| 1. |
- Asadi, Sahar, 1983-
(författare)
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Towards Dense Air Quality Monitoring : Time-Dependent Statistical Gas Distribution Modelling and Sensor Planning
- 2017
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis addresses the problem of gas distribution modelling for gas monitoring and gas detection. The presented research is particularly focused on the methods that are suitable for uncontrolled environments. In such environments, gas source locations and the physical properties of the environment, such as humidity and temperature may be unknown or only sparse noisy local measurements are available. Example applications include air pollution monitoring, leakage detection, and search and rescue operations.This thesis addresses how to efficiently obtain and compute predictive models that accurately represent spatio-temporal gas distribution.Most statistical gas distribution modelling methods assume that gas dispersion can be modelled as a time-constant random process. While this assumption may hold in some situations, it is necessary to model variations over time in order to enable applications of gas distribution modelling for a wider range of realistic scenarios.This thesis proposes two time-dependent gas distribution modelling methods. In the first method, a temporal (sub-)sampling strategy is introduced. In the second method, a time-dependent gas distribution modelling approach is presented, which introduces a recency weight that relates measurement to prediction time. These contributions are presented and evaluated as an extension of a previously proposed method called Kernel DM+V using several simulation and real-world experiments. The results of comparing the proposed time-dependent gas distribution modelling approaches to the time-independent version Kernel DM+V indicate a consistent improvement in the prediction of unseen measurements, particularly in dynamic scenarios under the condition that there is a sufficient spatial coverage. Dynamic scenarios are often defined as environments where strong fluctuations and gas plume development are present.For mobile robot olfaction, we are interested in sampling strategies that provide accurate gas distribution models given a small number of samples in a limited time span. Correspondingly, this thesis addresses the problem of selecting the most informative locations to acquire the next samples.As a further contribution, this thesis proposes a novel adaptive sensor planning method. This method is based on a modified artificial potential field, which selects the next sampling location based on the currently predicted gas distribution and the spatial distribution of previously collected samples. In particular, three objectives are used that direct the sensor towards areas of (1) high predictive mean and (2) high predictive variance, while (3) maximising the coverage area. The relative weight of these objectives corresponds to a trade-off between exploration and exploitation in the sampling strategy. This thesis discusses the weights or importance factors and evaluates the performance of the proposed sampling strategy. The results of the simulation experiments indicate an improved quality of the gas distribution models when using the proposed sensor planning method compared to commonly used methods, such as random sampling and sampling along a predefined sweeping trajectory. In this thesis, we show that applying a locality constraint on the proposed sampling method decreases the travelling distance, which makes the proposed sensor planning approach suitable for real-world applications where limited resources and time are available. As a real-world use-case, we applied the proposed sensor planning approach on a micro-drone in outdoor experiments.Finally, this thesis discusses the potential of using gas distribution modelling and sensor planning in large-scale outdoor real-world applications. We integrated the proposed methods in a framework for decision-making in hazardous inncidents where gas leakage is involved and applied the gas distribution modelling in two real-world use-cases. Our investigation indicates that the proposed sensor planning and gas distribution modelling approaches can be used to inform experts both about the gas plume and the distribution of gas in order to improve the assessment of an incident.
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| 2. |
- Arain, Muhammad Asif, 1983-
(författare)
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Efficient Remote Gas Inspection with an Autonomous Mobile Robot
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)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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| 3. |
- Canelhas, Daniel Ricão, 1983-
(författare)
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Truncated Signed Distance Fields Applied To Robotics
- 2017
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis is concerned with topics related to dense mapping of large scale three-dimensional spaces. In particular, the motivating scenario of this work is one in which a mobile robot with limited computational resources explores an unknown environment using a depth-camera. To this end, low-level topics such as sensor noise, map representation, interpolation, bit-rates, compression are investigated, and their impacts on more complex tasks, such as feature detection and description, camera-tracking, and mapping are evaluated thoroughly. A central idea of this thesis is the use of truncated signed distance fields (TSDF) as a map representation and a comprehensive yet accessible treatise on this subject is the first major contribution of this dissertation. The TSDF is a voxel-based representation of 3D space that enables dense mapping with high surface quality and robustness to sensor noise, making it a good candidate for use in grasping, manipulation and collision avoidance scenarios.The second main contribution of this thesis deals with the way in which information can be efficiently encoded in TSDF maps. The redundant way in which voxels represent continuous surfaces and empty space is one of the main impediments to applying TSDF representations to large-scale mapping. This thesis proposes two algorithms for enabling large-scale 3D tracking and mapping: a fast on-the-fly compression method based on unsupervised learning, and a parallel algorithm for lifting a sparse scene-graph representation from the dense 3D map.The third major contribution of this work consists of thorough evaluations of the impacts of low-level choices on higher-level tasks. Examples of these are the relationships between gradient estimation methods and feature detector repeatability, voxel bit-rate, interpolation strategy and compression ratio on camera tracking performance. Each evaluation thus leads to a better understanding of the trade-offs involved, which translate to direct recommendations for future applications, depending on their particular resource constraints.
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| 4. |
- Fan, Han, 1989-
(författare)
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Robot-aided Gas Sensing for Emergency Responses
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)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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| 5. |
- Mielle, Malcolm, 1991-
(författare)
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Helping robots help us : Using prior information for localization, navigation, and human-robot interaction
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)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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| 6. |
- Rudenko, Andrey, 1991-
(författare)
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Context-aware Human Motion Prediction for Robots in Complex Dynamic Environments
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Understanding human behavior is a key skill for intelligent systems that share physical and emotional spaces with humans. One of the main challenges to this end is the ability of such systems to make accurate predictions of human motion. This is a difficult task as human motion is influenced by a large variety of internal and external stimuli, such as own actions, the presence and actions of surrounding agents, social relations, rules and norms between them, or the environment with its topology, geometry, semantics and affordances.This thesis systematically addresses human motion prediction for autonomous systems by surveying the field, the different requirements to the prediction task, problem formulations and solution classes, and its application domains. Overviewing three decades of prior research from different communities, this thesis proposes a unifying taxonomy for motion prediction methods based on the modeling approach and level of contextual information used, and provides a review of the existing datasets and performance metrics. Furthermore, it discusses limitations of the state of the art and outlines directions for further research.Predicting human motion in complex dynamic and cluttered environments is particularly challenging due to the high level of required contextual awareness. To acquire, represent and incorporate a large variety of contextual cues is still an open challenge which is why in this thesis, we also make several methodological contributions. We present a planning-based approach that accounts for maps of obstacles and local interactions with social grouping constraints. This method accommodates many desired properties, such as predicting for an arbitrary number of observed people, estimating multi-modal probability distributions, reasoning over intentions, and supporting semantic map input. Apart from reaching state-of-the-art performance, this single method bridges the gap between short-term motion prediction, where social interaction is the most informative cue, and long-term prediction, where goal-orientation and obstacle geometry typically determine people’s motion trajectories.Along the same line, and in addition to contextual cues of the dynamic environment and the topometric map, semantic information about the environment is a highly informative cue for motion prediction. We address the less explored problem of predicting collision risks by inferring occupancy priors of human motion using only semantic maps as input. The proposed method, based on Convolutional Neural Networks, shows superior performance over the state of the art and demonstrates a novel way to use and apply semantics for the prediction task.Datasets that contain relevant qualities and quantities of difficulty are critical for benchmarking autonomous systems in general and for motion prediction in particular. Surprisingly, the commonly used datasets are rather limited in that they typically consider simple to almost trivial scenarios, contain little contextual cues and partly suffer from annotation issues. To address these issues, this thesis proposes a weakly-scripted data collection protocol for recording diverse and accurate trajectories of people and robots in interactive scenarios. The protocol includes social roles with simple instructions for the participants, dynamically-allocated goals, group motion and varied obstacle positioning. The data, recorded according to the introduced collection protocol, is used in a motion prediction benchmark, designed for thorough performance evaluation in a variety of experiments: accuracy conditioned on several key factors (e.g. prediction horizon, observation length), evaluation of knowledge transfer to a new environment, testing robustness against perception noise.The results presented in this thesis are relevant for a broad range of prediction problems with applications in robotics, autonomous driving or video surveillance. With the first systematic taxonomy of prediction approaches, new experiments for benchmarking and novel methods that account for particularly rich contextual cues, we contribute to the field by fostering cross-domain exchange and comparison, and by laying the foundations for various directions of future research.
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| 7. |
- Hoang, Dinh-Cuong, 1991-
(författare)
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Vision-based Perception For Autonomous Robotic Manipulation
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In order to safely and effectively operate in real-world unstructured environments where a priori knowledge of the surroundings is not available, robots must have adequate perceptual capabilities. This thesis is concerned with several important aspects of vision-based perception for autonomous robotic manipulation. With a focus on topics related to scene reconstruction, object pose estimation and grasp configuration generation, we aim at helping robots to better understand their surroundings, to avoid undesirable contacts with the environment and to accurately grasp selected objects.With the wide availability of affordable RGB-D cameras, research on visual SLAM (Simultaneous Localization and Mapping) or scene reconstruction has made giant strides in development. As a key element of an RGB-D reconstruction system, a large number of registration algorithms have been proposed in the context of RGB-D Tracking and Mapping (TAM). The state-of-the-art methods rely on color and depth information to track camera poses. Besides depth and color images, semantic information is now often available due to the advancement of image segmentation driven by deep learning. We are interested to explore to what extent the use of semantic cues can increase the robustness of camera pose tracking. This leads to the first contribution of this dissertation. A method for reliable camera tracking using an objective function that combines geometric, appearance, and semantic cues with adaptive weights.Beyond the purely geometric model of the environment produced by classical reconstruction systems, the inclusion of rich semantic information and 6D poses of object instances within a dense map is useful for robots to effectively operate and interact with objects. Therefore, the second contribution of this thesis is an approach for recognizing objects present in a scene and estimating their full pose by means of an accurate 3D semantic reconstruction. Our framework deploys simultaneously a 3D mapping algorithm to reconstruct a semantic model of the environment, and an incremental 6D object pose recovery algorithm that carries out predictions using the reconstructed model. We demonstrate that we can exploit multiple viewpoints around the same object to achieve robust and stable 6D pose estimation in the presence of heavy clutter and occlusion.The methods taking RGB-D images as input have achieved state-of-the-art performance on the object pose estimation task. However, in a number of cases, color information may not be available — for example, when the input is point cloud data from laser range finders or industrial high-resolution 3D sensors. Therefore, besides methods using RGB-D images, studies on recovering the 6D pose of rigid objects from 3D point clouds containing only geometric information are necessary. The third contribution of this dissertation is a novel deep learning architecture to address the problem of estimating the 6D pose of multiple rigid objects in a cluttered scene, using only a 3D point cloud of the scene as an input. The proposed architecture pools geometric features together using a self-attention mechanism and adopts a deep Hough voting scheme for pose proposal generation. We show that by exploiting the correlation between poses of object instances and object parts we can improve the performance of object pose estimation.By applying a 6D object pose estimation algorithm, robots can perform grasping known objects where the 3D model of objects is available and a grasp database is pre-defined. What if we want to grasp novel objects? The fourth contribution of this thesis is a method for robust manipulation of novel objects in cluttered environments. we develop an end-to-end deep learning approach for generating grasp configurations for a two-finger parallel jaw gripper, based on 3D point cloud observations of the scene. The proposed model generates candidates by casting votes to accumulate evidence for feasible grasp configurations. We exploit contextual information by encoding the dependency of objects in the scene into features to boost the performance of grasp generation.
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| 8. |
- Kucner, Tomasz Piotr, 1988-
(författare)
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Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots
- 2018
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- To bring robots closer to real-world autonomy, it is necessary to equip them with tools allowing them to perceive, model and behave adequately to dynamic changes in the environment. The idea of incorporating information about dynamics not only in the robots reactive behaviours but also in global planning process stems from the fact that dynamic changes are typically not completely random and follow spatiotemporal patterns. The overarching idea behind the work presented in this thesis is to investigate methods allowing to represent the variety of the real-world spatial motion patterns in a compact, yet expressive way. The primary focus of the presented work is on building maps capturing the motion patterns of dynamic objects and/or the flow of continuous media.The contribution of this thesis is twofold. First, I introduce Conditional-Transition Map: a representation for modelling motion patterns of dynamic objects as a multimodal flow of occupancy over a grid map. Furthermore, in this thesis I also propose an extension (Temporal Conditional-Transition Map), which models the speed of said flow. The proposed representations connect the changes of occupancy among adjacent cells. Namely, they build conditional models of the direction to where occupancy is heading given the direction from which the occupancy arrived. Previously, all of the representations modelling dynamics in grid maps assumed cell independence. The representations assuming cell independence are substantially less expressive and store only information about the observed levels of dynamics (i.e. how frequent changes are at a certain location). In contrast, the proposed representations also encode information about the direction of motion. Furthermore, the multimodal and conditional character of the representations allows to distinguish and correctly model intersecting flows. The capabilities of the introduced grid-based representations are demonstrated with experiments performed on real-world data sets.In the second part of this thesis, I introduce Circular Linear Flow Field map modelling flow of continuous media and discrete objects. This representation, in contrast to the work presented in the first part of this thesis, does not model occupancy changes directly. Instead, it employs a field of Gaussian Mixture Models, whose local elements are probability distributions of (instantaneous) velocities, to describe motion patterns. Since it assumes only velocity measurements, the proposed representation have been used to model a broad spectrum of dynamics including motion patterns of people and airflow. Using a Gaussian Mixture Model allows to capture the multimodal character of real-world dynamics (e.g. intersecting flows) and also to account for flow variability. In addition to the basic learning algorithms, I present solutions (sampling-based and kernel-based approach) for the problem of building a dense Circular Linear Flow Field map using spatially sparse but temporally dense sets of measurements. In the end, I present how to use the Circular Linear Flow Field map in motion planning to achieve flow compliant trajectories. The capabilities of Circular Linear Flow Field maps are presented and evaluated using simulated and real-world datasets.The spectrum of applications for the representations and approaches presented in this thesis is very broad. Among others, the results of this thesis can be used by service robots providing help for passengers in crowded airports or drones surveying landfills to detect leakages of greenhouse gases. In the case of a service robot interacting with passengers in a populated airport, the information about the flow of passengers allows to build not only the shortest path between points “A” and “B” but also enables the robot to behave seamlessly, unobtrusively and safely. In the case of a drone patrolling a landfill the impact of airflow, is equally significant. In this scenario, information about airflow allows harnessing the energy of airstreams to lower the energy consumption of a drone. Another way to utilise information about the wind flow is to use it to improve localisation of sources of gas leakage.
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| 9. |
- Wiedemann, Thomas, 1988-
(författare)
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Domain Knowledge Assisted Robotic Exploration and Source Localization
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Deploying mobile robots to explore hazardous environments provides an advantageous way to avoid threats for human operators. For example, in situations, where airborne toxic or explosive material is leaking, robots can be dispatched to localize the leaks. This thesis investigates a novel exploration strategy to automatically localize such emission sources with multiple mobile robots that are equipped with sensors to measure the concentration of the emitted gas.The problem of localizing gas sources consists of two sub-problems that are addressed here. First, the thesis develops a method to estimate the source locations from sequences of localized concentration measurements. This approach can be also applied in case the measurements are collected by static sensor networks or human operators. Second, the thesis proposes an exploration strategy that guides mobile robots to informative measurement locations. With this strategy, a high level of autonomy is achieved and it is ensured that the collected measurements help to estimate the sources. As the main contribution, the proposed approach incorporates prior available domain knowledge about the gas dispersion process and the environment. Accordingly, the approach was coined Domain-knowledge Assisted Robotic Exploration and Source-localization (DARES). Domain knowledge is incorporated in two ways. First, the advection-diffusion Partial Differential Equation (PDE) provides a mathematical model of the gas dispersion process. A Bayesian interpretation of the PDE allows us to estimate the source distribution and to design the exploration strategy. Second, the additional assumption is exploited that the sources are sparsely distributed in the environment, even though we do not know their exact number. The Bayesian inference approach incorporates this assumption by means of a sparsity inducing prior.Simulations and experiments show that the sparsity inducing prior helps to localize the sources based on fewer measurements compared to not exploiting the sparsity assumption. Further, the DARES approach results in efficient measurement patterns of the robots, which tend to start in downwind regions and move in upwind direction towards the sources where they cluster their measurements. It is remarkable that this behavior arises naturally without explicit instructions as a result of including domain knowledge and the proposed exploration strategy.
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