Tracking Odor Plumes in a Laminar Wind Field with Bio-inspired Algorithms

Lochmatter, Thomas; Martinoli, Alcherio

doi:10.1007/978-3-642-00196-3_54

Cited by 58 publications

(61 citation statements)

References 20 publications

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“…Through real-robot [16] [15] and simulation [14] experiments, we have recently shown that the surge-spiral [6] [7] [2] [4] and the surge-cast [15] algorithms are faster and more reliable than pure casting [11] [10] [23] [13] [12] [1] in laminar wind flow. The experiments were run using a single robot, and the result was in- Thomas Lochmatter, Alcherio Martinoli Distributed Intelligent Systems and Algorithms Laboratory (DISAL),École Polytechnique Fédérale de Lausanne (EPFL), Station 2, 1015 Lausanne, Switzerland.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, we compare the performance difference when moving from a single-robot to a homogeneous multi-robot system with 2 or 5 robots. Our performance metric is the distance overhead (traveled distance d t divided by upwind distance d u ), which is an excellent indicator for the speed of a plume following algorithm on a holonomic robot [15]. Moreover, we only require one robot to reach the odor source, and use the distance overhead of the first robot to reach the source as the performance of the robotic team.…”

Section: Introductionmentioning

confidence: 99%

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Understanding the Potential Impact of Multiple Robots in Odor Source Localization

Lochmatter

Martinoli

2009

Distributed Autonomous Robotic Systems 8

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We investigate the performance of three bio-inspired odor source localization algorithms used in non-cooperating multi-robot systems. Our performance metric is the distance overhead of the first robot to reach the source, which is a good measure for the speed of an odor source localization algorithm. Using the performance distribution of single-robot experiments, we calculate an ideal performance for multi-robot teams. We carry out simulations in a realistic robotic simulator and provide quantitative evidence of the differences between ideal and realistic performances of a given algorithm. A closer analysis of the results show that these differences are mainly due to physical interference among robots.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Understanding the Potential Impact of Multiple Robots in Odor Source Localization

Lochmatter

Martinoli

2009

Distributed Autonomous Robotic Systems 8

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“…The comparison is carried out both in simulation [63], with experiments carried out in a wind tunnel with real robots [64] and even theoretically under simplifying assumptions [65]. The algorithms are compared in terms of success rate and distance overhead when tracking the plume up to the source.…”

Section: Gas Source Localizationmentioning

confidence: 99%

Gas Discrimination for Mobile Robots

Trincavelli

2011

Künstl Intell

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The problem addressed in this thesis is discrimination of gases with an array of partially selective gas sensors. Metal oxide gas sensors are the most common gas sensing technology since they have, compared to other gas sensing technologies, a high sensitivity to the target compounds, a fast response time, they show a good stability of the response over time and they are commercially available. One of the most severe limitation of metal oxide gas sensors is the scarce selectivity, that means that they do not respond only to the compound for which they are optimized but also to other compounds. One way to enhance the selectivity of metal oxide gas sensors is to build an array of sensors with different, and partially overlapping, selectivities and then analyze the response of the array with a pattern recognition algorithm. The concept of an array of partially selective gas sensors used together with a pattern recognition algorithm is known as an electronic nose (e-nose).In this thesis the attention is focused on e-nose applications related mobile robotics. A mobile robot equipped with an e-nose can address tasks like environmental monitoring, search and rescue operations or exploration of hazardous areas. In e-noses mounted on mobile robots the sensing array is most often directly exposed to the environment without the use of a sensing chamber. This choice is often made because of constraints in weight, costs and because the dynamic response obtained by the direct interaction of the sensors with the gas plume contains valuable information. However, this setup introduces additional challenges due to the gas dispersion that characterize natural environments. Turbulent and chaotic gas dispersal causes the array of sensors to be exposed to rapid changes in concentration that cause the sensor response to be highly dynamic and to seldom reach a steady state. Therefore the discrimination of gases has to be performed on features extracted from the dynamics of the signal. The problem is further complicated by variations in temperature and humidity, physical variables to which metal oxide gas sensors are crossensitive. For these reasons the problem of discrimination of gases when an array of sensors is directly exposed to the environment is different from when the array of sensors is in a controlled chamber.i ii This thesis is a compilation of papers whose contributions are two folded. On one side new algorithms for discrimination of gases with an array of sensors directly exposed to the environment are presented. On the other side, innovative experimental setups are proposed. These experimental setups enable the collection of high quality data that allow a better insight in the problem of discrimination of gases with mobile robots equipped with an e-nose. The algorithmic contributions start with the design and validation of a gas discrimination algorithm for gas sensors array directly exposed to the environment. The algorithm is then further developed in order to be able to run online on a robot, thereby enabling t...

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“…Bio-inspired [16], [17], concentration gradient climbing (chemotaxis) and up-wind directed search (anemotaxis [19], [24], [25]) are the most common approaches to track odor plumes by mobile robots. Several other methods have been proposed for plume tracking using swarm robotic concepts, namely, biasing expansion swarm approaches (BESA) [26], biased random walk (BRW), evolutionary strategies [27], particle swarm optimization (PSO) [28]- [30], glowworm swarm optimization (GSO) [31], gradient climbing techniques, swarm spiral surge [32], physics-based swarming approach [33], and attraction/repulsion forces [15].…”

Section: Introductionmentioning

confidence: 99%

Optimal Swarm Formation for Odor Plume Finding

Marjovi

Marques

2014

IEEE Trans. Cybern.

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Abstract-This paper presents an analytical approach to the problem of odor plume finding by a network of swarm robotic gas sensors and finds an optimal configuration for them, given a set of assumptions. Considering cross-wind movement for the swarm, we found that the best spatial formation of robots in finding odor plumes is diagonal line configuration with equal distance between each pair of neighboring robots. We show that the distance between neighboring pairs in the line topology depends mainly on the wind speed and the environmental conditions, whereas, the number of robots and the swarm's crosswind movement distance do not show significant impact on optimal configurations. These solutions were analyzed and verified by simulations and experimentally validated in a reduced scale realistic environment using a set of mobile robots.

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Tracking Odor Plumes in a Laminar Wind Field with Bio-inspired Algorithms

Cited by 58 publications

References 20 publications

Understanding the Potential Impact of Multiple Robots in Odor Source Localization

Understanding the Potential Impact of Multiple Robots in Odor Source Localization

Gas Discrimination for Mobile Robots

Optimal Swarm Formation for Odor Plume Finding

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