Targeting the problem of generating high-resolution air quality maps for cities, we leverage four different sources of data: (i) in-situ air quality measurements produced by our mobile sensor network deployed on publi...
ISBN:
(纸本)9780994988614
Targeting the problem of generating high-resolution air quality maps for cities, we leverage four different sources of data: (i) in-situ air quality measurements produced by our mobile sensor network deployed on public transportation vehicles, (ii) explanatory air-quality and meteorological variables obtained from two static monitoring stations, (iii) land-use data of the city, and (iv) traffic statistics. We propose two novel approaches for estimating the targeted pollutant level at desired time-location pairs, extending also to areas of the city that are beyond the coverage of our mobile sensor network. The first is a log-linear regression model which is built over a virtual dependency graph based on land-use data. The second is a deep learning framework that automatically captures the dependencies of the data based on autoencoders. We have evaluated the two proposed approaches against three canonical modeling techniques considering metrics of coefficient of determination (R²), root mean square error (RMSE), and the fraction of predictions within a factor of two of observations (FAC2). Using more than 45 million real measurements in the models, the results show consistently superior performance in respect to the canonical techniques.
In this paper, we present a quantitative, trajectory-based method for calibrating stochastic motion models of water-floating robots. Our calibration method is based on the Correlated Random Walk (CRW) model, and consi...
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In this paper, we present a quantitative, trajectory-based method for calibrating stochastic motion models of water-floating robots. Our calibration method is based on the Correlated Random Walk (CRW) model, and consists in minimizing the Kolmogorov-Smirnov (KS) distance between the step length and step angle distributions of real and simulated trajectories generated by the robots. First, we validate this method by calibrating a physics-based motion model of a single 3-cm-sized robot floating at a water/air interface under fluidic agitation. Second, we extend the focus of our work to multi-robot systems by performing a sensitivity analysis of our stochastic motion model in the context of Self-Assembly (SA). In particular, we compare in simulation the effect of perturbing the calibrated parameters on the predicted distributions of self-assembled structures. More generally, we show that the SA of water-floating robots is very sensitive to even small variations of the underlying physical parameters, thus requiring real-time tracking of its dynamics.
This paper focuses on a real system implementation, analysis, and evaluation of a cooperative sensor fusion algorithm based on a Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter, using simulated and rea...
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ISBN:
(纸本)9781509037636
This paper focuses on a real system implementation, analysis, and evaluation of a cooperative sensor fusion algorithm based on a Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter, using simulated and real vehicles endowed with automotive-grade sensors. We have extended our previously presented cooperative sensor fusion algorithm with a fusion weight optimization method and implemented it on a vehicle that we denote as the ego vehicle. The algorithm fuses information obtained from one or more vehicles located within a certain range (that we call cooperative), which are running a multi-object tracking PHD filter, and which are sharing their object estimates. The algorithm is evaluated on two Citroen C-ZERO prototype vehicles equipped with Mobileye cameras for object tracking and lidar sensors from which the ground truth positions of the tracked objects are extracted. Moreover, the algorithm is evaluated in simulation using simulated C-ZERO vehicles and simulated Mobileye cameras. The ground truth positions of tracked objects are in this case provided by the simulator. Multiple experimental runs are conducted in both simulated and real-world conditions in which a few legacy vehicles were tracked. Results show that the cooperative fusion algorithm allows for extending the sensing field of view, while keeping the tracking accuracy and errors similar to the case in which the vehicles act alone.
We present the M 3 framework, a formal and generic computational framework for modeling and controlling stochastic distributedsystems of purely reactive robots in an automated and real-time fashion. Based on the tra...
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We present the M 3 framework, a formal and generic computational framework for modeling and controlling stochastic distributedsystems of purely reactive robots in an automated and real-time fashion. Based on the trajectories of the robots, the framework builds up an internal microscopic representation of the system, which then serves as a blueprint of models at higher abstraction levels. These models are then calibrated using a Maximum Likelihood Estimation (MLE) algorithm. We illustrate the structure and performance of the framework by performing the online optimization of a bang-bang controller for the stochastic self-assembly of water-floating, magnetically latching, passive modules. The experimental results demonstrate that the generated models can successfully optimize the assembly of desired structures.
Formation building and keeping among vehicles has been studied for many years, since 1987 with Reynolds' rules [1]. This paper presents a control algorithm, based on recent work in graph theory, able to reconfigur...
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Formation building and keeping among vehicles has been studied for many years, since 1987 with Reynolds' rules [1]. This paper presents a control algorithm, based on recent work in graph theory, able to reconfigure static formations of non-holonomic vehicles endowed solely with local positioning capabilities. The convergence of our approach is mathematically proven and applied to a realistic robotic platform.
Static and mobile sensor nodes can be employed in gas monitoring tasks to detect gas leaks in an early stage and localize gas sources. Due to the intermittent nature of gas plumes and the slow dynamics of commonly use...
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ISBN:
(数字)9798350355369
ISBN:
(纸本)9798350355376
Static and mobile sensor nodes can be employed in gas monitoring tasks to detect gas leaks in an early stage and localize gas sources. Due to the intermittent nature of gas plumes and the slow dynamics of commonly used gas sensors, measuring gas concentrations accurately and timely poses significant challenges. These challenges are exacerbated when measurements are gathered while moving. Actively sniffing in the airflow, facilitated by actuators, holds the potential to improve the quality of measurements obtained by the sensor nodes. In this paper, we present the design of a small-scale, modular sensor node endowed with gas and wind sensing modalities. To assess the benefits of active sampling and the rationale behind this enhancement, comparisons among three different air sampling modes in both static and mobile settings are conducted. Our findings suggest that passive sampling can adequately capture the primary features of gas plumes given sufficient exposure and measuring time at each position. However, active sampling enhances the responsiveness of sensor nodes, enabling the detection of more detailed fluctuations in the gas concentration and thus alleviating spatial shifts in the sensor response induced by mobility effects.
Given the patchy nature of gas plumes and the slow response of conventional gas sensors, the use of mobile robots for Gas Source Localization (GSL) tasks presents significant challenges. These aspects increase the dif...
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ISBN:
(数字)9798350384574
ISBN:
(纸本)9798350384581
Given the patchy nature of gas plumes and the slow response of conventional gas sensors, the use of mobile robots for Gas Source Localization (GSL) tasks presents significant challenges. These aspects increase the difficulties in obtaining gas measurements, encompassing both qualitative and quantitative aspects. Most existing model-based GSL algorithms rely on lengthy stops at each sampling point to ensure accurate gas measurements. However, this approach not only prolongs the time required for a single measurement but also hinders sampling during robot motion, thus exacerbating the scarcity of available gas measurements. In this work, our goal is to push the boundaries in terms of continuity in sampling to enhance system efficiency. Firstly, we decouple and comprehensively evaluate the impact of both plume dynamics and gas sensor properties on the GSL performance. Secondly, we demonstrate that adopting a continuous sampling strategy, which has been generally overlooked in prior research, markedly enhances the system efficiency by obviating the prolonged measurement pauses and leveraging all the data gathered during the robot motion. Thirdly, we further expand the capabilities of the continuous sampling by introducing a novel informative path-planning strategy, which takes into account all the information gathered along the robot's movement. The proposed method is evaluated in both simulation and reality under different scenarios emulating indoor environmental conditions.
We propose three modeling methods using a mobile sensor network to generate high spatio-temporal resolution air pollution maps for urban environments. In our deployment in Lausanne (Switzerland), dedicated sensing nod...
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We propose three modeling methods using a mobile sensor network to generate high spatio-temporal resolution air pollution maps for urban environments. In our deployment in Lausanne (Switzerland), dedicated sensing nodes are anchored to the public buses and measure multiple air quality parameters including the Lung Deposited Surface Area (LDSA), a state of the art metric for quantifying human exposure to ultra fine particles. In this paper, our focus is on generating LDSA maps. In particular, since the sensor network coverage is spatially and temporally dynamic, we leverage models to estimate the values for the locations and times where the data are not available. We first discretize the area topologically based on the street segments in the city and we then propose the following three prediction models: i) a log-linear regression model based on nine meteorological (e.g., Temperature and precipitations) and gaseous (e.g., NO 2 and CO) explanatory variables measured at two static stations in the city, ii) a novel network-based log-linear regression model that takes into account the LDSA values of the most correlated streets and also the nine explanatory variables mentioned above, iii) a novel Probabilistic Graphical Model (PGM) in which each street segment is considered as one node of the graph, and inference on conditional joint probability distributions of the nodes results in estimating the values in the nodes of interest. More than 44 millions of geo- and time-stamped LDSA measurements (i.e., More than 14 months of real data) are used in this paper to evaluate the proposed modeling approaches in various time resolutions (hourly, daily, weekly and monthly). The results show that the three approaches bring significant improvements in R2, RMSE and FAC metrics compared to a baseline K-Nearest Neighbor method.
The deployment of robots for Gas Source Localization (GSL) tasks in hazardous scenarios significantly reduces the risk to humans and animals. Gas sensing using mobile robots focuses primarily on simplified scenarios, ...
The deployment of robots for Gas Source Localization (GSL) tasks in hazardous scenarios significantly reduces the risk to humans and animals. Gas sensing using mobile robots focuses primarily on simplified scenarios, due to the complexity of gas dispersion, with a current trend towards tackling more complex environments. However, most state-of-art GSL algorithms for environments with obstacles only depend on local information, leading to low efficiency in large and more structured spaces. The efficiency of GSL can be improved dramatically by coupling it with a global knowledge of gas distribution in the environment. However, since gas dispersion in a built environment is difficult to model analytically, most previous work incorporating a gas dispersion model was tested under simplified assumptions, which do not take into consideration the impact of the presence of obstacles to the airflow and gas plume. In this paper, we propose a probabilistic algorithm that enables a robot to efficiently localize gas sources in built environments, by combining a state-of-the-art probabilistic GSL algorithm, Source Term Estimation (STE) with a learned plume model. The pipeline of generating gas dispersion datasets from realistic simulations, the training and validation of the model, as well as the integration of the learned model with the STE framework are presented. The performance of the algorithm is validated both in high-fidelity simulations and real experiments, with promising results obtained under various obstacle configurations.
Environmental monitoring and mapping operations are an essential tool to combat climate change. An important branch of this domain concerns the construction of reliable gas maps. Adaptive navigation strategies coupled...
Environmental monitoring and mapping operations are an essential tool to combat climate change. An important branch of this domain concerns the construction of reliable gas maps. Adaptive navigation strategies coupled with multi-robot systems improve the outcome of an environmental mapping mission by focusing more efficiently on informative areas. This direction is yet to be explored in the context of gas mapping, which presents peculiar challenges due to the hard-to-sense and expensive-to-model nature of the underlying phenomenon. In this paper, we introduce the application of a multi-robot system to a gas mission with severe time constraints. We study the impact of information-based navigation strategies, coupled with increasing levels of coordination among the robots, on information gathering and consequent map reconstruction performance. We also focus on proposing solutions that inject additional knowledge into the system to enhance the final mapping outcome. We tested the strategies through extensive high-fidelity simulation experiments, and we compared the proposed approaches to three relevant baseline methods.
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