Sensing odour trails for mobile robot navigation

Russell, Andrew; Thiel, David V.; Mackay‐Sim, Alan

doi:10.1109/robot.1994.351111

Cited by 51 publications

(31 citation statements)

References 6 publications

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“…Most work on chemical sensing for mobile robots assumes an experimental setup that minimizes the influence of turbulent transport by either minimising the source-to-sensor distance in trail following [28,[33][34][35] or by assuming a strong unidirectional airstream in the environment [10,13,30,32]. Primarily a strong airstream can be used to get additional information about the local wind speed and direction from an anemometer.…”

Section: Related Workmentioning

confidence: 99%

Experimental analysis of gas-sensitive Braitenberg vehicles

Lilienthal¹,

Duckett²

2004

Advanced Robotics

108

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Abstract. This article addresses the problem of localising a static gas source in an indoor environment by a mobile robot. In contrast to previous works, the environment is not artificially ventilated to produce a strong unidirectional airflow. Here, the dominant transport mechanisms of gas molecules are turbulence and convection flow rather than diffusion, which results in a patchy, chaotically fluctuating gas distribution. Two Braitenberg-type strategies (positive and negative tropotaxis) based on the instantaneously measured spatial concentration gradient were investigated. Both strategies were shown to be of potential use for gas source localisation. As a possible solution to the problem of gas source declaration (the task of determining with certainty that the gas source has been found), an indirect localisation strategy based on exploration and concentration peak avoidance is suggested. Here, a gas source is located by exploiting the fact that local concentration maxima occur more frequently near the gas source compared to distant regions.

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Section: Related Workmentioning

confidence: 99%

Experimental analysis of gas-sensitive Braitenberg vehicles

Lilienthal¹,

Duckett²

2004

Advanced Robotics

108

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“…This is normally not the case in experiments where a gas plume has to be traced and a distant gas source has to be localized. In [69] it has been proposed a device, depicted in Figure 4.14, to further enhance the concentration gradient encountered between the trail and non-trail region. This device draws air from the floor to the sensor inlet and blows air in the opposite direction around the sensor inlet in order to create an "air curtain".…”

Section: Chemical Trail Followingmentioning

confidence: 99%

“…This device draws air from the floor to the sensor inlet and blows air in the opposite direction around the sensor inlet in order to create an "air curtain". However, the efficacy of this device has been questioned by Larionova et al in [71], and it is currently unclear whether the different results have been obtained because of small differences in the implementation of the air curtain or to differences in the tasks considered (detecting a narrow trail in [69] versus a comparatively wide area in [71]). …”

Section: Chemical Trail Followingmentioning

confidence: 99%

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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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“…The simulation model was integrated with the antennas and mouth containing tactile and chemical sensors to percept information from the environment, that is, it performs by wandering, edge following, seeking food, and feeding food. In 1994 Australian researchers A. Russell et al [140] emulated ant behavior by creating robotic systems that are capable of both laying down and detecting chemical trails. These systems represent chemotaxis: detecting and orienting themselves along a chemical trail.…”

Section: Introductionmentioning

confidence: 99%

Neural Preprocessing and Control of Reactive Walking Machines

Manoonpong¹

2007

Cognitive Technologies

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Research in the domain of biologically inspired walking machines has focused for the most part on the mechanical designs and locomotion control. Although some of this research has been concentrated on the generation of a reactive behavior of walking machines, it has been restricted only to a few of such reactive behaviors. However, from this research, there are only few examples where different behaviors have been implemented in one machine at the same time. In general, these walking machines were solely designed for pure locomotion, i.e. without sensing environmental stimuli.Therefore, in this thesis, biologically inspired walking machines with different reactive behaviors are presented. Inspired by obstacle avoidance and escape behavior of scorpions and cockroaches, such behavior is implemented in the walking machines as a negative tropism. On the other hand, a sound induced behavior called "sound tropism", in analogy to the prey capture behavior of spiders, is employed as a model of a positive tropism. The biological sensing systems which those animals use to trigger the described behaviors are investigated so that they can be reproduced in the abstract form with respect to their principle functionalities. In addition, the morphologies of a salamander and a cockroach which are designed for efficient locomotion are also taken into account for the leg and trunk designs of the four-and six-legged walking machines, respectively. Different behavior controls for generating the biologically inspired reactive behaviors are developed on the basis of a modular neural structure. Each behavior control consists of a neural preprocessing module and a neural control module. Preprocessing is for sensory signals while the neural control generates basic locomotion and changes the appropriate motions, e.g. turning left, right or walking backward, with respect to sensory signals. Neural preprocessing and control are formed by realizing discrete-time dynamical properties of recurrent neural networks. Parts of the networks are generated iii iv Abstract and optimized by using an evolutionary algorithm. Utilizing the modular neural structure, the coupling of the neural control module with different neural preprocessing modules leads to the desired behavior controllers, e.g. obstacle avoidance and sound tropism. Furthermore, these behavior controllers are then fused by using a sensor fusion technique consisting of lookup table and time scheduling methods to obtain an effective behavior fusion controller, whereby different neural preprocessing modules have to cooperate.Eventually, all of these reactive behavior controllers together with the physical sensor systems are implemented on the physical walking machines to be tested in a real world environment. The fully equipped walking machines can be seen as artificial perception-action systems. As a result, the walking machine(s) is able to respond to environmental stimuli, e.g. wandering around, sound tropism (positive tropism), avoiding obstacles and even escaping from corners a...

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Sensing odour trails for mobile robot navigation

Cited by 51 publications

References 6 publications

Experimental analysis of gas-sensitive Braitenberg vehicles

Experimental analysis of gas-sensitive Braitenberg vehicles

Gas Discrimination for Mobile Robots

Neural Preprocessing and Control of Reactive Walking Machines

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