Swarm robotic odor localization: Off-line optimization and validation with real 
robots

Hayes, A.T.; Martinoli, Alcherio; Goodman, R.M.

doi:10.1017/s0263574703004946

Cited by 104 publications

(58 citation statements)

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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%

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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%

“…Multi-robot odor source localization experiments with an algorithm called spiral surge (which is close to the surge-spiral algorithm used here) have previously been carried out by Hayes et al [6] [7]. Hayes ran experiments with up to 6 real robots, and up to 10 robots in simulation.…”

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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“…In this context, complex systems, such as fish schools or animal herds, can be viewed as information-processing entities with a collective awareness of their environment (7). Understanding their capacity for performing search tasks will have important consequences for the development of distributed technologies, such as olfactory robot swarms, with applications in the detection of explosives, landmines, or locating people in search-and-rescue operations (8)(9)(10).…”

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confidence: 99%

Context-dependent interaction leads to emergent search behavior in social aggregates

Torney

Neufeld

Couzin

2009

Proc. Natl. Acad. Sci. U.S.A.

103

134

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Locating the source of an advected chemical signal is a common challenge facing many living organisms. When the advecting medium is characterized by either high Reynolds number or high Peclet number, the task becomes highly nontrivial due to the generation of heterogeneous, dynamically changing filamental concentrations that do not decrease monotonically with distance to the source. Defining search strategies that are effective in these environments has important implications for the understanding of animal behavior and for the design of biologically inspired technology. Here we present a strategy that is able to solve this task without the higher intelligence required to assess spatial gradient direction, measure the diffusive properties of the flow field, or perform complex calculations. Instead, our method is based on the collective behavior of autonomous individuals following simple social interaction rules which are modified according to the local conditions they are experiencing. Through these context-dependent interactions, the group is able to locate the source of a chemical signal and in doing so displays an awareness of the environment not present at the individual level. This behavior illustrates an alternative pathway to the evolution of higher cognitive capacity via the emergent, group-level intelligence that can result from local interactions.collective intelligence | olfactory search | cooperation T hroughout the natural world, organisms are constantly faced with the challenge of locating the resources required for their survival. Often this means navigating their environment based on spatiotemporally varying information such as advected chemical cues, thermal gradients, or magnetic fields. It has been noted that collective behavior can greatly assist animal navigation. One explanation for this, known as the "many wrongs" principle (1), is that inherent noise in the environment is dampened due to multiple sampling by individuals within a group. A quantitative study of an effect of this type was made by Grünbaum (2) and the benefits of sociality clearly illustrated. However, this effect does not fully capture the potential emergent properties of social aggregations, which often display complex behaviors entirely absent at the individual level (3-6). In this context, complex systems, such as fish schools or animal herds, can be viewed as information-processing entities with a collective awareness of their environment (7). Understanding their capacity for performing search tasks will have important consequences for the development of distributed technologies, such as olfactory robot swarms, with applications in the detection of explosives, landmines, or locating people in search-and-rescue operations (8-10).The use of chemical signals by organisms in order to gain information about their environment is a ubiquitous behavior commonly seen in aquatic animals or flying insects and observed in a diverse range of species over a range of scales. For low Reynolds number, viscous environments chemotaxis in o...

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“…The design outcome can be defined as a group behaviour, and an evolutionary algorithm addresses the hard problem of a solution for the individual robot. Often a simulation is used and provides convenient access to group-level evaluative metrics [16] [14][17] [6] [23].…”

Section: Introductionmentioning

confidence: 99%

The distributed co-evolution of an on-board simulator and controller for swarm robot behaviours

2014

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The distributed co-evolution of an on-board simulator and controller for swarm robot behaviours. Evolutionary Intelligence, 7 (2). pp. 95-106. ISSN 1864-5909 Available from: http://eprints.uwe.ac.uk/23450We recommend you cite the published version. The publisher's URL is: http://dx.doi.org/10.1007/s12065-014-0112-8 Refereed: YesThe final publication is available at Springer via http://dx.doi.org/doi:10.1007/s12065?014?0112?8 Disclaimer UWE has obtained warranties from all depositors as to their title in the material deposited and as to their right to deposit such material. UWE makes no representation or warranties of commercial utility, title, or fitness for a particular purpose or any other warranty, express or implied in respect of any material deposited.UWE makes no representation that the use of the materials will not infringe any patent, copyright, trademark or other property or proprietary rights. UWE accepts no liability for any infringement of intellectual property rights in any material deposited but will remove such material from public view pending investigation in the event of an allegation of any such infringement. PLEASE SCROLL DOWN FOR TEXT.Noname manuscript No. Abstract We investigate the reality gap, specifically the environmental correspondence of an on-board simulator. We describe a novel distributed co-evolutionary approach to improve the transference of controllers that co-evolve with an on-board simulator. A novelty of our approach is the the potential to improve transference between simulation and reality without an explicit measurement between the two domains. We hypothesise that a variation of on-board simulator environment models across many robots can be competitively exploited by comparison of the real controller fitness of many robots. We hypothesise that the real controller fitness values across many robots can be taken as indicative of the varied fitness in environmental correspondence of on-board simulators, and used to inform the distributed evolution an on-board simulator environment model without explicit measurement of the real environment. Our results demonstrate that our approach creates an adaptive relationship between the onboard simulator environment model, the real world behaviour of the robots, and the state of the real environment. The results indicate that our approach is sensitive to whether the real behavioural performance of the robot is informative on the state real environment.

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Swarm robotic odor localization: Off-line optimization and validation with real robots

Cited by 104 publications

References 40 publications

Understanding the Potential Impact of Multiple Robots in Odor Source Localization

Understanding the Potential Impact of Multiple Robots in Odor Source Localization

Context-dependent interaction leads to emergent search behavior in social aggregates

The distributed co-evolution of an on-board simulator and controller for swarm robot behaviours

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