Understanding the Potential Impact of Multiple Robots in Odor Source Localization

Abstract-We derive the theoretical performance of three bio-inspired odor source localization algorithms (casting, surgespiral and surge-cast) in laminar wind flow. Based on the geometry of the trajectories and the wind direction sensor error, we calculate the distribution of the distance overhead and the mean success rate using Bayes inference. Our approach is related to particle filtering and produces smooth output distributions. The results are compared to existing real-robot and simulation results, and a good match is observed.

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“…For β > 15 o , the theoretically derived distance overhead distribution is almost normal and in accordance with our previous findings [26].…”

Section: Resultssupporting

confidence: 91%

Theoretical analysis of three bio-inspired plume tracking algorithms

Lochmatter

Martinoli

2009

2009 IEEE International Conference on Robotics and Automation

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“…To perform the surge collaboratively with a team of robots, all of the other robots surge toward any robot that communicates that it got a hit. Lochmatter and Martinoli (2009) continue to find this algorithm to be effective, to the present.…”

Section: Other Approachesmentioning

confidence: 82%

Experimental studies of swarm robotic chemical plume tracing using computational fluid dynamics simulations

Zarzhitsky

Spears²,

Thayer³

2010

International Journal of Intelligent Computing and Cybernetics

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Purpose-The purpose of this paper is to describe a multi-robot solution to the problem of chemical source localization, in which a team of inexpensive, simple vehicles with short-range, low-power sensing, communication, and processing capabilities trace a chemical plume to its source emitter Design/methodology/approach-The source localization problem is analyzed using computational fluid dynamics simulations of airborne chemical plumes. The analysis is divided into two parts consisting of two large experiments each: the first part focuses on the issues of collaborative control, and the second part demonstrates how task performance is affected by the number of collaborating robots. Each experiment tests a key aspect of the problem, e.g. effects of obstacles, and defines performance metrics that help capture important characteristics of each solution. Findings-The new empirical simulations confirmed previous theoretical predictions: a physics-based approach is more effective than the biologically inspired methods in meeting the objectives of the plume-tracing mission. This gain in performance is consistent across a variety of plume and environmental conditions. This work shows that high success rate can be achieved by robots using strictly local information and a fully decentralized, fault-tolerant, and reactive control algorithm. Originality/value-This is the first paper to compare a physics-based approach against the leading alternatives for chemical plume tracing under a wide variety of fluid conditions and performance metrics. This is also the first presentation of the algorithms showing the specific mechanisms employed to achieve superior performance, including the underlying fluid and other physics principles and their numerical implementation, and the mechanisms that allow the practitioner to duplicate the outstanding performance of this approach under conditions of many robots navigating through obstacle-dense environments.

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“…Overall, the algorithms based on upwind surge yield significantly better performance than pure casting. Interesting extensions of these evaluations are presented in [66], where the algorithms are evaluated in presence of obstacles and in [67], where the performance of the same three algorithms is evaluated in the context of a non-cooperating multirobot system.…”

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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Understanding the Potential Impact of Multiple Robots in Odor Source Localization

Cited by 13 publications

References 24 publications

Theoretical analysis of three bio-inspired plume tracking algorithms

Theoretical analysis of three bio-inspired plume tracking algorithms

Experimental studies of swarm robotic chemical plume tracing using computational fluid dynamics simulations

Gas Discrimination for Mobile Robots

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