Cooperative Filters and Control for Cooperative Exploration

Zhang, Fumin; Leonard, Naomi Ehrich

doi:10.1109/tac.2009.2039240

Cited by 248 publications

(153 citation statements)

References 54 publications

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“…For the group measuring a distributed environmental field, the elliptical circulation provides high spatial density of measurements by the group. For example, it can improve their accuracy in estimating the gradient of the measured field or tracking a level set or boundary of a resource patch (Zhang and Leonard 2010). This collective motion also provides redundancy in measurements, particularly useful in the case of a sensor failure.…”

Section: Discussionmentioning

confidence: 99%

“…We show through analysis how the speed oscillations of a group in circular motion yield systematic cycling in spatial ordering. The resulting collective motion patterns also suggest possibilities for enriched spatial density of measurements of an environmental field that can benefit accuracy in collective estimation (Torney et al 2009;Zhang and Leonard 2010).…”

Section: Modeling Collective Dynamics With Coordinated Speed Oscillatmentioning

confidence: 92%

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Coordinated Speed Oscillations in Schooling Killifish Enrich Social Communication

2015

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We examine the spatial dynamics of individuals in small schools of banded killifish (Fundulus diaphanus) that exhibit rhythmic, oscillating speed, typically with sustained, coordinated, out-of-phase speed oscillations as they move around a shallow water tank. We show that the relative motion among the fish yields a periodically time-varying network of social interactions that enriches visually driven social communication. The oscillations lead to the regular making and breaking of occlusions, which we term "switching." We show that the rate of convergence to consensus (biologically, the capacity for individuals in groups to achieve effective coordinated motion) governed by the switching outperforms static alternatives, and performs as well as the less practical case of every fish sensing every other fish. We show further that the oscillations in speed yield oscillations in relative bearing between fish over a range that includes the angles previously predicted to be optimal for a fish to detect changes in heading and speed of its neighbors. To investigate systematically, we derive and analyze a dynamic model of interacting agents that move with oscillatory speed. We show that coordinated circular motion of the school leads to systematic cycling of spatial ordering of agents and possibilities for enriched spatial density of measurements of the external environment. Our results highlight the potential benefits of dynamic communication topologies in collective animal behavior, and suggest new, useful control laws for the distributed coordination of mobile robotic networks.

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Section: Discussionmentioning

confidence: 99%

Section: Modeling Collective Dynamics With Coordinated Speed Oscillatmentioning

confidence: 92%

Coordinated Speed Oscillations in Schooling Killifish Enrich Social Communication

2015

Self Cite

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“…Capitalizing on this idea and building on the methods of [55,56],Ögren et al [4] developed a provable methodology to control the shape of the formation as well as the rotation, translation and expansion of the formation (see also [64]); this was used to design control strategies for a network of vehicles to adaptively climb gradients in the sampled field and thus robustly find peaks (see Section 3.1 below for a review of the implementation of this methodology in the field). These ideas were extended further by Zhang and Leonard [65,66] to design provable control laws for cooperative level set tracking, whereby small vehicle groups could cooperate to generate contour plots of noisy, unknown fields, adjusting their formation shape to provide optimal filtering of their noisy measurements. Related work addressed environmental boundary tracking [67,68], coverage control [69,70], target tracking [71,72], and maximization of information [50].…”

Section: Background and Historymentioning

confidence: 99%

Cooperative Vehicle Environmental Monitoring

Leonard

2016

Springer Handbook of Ocean Engineering

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Over the last decade, methodologies for automated cooperative control of robotic vehicles have been designed, deployed and proven to provide efficient, reliable, and sustained monitoring of the uncertain and inhospitable ocean environment. Unprecedented data sets have been collected from deployments of cooperative vehicles in the field, and both real-time and post-deployment analyses have led to new understanding of the environment. This first decade of success in cooperative vehicle environmental monitoring sets the stage for new opportunities and future gain, especially as the development of cooperative control methodologies can continue to leverage ongoing technological and scientific advances in underwater communication and sensing, energy and computational efficiency, vehicle size, speed, maneuverability and cost, and ocean modeling and prediction.Indeed, the demonstrated potential of cooperative vehicle control has led to increased demand for fleets of autonomous underwater vehicles (AUVs) for use in measuring ocean physics, biology, chemistry and geology to improve understanding of natural dynamics and human-influenced changes in the marine environment. Further, methodologies for cooperative control of robotic vehicles in the ocean are readily adaptable to applications on land, in the air and in space; likewise, there is much to be learned from developments in these other domains. The recent explosion in research on networks and complex systems, including investigation of mechanisms that explain a "collective intelligence" exhibited by animal aggregations on the move, are also being leveraged to advance design of cooperative vehicle dynamics.For environmental monitoring to be successful, physical, chemical, and biological variables must be measured across a range of spatial and temporal scales; in the ocean the monitoring strategy must also contend with a harsh, three-dimensional physical space that is highly uncertain and dynamic. Small spatial and temporal scales associated with the measured variables typically make a stationary sensor array impractical because a very large number of sensors would be needed to get sufficient resolution in space and/or time. An array of mobile sensors, however, may be very well suited to such a challenge since mobility can be exploited to dynamically distribute fewer sensors according to the spatial and temporal scales.The underlying principle of cooperative control of vehicles for environmental monitoring leverages mobility of sensors and uses an interacting dynamic among the individual sensors to yield a collective behavior that performs better than the sum of the parts. If the vehicles can communicate their state or measure the relative state of others in the team, then they can cooperate and the cooperative vehicle dynamics can provide coordinated motion of the team as a whole. The resulting vehicle network functions as a dynamically reconfigurable sensor array with a capability for high performance in environmental monitoring not available at the level of individual...

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“…Other approaches to formation geometry changes for obstacle avoidance include priority-based arbitration between the behaviors [5], formation change using a transition matrix [6], scaling [7], or avoiding obstacles while treating formation as a rigid body [8]. Deformable formations are studied in [9] [10], where exploration of a noisy environmental field drives shape of the multi-sensor formation.…”

Section: Introductionmentioning

confidence: 99%

Graph-based distributed control for adaptive multi-robot patrolling through local formation transformation

Wasik

Pereira

Ventura

et al. 2016

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-Multi-robot cooperative navigation in real-world environments is essential in many applications, including surveillance and search-and-rescue missions. State-of-the-art methods for cooperative navigation are often tested in ideal laboratory conditions and not ready to be deployed in realworld environments, which are often cluttered with static and dynamic obstacles. In this work, we explore a graph-based framework to achieve control of real robot formations moving in a world cluttered with a variety of obstacles by introducing a new distributed algorithm for reconfiguring the formation shape. We systematically validate the reconfiguration algorithm using three real robots in scenarios of increasing complexity.

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Cooperative Filters and Control for Cooperative Exploration

Cited by 248 publications

References 54 publications

Coordinated Speed Oscillations in Schooling Killifish Enrich Social Communication

Coordinated Speed Oscillations in Schooling Killifish Enrich Social Communication

Cooperative Vehicle Environmental Monitoring

Graph-based distributed control for adaptive multi-robot patrolling through local formation transformation

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