How Multirobot Systems Research will Accelerate our Understanding of Social Animal Behavior

Balch, Tucker; Dellaert, Frank; Feldman, Adam S.; Guillory, Andrew; Isbell, Charles L.; Khan, Zia; Pratt, Stephen C.; Stein, Andrew N.; Wilde, Hank

doi:10.1109/jproc.2006.876969

Cited by 42 publications

(25 citation statements)

References 36 publications

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“…Recent advances in automation are overcoming these issues [14][15][16], and there now exist several image-based systems capable of extracting individual behavior with minimal or zero manual intervention (Table S1 in the supplementary material online). Tracking over ecologically relevant spatiotemporal scales is becoming easier, owing to advances in imaging and computing technologies, and by the development of software that can track in real time [17][18][19] and recognize individuals across image sequences [20,21].…”

Section: Automated Image-based Trackingmentioning

confidence: 99%

“…In supervised learning, the human expert identifies and categorizes patterns in the data by informing the software of categorical behavior annotations [8,15,71,73] (Movie S11, Movie S12, and Movie S14 in the supplementary material online). For example, 'wing grooming' could represent when a fly rubs one or both metathoracic legs over the top or the underside of the wing(s) [73].…”

Section: Automated Behavioral Analysismentioning

confidence: 99%

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Automated image-based tracking and its application in ecology

Dell

Bender²,

Branson

et al. 2014

Trends in Ecology & Evolution

445

484

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The behavior of individuals determines the strength and outcome of ecological interactions, which drive population, community, and ecosystem organization. Bio-logging, such as telemetry and animal-borne imaging, provides essential individual viewpoints, tracks, and life histories, but requires capture of individuals and is often impractical to scale. Recent developments in automated image-based tracking offers opportunities to remotely quantify and understand individual behavior at scales and resolutions not previously possible, providing an essential supplement to other tracking methodologies in ecology. Automated image-based tracking should continue to advance the field of ecology by enabling better understanding of the linkages between individual and higher-level ecological processes, via high-throughput quantitative analysis of complex ecological patterns and processes across scales, including analysis of environmental drivers. Measuring behaviorIndividual behavior (see Glossary) underlies almost all aspects of ecology [1][2][3][4][5]. Accurate and highly resolved behavioral data are therefore critical for obtaining a mechanistic and predictive understanding of ecological systems [5]. Historically, direct observation by trained biologists was used to quantify behavior [6,7]. However, the extent and resolution to which direct observations can be made is highly constrained [8] and the number of individuals that can be observed simultaneously is small. In addition, an exact record of events is not preserved, only the biologist's subjective account of them.Recent technological advances in tracking now make it possible to collect large amounts of highly precise and accurate behavioral data. For many organisms equipment can be attached that provide information about the Glossary Background subtraction: a method used by software to compare the current video frame with a stored picture of the background; any pixel of the current frame that is significantly different from the corresponding pixel in the background is likely to be associated with the body of an animal. Useful in situations where the background is unchanging, for example, when the surface of the background is rigid and lighting does not change. Behavior: the actions of individuals, often in response to stimuli. Behavior can involve movement of the individual's body through space, such as walking or chasing, or can occur while the animal is stationary, such as grooming or eating. Bio-logging: attachment or implantation of equipment to organisms to provide information about their identity, location, behavior, or physiology (e.g., global positioning systems, accelerometers, video cameras, telemetry tags). Ecological interaction: any interaction between an organism and its environment, or between two organisms (i.e., including interactions between conspecifics). Fingerprinting: a method used to identify unmarked individuals using natural variation in their physical and/or behavioral appearance. The method works by transforming the images of each individual ...

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Section: Automated Image-based Trackingmentioning

confidence: 99%

Section: Automated Behavioral Analysismentioning

confidence: 99%

Automated image-based tracking and its application in ecology

Dell

Bender²,

Branson

et al. 2014

Trends in Ecology & Evolution

445

484

View full text Add to dashboard Cite

show abstract

“…Similarly, a robot must respond to the predicted position and trajectories of targets from a laser scan within seconds [37]. For the specific applications in the study of animal behavior that we address, tools that facilitate behavioral data collection must allow easy and quick acquisition, modification, and annotation of tracking data to gain widespread use [1], [2], [25].…”

Section: Related Workmentioning

confidence: 99%

“…The analysis of behavioral trajectory data has important implications in the study of animal behavior [1], [25], [12], [11] and this particular The test sequence presents a substantial challenge for any multitarget tracking algorithm. The sequence consisted of 10,400 frames recorded at a resolution of 720 Â 480 pixels at 30 Hz.…”

Section: Interacting Targetsmentioning

confidence: 99%

MCMC Data Association and Sparse Factorization Updating for Real Time Multitarget Tracking with Merged and Multiple Measurements

Khan

Balch

Dellaert

2006

IEEE Trans. Pattern Anal. Mach. Intell.

Self Cite

109

100

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Abstract-In several multitarget tracking applications, a target may return more than one measurement per target and interacting targets may return multiple merged measurements between targets. Existing algorithms for tracking and data association, initially applied to radar tracking, do not adequately address these types of measurements. Here, we introduce a probabilistic model for interacting targets that addresses both types of measurements simultaneously. We provide an algorithm for approximate inference in this model using a Markov chain Monte Carlo (MCMC)-based auxiliary variable particle filter. We Rao-Blackwellize the Markov chain to eliminate sampling over the continuous state space of the targets. A major contribution of this work is the use of sparse least squares updating and downdating techniques, which significantly reduce the computational cost per iteration of the Markov chain. Also, when combined with a simple heuristic, they enable the algorithm to correctly focus computation on interacting targets. We include experimental results on a challenging simulation sequence. We test the accuracy of the algorithm using two sensor modalities, video, and laser range data. We also show the algorithm exhibits real time performance on a conventional PC.

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“…By adopting artificial evolution, it is possible to learn and synthesize the control systems of a group of robots that are able to display expected behaviours without human supervision. In addition, explaining the initiation and maintaining of cooperation in multi-robot systems will accelerate our understanding of biological systems and human societies [3,4].…”

Section: Introductionmentioning

confidence: 99%

The Evolution of Cooperative Behaviours in Physically Heterogeneous Multi-Robot Systems

Yang

Liu

et al. 2012

International Journal of Advanced Robotic Systems

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Evolutionary Robotics (ER) is a methodology that uses evolutionary computation to develop controllers for autonomous robots. The existing works focus on single robot systems or physically homogenous multi-robot teams, although physical heterogeneousness is more prevalent in the real world. It is therefore instructive to examine whether cooperative behaviours can be synthesized using artificial evolution for a team of physically heterogeneous robots. This paper makes an important contribution in answering the question of whether robots with distinct capabilities can synthesize their control strategies to accommodate their own capabilities without human intervention. We present an empirical analysis of the collaboration mechanisms and suggest guidelines about how to choose appropriate evolution methods. Simulated experiments with a team of e-puck robots show that evolution can lead to effective controllers for robots with distinct capabilities.

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How Multirobot Systems Research will Accelerate our Understanding of Social Animal Behavior

Cited by 42 publications

References 36 publications

Automated image-based tracking and its application in ecology

Automated image-based tracking and its application in ecology

MCMC Data Association and Sparse Factorization Updating for Real Time Multitarget Tracking with Merged and Multiple Measurements

The Evolution of Cooperative Behaviours in Physically Heterogeneous Multi-Robot Systems

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