamics will be increasingly used to improve robot performance in real-world use cases. At the same time, the field is still in a phase of rapid development where novel contributions could significantly impact this research area.
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| 45. |
- Lundell, Jens, et al.
(author)
-
Safe-To-Explore State Spaces : Ensuring Safe Exploration in Policy Search with Hierarchical Task Optimization
- 2018
-
In: IEEE-RAS Conference on Humanoid Robots. - : IEEE. ; , s. 132-138, s. 132-138
-
Conference paper (peer-reviewed)abstract
- Policy search reinforcement learning allows robots to acquire skills by themselves. However, the learning procedure is inherently unsafe as the robot has no a-priori way to predict the consequences of the exploratory actions it takes. Therefore, exploration can lead to collisions with the potential to harm the robot and/or the environment. In this work we address the safety aspect by constraining the exploration to happen in safe-to-explore state spaces. These are formed by decomposing target skills (e.g., grasping) into higher ranked sub-tasks (e.g., collision avoidance, joint limit avoidance) and lower ranked movement tasks (e.g., reaching). Sub-tasks are defined as concurrent controllers (policies) in different operational spaces together with associated Jacobians representing their joint-space mapping. Safety is ensured by only learning policies corresponding to lower ranked sub-tasks in the redundant null space of higher ranked ones. As a side benefit, learning in sub-manifolds of the state-space also facilitates sample efficiency. Reaching skills performed in simulation and grasping skills performed on a real robot validate the usefulness of the proposed approach.
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| 46. |
- Mielle, Malcolm, 1991-
(author)
-
Helping robots help us : Using prior information for localization, navigation, and human-robot interaction
- 2019
-
Doctoral thesis (other academic/artistic)abstract
- Maps are often used to provide information and guide people. Emergency maps or floor plans are often displayed on walls and sketch maps can easily be drawn to give directions. However, robots typically assume that no knowledge of the environment is available before exploration even though making use of prior maps could enhance robotic mapping. For example, prior maps can be used to provide map data of places that the robot has not yet seen, to correct errors in robot maps, as well as to transfer information between map representations.I focus on two types of prior maps representing the walls of an indoor environment: layout maps and sketch maps. I study ways to relate information of sketch or layout maps with an equivalent metric map and study how to use layout maps to improve the robot’s mapping. Compared to metric maps such as sensor-built maps, layout and sketch maps can have local scale errors or miss elements of the environment, which makes matching and aligning such heterogeneous map types a hard problem.I aim to answer three research questions: how to interpret prior maps by finding meaningful features? How to find correspondences between the features of a prior map and a metric map representing the same environment? How to integrate prior maps in SLAM so that both the prior map and the map built by the robot are improved?The first contribution of this thesis is an algorithm that can find correspondences between regions of a hand-drawn sketch map and an equivalent metric map and achieves an overall accuracy that is within 10% of that of a human. The second contribution is a method that enables the integration of layout map data in SLAM and corrects errors both in the layout and the sensor map.These results provide ways to use prior maps with local scale errors and different levels of detail, whether they are close to metric maps, e.g. layout maps, or non-metric maps, e.g. sketch maps. The methods presented in this work were used in field tests with professional fire-fighters for search and rescue applications in low-visibility environments. A novel radar sensor was used to perform SLAM in smoke and, using a layout map as a prior map, users could indicate points of interest to the robot on the layout map, not only during and after exploration, but even before it took place.
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| 47. |
- Mojtahedzadeh, Rasoul, 1977-, et al.
(author)
-
Probabilistic Relational Scene Representation and Decision Making Under Incomplete Information for Robotic Manipulation Tasks
- 2014
-
In: Robotics and Automation (ICRA), 2014 IEEE International Conference on. - : IEEE Robotics and Automation Society. - 9781479936854 ; , s. 5685-5690
-
Conference paper (peer-reviewed)abstract
- In this paper, we propose an approach for robotic manipulation systems to autonomously reason about their environments under incomplete information. The target application is to automate the task of unloading the content of shipping containers. Our goal is to capture possible support relations between objects in partially known static configurations. We employ support vector machines (SVM) to estimate the probability of a support relation between pairs of detected objects using features extracted from their geometrical properties and 3D sampled points of the scene. The set of probabilistic support relations is then used for reasoning about optimally selecting an object to be unloaded first. The proposed approach has been extensively tested and verified on data sets generated in simulation and from real world configurations.
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| 48. |
- Mojtahedzadeh, Rasoul, 1977-, et al.
(author)
-
Support relation analysis and decision making for safe robotic manipulation tasks
- 2015
-
In: Robotics and Autonomous Systems. - Amsterdam : Elsevier. - 0921-8890 .- 1872-793X. ; 71:SI, s. 99-117
-
Journal article (peer-reviewed)abstract
- In this article, we describe an approach to address the issue of automatically building and using high-level symbolic representations that capture physical interactions between objects in static configurations. Our work targets robotic manipulation systems where objects need to be safely removed from piles that come in random configurations. We assume that a 3D visual perception module exists so that objects in the piles can be completely or partially detected. Depending on the outcome of the perception, we divide the issue into two sub-issues: 1) all objects in the configuration are detected; 2) only a subset of objects are correctly detected. For the first case, we use notions from geometry and static equilibrium in classical mechanics to automatically analyze and extract act and support relations between pairs of objects. For the second case, we use machine learning techniques to estimate the probability of objects supporting each other. Having the support relations extracted, a decision making process is used to identify which object to remove from the configuration so that an expected minimum cost is optimized. The proposed methods have been extensively tested and validated on data sets generated in simulation and from real world configurations for the scenario of unloading goods from shipping containers.
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| 49. |
- Pashami, Sepideh, 1985-, et al.
(author)
-
rTREFEX: Reweighting norms for detecting changes in the response of MOX gas sensors
- 2014
-
In: Sensor Letters. - : American Scientific Publishers. - 1546-198X .- 1546-1971. ; 12:6/7, s. 1123-1127
-
Journal article (peer-reviewed)abstract
- The detection of changes in the response of metal oxide (MOX) gas sensors deployed in an open sampling system is a hard problem. It is relevant for applications such as gas leak detection in mines or large-scale pollution monitoring where it is impractical to continuously store or transfer sensor readings and reliable calibration is hard to achieve. Under these circumstances, it is desirable to detect points in the signal where a change indicates a significant event, e.g. the presence of gas or a sudden change of concentration. The key idea behind the proposed change detection approach is that a change in the emission modality of a gas source appears locally as an exponential function in the response of MOX sensors due to their long response and recovery times. The algorithm proposed in this paper, rTREFEX, is an extension of the previously proposed TREFEX algorithm. rTREFEX interprets the sensor response by fitting piecewise exponential functions with different time constants for the response and recovery phase. The number of exponentials, which has to be kept as low as possible, is determined automatically using an iterative approach that solves a sequence of convex optimization problems based on l1-norm. The algorithm is evaluated with an experimental setup where a gas source changes in intensity, compound, and mixture ratio, and the gas source is delivered to the sensors exploiting natural advection and turbulence mechanisms. rTREFEX is compared against the previously proposed TREFEX, which already proved superior to other algorithms.
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| 50. |
- Rietz, Finn, 1995-, et al.
(author)
-
Towards Task-Prioritized Policy Composition
- 2022
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Conference paper (peer-reviewed)abstract
- Combining learned policies in a prioritized, ordered manner is desirable because it allows for modular design and facilitates data reuse through knowledge transfer. In control theory, prioritized composition is realized by null-space control, where low-priority control actions are projected into the null-space of high-priority control actions. Such a method is currently unavailable for Reinforcement Learning. We propose a novel, task-prioritized composition framework for Reinforcement Learning, which involves a novel concept: The indifferent-space of Reinforcement Learning policies. Our framework has the potential to facilitate knowledge transfer and modular design while greatly increasing data efficiency and data reuse for Reinforcement Learning agents. Further, our approach can ensure high-priority constraint satisfaction, which makes it promising for learning in safety-critical domains like robotics. Unlike null-space control, our approach allows learning globally optimal policies for the compound task by online learning in the indifference-space of higher-level policies after initial compound policy construction.
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