The problem of movelength and turn definition in analysis of orientation data

Tourtellot, Michael K.; Collins, Robert D.; Bell, William J.

doi:10.1016/s0022-5193(05)80428-x

Cited by 52 publications

(26 citation statements)

References 6 publications

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“…The acceleration and the minimum and maximum speeds of ants were calculated from the trajectories of 226 individuals. Owing to artefacts associated with analysis of trajectory data when using time to discretize trajectories (Tourtellot et al 1991), the calculation of angular deviation as a result of interactions was standardized by recording the position of a focal ant in the first frame when it contacted another ant (usually antennal contact with the antennae, legs and/or body). Analysing the distance between ants during such initial contacts allowed us to estimate the interaction range, r p .…”

Section: Methodsmentioning

confidence: 99%

Self-organized lane formation and optimized traffic flow in army ants

Couzin

Franks²

2003

Proc. R. Soc. Lond. B

341

338

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We show how the movement rules of individual ants on trails can lead to a collective choice of direction and the formation of distinct traffic lanes that minimize congestion. We develop and evaluate the results of a new model with a quantitative study of the behaviour of the army ant Eciton burchelli. Colonies of this species have up to 200 000 foragers and transport more than 3000 prey items per hour over raiding columns that exceed 100 m. It is an ideal species in which to test the predictions of our model because it forms pheromone trails that are densely populated with very swift ants. The model explores the influences of turning rates and local perception on traffic flow. The behaviour of real army ants is such that they occupy the specific region of parameter space in which lanes form and traffic flow is maximized.

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Section: Methodsmentioning

confidence: 99%

Self-organized lane formation and optimized traffic flow in army ants

Couzin

Franks²

2003

Proc. R. Soc. Lond. B

341

338

View full text Add to dashboard Cite

show abstract

“…The sampling rate was chosen according to Tourtellot et al [59]: The time interval between two successive points should let an individual move approximately its own body length. Thus, the sampling rate was set to one point every 0.48 s for the robots.…”

Section: Path Analysis For An Individual Robotmentioning

confidence: 99%

“…But to compute l and g, one would need an unbiased criterion to compute the distribution of turning angles, that is, to establish accurately at which moment the individual significantly changed the direction of its path [59,60]. To compute the value of l Ã we rather used the same method as in Jeanson et al [37], which does not require the characterization of the distribution of turning angles.…”

Section: Central Zonementioning

confidence: 99%

The Embodiment of Cockroach Aggregation Behavior in a Group of Micro-robots

et al. 2008

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We report the faithful reproduction of the self-organized aggregation behavior of the German cockroach Blattella germanica with a group of robots. We describe the implementation of the biological model provided by Jeanson et al. in Alice robots, and we compare the behaviors of the cockroaches and the robots using the same experimental and analytical methodology. We show that the aggregation behavior of the German cockroach was successfully transferred to the Alice robot despite strong differences between robots and animals at the perceptual, actuatorial, and computational levels. This article highlights some of the major constraints one may encounter during such a work and proposes general principles to ensure that the behavioral model is accurately transferred to the artificial agents.

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“…However, brains of insects take in more sensory information than many robots (e.g. proprioception, chemical and tactile information, as well as vision), can learn associative memories (Collet and Land, 1975; Yu, Lee and Kim, 2012; Graham et al 2010)), and cannot be predicted by a rigid set of rules (Tourtellot et al, 1991;Challet et al, 2005;Jeanson et al, 2003). Here, by investigating and modeling cockroach behavior in response to barriers to goals, we show how strategies used by insects are quite different from "Bug Algorithms."…”

Section: Introductionmentioning

confidence: 99%

How cockroaches exploit tactile boundaries to find new shelters

et al. 2015

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Animals such as cockroaches depend on exploration of unknown environments, and their strategies may inspire robotic approaches. We have previously shown that cockroach behavior, with respect to shelters and the walls of an otherwise empty arena, can be captured with a stochastic state--based algorithm. We call this algorithm RAMBLER, Randomized Algorithm Mimicking Biased Lone Exploration in Roaches. In this work, we verified and extended this model by adding a barrier in the previously used arena and conducted more cockroach experiments. In two arena configurations, our simulated model's path length distribution was similar to the experimental distribution (mean experimental path length 3.4m and 3.2m, mean simulated path length 3.9m and 3.3m). By analyzing cockroach behavior before, along, and at the end of the barrier, we have generalized RAMBLER to address arbitrarily complex 2D mazes. For biology, this is an abstract behavioral model of a decision--making process in the cockroach brain. For robotics, this is a strategy that may improve exploration for goals, especially in unpredictable environments with non--convex obstacles. Generally, cockroach behavior seems to recommend variability in the absence of planning, and following paths defined by walls.

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The problem of movelength and turn definition in analysis of orientation data

Cited by 52 publications

References 6 publications

Self-organized lane formation and optimized traffic flow in army ants

Self-organized lane formation and optimized traffic flow in army ants

The Embodiment of Cockroach Aggregation Behavior in a Group of Micro-robots

How cockroaches exploit tactile boundaries to find new shelters

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