Collective Motion, Sensor Networks, and Ocean Sampling

By Naomi Ehrich Leonard, Derek A. Paley, Francois Lekien, Rodolphe Sepulchre, David M. Fratantoni, Russ E. Davis. 2007

958 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Ocean Engineering

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In Artificial Intelligence

In Computer Networks and Communications

In Ocean Engineering

Citations in the corpus

An Overview of Recent Progress in the Study of Distributed Multi-Agent Coordination (2012, background): “Distributed formation refers to the group behavior that all the agents form a pre-designed geometrical configuration through local interactions with or without a common reference.”
Mobile Sensor Network Control Using Mutual Information Methods and Particle Filters (2010, methodology): “Information theoretic costs metrics have been used to manage sensors [19], and led to algorithms to control sensor networks for information gathering over an area by parameterizing the motion of collectives of vehicles [20].”
Emergent dynamics of the Cucker-Smale flocking model and its variants (2016, background): “[6, 64], and they have been extensively studied recently because of their possible applications to sensor networks, controls of robots and unmanned aerial vehicles [49, 55, 57], and opinion formation of social networks.”
Formation control using range-only measurements (2011, positive): “It has been demonstrated that, when coordinating robotic agents to cooperatively execute a task, it is sometimes advantageous to have them maneuvering in a formation (Leonard et al., 2007).”
A survey of practical issues in underwater networks (2006, background): “The most typical is through adaptive and collaborative sampling, where sensor data influences survey patterns [59].”
Data-Driven Sparse Sensor Placement for Reconstruction: Demonstrating the Benefits of Exploiting Known Patterns (2017, background): “More generally, sensor and actuator placement may be optimized for robustness [66], [67], or for network control and consensus problems [68], [69], [70], [71], [72].”
Time-optimal path planning in dynamic flows using level set equations: theory and schemes (2014, background): “Coordinated path planning,1074 which has been extensively studied and developed re-1075 cently (Leonard and Fiorelli, 2001; Paley et al, 2008;1076 Leonard et al, 2007), renders certain types of missions1077 possible, which otherwise, could not be executed by1078 single-vehicle systems.”
Emergent Behavior of a Cucker-Smale Type Particle Model With Nonlinear Velocity Couplings (2010, background): “INTRODUCTION Collective self-organized phenomena such as flocking and synchronization appear in biological, mechanical and social groups of autonomous agents and have received lots of attention in recent years due to their engineering applications like a sensor network, formation control of robot an”
Stabilization of Planar Collective Motion With Limited Communication (2008, background): “The development in this paper is strongly motivated by the application to mobile sensor networks used for autonomous ocean sampling [8], [9].”
Coordinated control of an underwater glider fleet in an adaptive ocean sampling field experiment in Monterey Bay (2010, methodology): “A sampling performance metric is defined and justified in Leonard et al. (2007), and a parameterized set of coordinated motion patterns is examined with respect to this metric.”
Decentralized Environmental Modeling by Mobile Sensor Networks (2008, background): “Other approaches to controlled sampling and modeling of a scalar field include [11], [12], [25], [27], [30], [37], [48], and [49].”
Scalable Approach to Uncertainty Quantification and Robust Design of Interconnected Dynamical Systems (2011, positive): “The notion of coverage we use is closer to that in (Hussein & Stipanović, 2007) and (Leonard et al., 2007).”
Decentralized estimation and control of graph connectivity in mobile sensor networks (2008, background): “In mobile sensor network systems, current research areas include target tracking [14], [22], [11], formation and coverage control [2]–[4], [6], environmental monitoring [10], [16], [18], and several others.”
Formation tracking and attitude synchronization control of underactuated ships along closed orbits (2015, background): “In order to adaptively seek measurements of flow and biological variables across a range of spatial and temporal scales, the coordinated vehicle sea routes are designed according to the spatial and temporal variability in the field [2].”
Coordinated patterns of unit speed particles on a closed curve (2007, background): “It is later shown in [15] that ellipses can be mapped to circles using a nonlinear transform so that some of the results 13 in [24] can be generalized to ellipses.”
Should I stay or should I go? How the human brain manages the trade-off between exploitation and exploration (2007, background): “The midbrain dopamine system has been implicated in the signalling of reward prediction errors critical for learning the value of specific actions (Montague et al. 1996; Schultz et al. 1997) and for decision-making based on those values (McClure et al. 2003).”