Using local wind information for gas distribution mapping in outdoor environments with a mobile robot

Reggente, Matteo; Lilienthal, Achim J.

doi:10.1109/icsens.2009.5398498

Cited by 49 publications

(41 citation statements)

References 11 publications

(17 reference statements)

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“…In this case we presume that the original distribution circle is morphed into a more ellipse-like region. Our calculations of the characteristics of the ellipse are based on the works by Getis and Jackson [12], and Reggente and Lilienthal [13]. The major axis of the ellipse is in the direction of the prevailing wind.…”

Section: Wind Speed and Directionmentioning

confidence: 99%

Discovering Co-location Patterns in Datasets with Extended Spatial Objects

Adilmagambetov

Zaı̈ane

Osornio-Vargas

2013

Data Warehousing and Knowledge Discovery

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Abstract. Co-location mining is one of the tasks of spatial data mining, which focuses on the detection of the sets of spatial features frequently located in close proximity of each other. Previous work is based on transaction-free apriori-like algorithms. The approach we propose is based on a grid transactionization of geographic space and designed to mine datasets with extended spatial objects. A statistical test is used instead of global thresholds to detect significant co-location patterns.

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Section: Wind Speed and Directionmentioning

confidence: 99%

Discovering Co-location Patterns in Datasets with Extended Spatial Objects

Adilmagambetov

Zaı̈ane

Osornio-Vargas

2013

Data Warehousing and Knowledge Discovery

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“…In multi-agent systems a straightforward approach is centralised workload distribution: a main controller collects all required data, such as robots' positions and sensor readings, then allocates scheduled tasks to the most suitable robots (Keshmiri and Payandeh, 2011;Liu and Kroll, 2012) in order to achieve the goal. Despite the simplicity of this approach and recent breakthroughs within indoor environments (D'Andrea, 2012), there are several well-known challenges related to the accuracy and availability of global information; precise information is not always possible to obtain, especially within large domains, for instance outdoor (Reggente and Lilienthal, 2009) or unstructured scenarios (Chitta et al, 2012). In addition, the robustness of such systems might be compromised when a problem or failure affects the central controller, which could lead to a halt in overall system operation.…”

Section: Introductionmentioning

confidence: 99%

Adaptive foraging for simulated and real robotic swarms: the dynamical response threshold approach

et al. 2016

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Developing self-organized swarm systems capable of adapting to environmental changes as well as to dynamic situations is a complex challenge. An efficient labour division model, with the ability to regulate the distribution of work among swarm robots, is an important element of this kind of system. This paper extends the popular Response Threshold Model (RTM) and proposes a new Adaptive Response Threshold Model (ARTM). Experiments were carried out in simulation and in real-robot scenarios with the aim of studying the performance of this new adaptive model. Results presented in this paper verify that the extended approach improves on the adaptability of previous systems. For example, by reducing collision duration among robots in foraging missions, our approach helps small swarms of robots to adapt more efficiently to changing environments, thus increasing their self-sustainability (survival rate). Finally, we propose a minimal version of ARTM, which is derived from the conclusions obtained through real-robot and simulation results.

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“…In a previous work [6] we have introduced a method to include local wind information in statistical 2D gas distribution modelling. The wind information is taken into account during the creation of the map where spatial integration of the point measurements is carried out by using a bivariate Gaussian kernel.…”

Section: Introductionmentioning

confidence: 99%

The 3D-Kernel DM+V/W algorithm: Using wind information in three dimensional gas distribution modelling with a mobile robot

Reggente

Lilienthal

2010

2010 IEEE Sensors

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Abstract-In this paper we present a statistical method to build three-dimensional gas distribution maps from gas sensor and wind measurements obtained with a mobile robot in uncontrolled environments. The particular contribution of this paper is to introduce and evaluate an algorithm for 3D statistical gas distribution mapping, that takes into account airflow information. 3D-Kernel DM+V/W algorithm uses a multivariate Gaussian weighting function to model the information provided by the gas sensors and an ultrasonic anemometer. The proposed algorithm is evaluated with respect to the ability of the obtained models to predict unseen measurements. The results based on 15 trials with a mobile robot in an indoor environment show improvements in the model performance when using the 3D kernel DM+V/W algorithm. Moreover the model is able to adapt to the dynamical changes of the environment learning the hyper-parameter from the sensors readings.

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Using local wind information for gas distribution mapping in outdoor environments with a mobile robot

Cited by 49 publications

References 11 publications

Discovering Co-location Patterns in Datasets with Extended Spatial Objects

Discovering Co-location Patterns in Datasets with Extended Spatial Objects

Adaptive foraging for simulated and real robotic swarms: the dynamical response threshold approach

The 3D-Kernel DM+V/W algorithm: Using wind information in three dimensional gas distribution modelling with a mobile robot

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