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Coverage control for mobile sensing networks
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This paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies which are adaptive, distributed, asynchronous, and verifiably correct.Abstract:
This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.read more
Citations
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Consensus and Cooperation in Networked Multi-Agent Systems
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Flocking for multi-agent dynamic systems: algorithms and theory
TL;DR: A theoretical framework for design and analysis of distributed flocking algorithms, and shows that migration of flocks can be performed using a peer-to-peer network of agents, i.e., "flocks need no leaders."
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Stability of multiagent systems with time-dependent communication links
TL;DR: It is observed that more communication does not necessarily lead to faster convergence and may eventually even lead to a loss of convergence, even for the simple models discussed in the present paper.
Posted Content
An Overview of Recent Progress in the Study of Distributed Multi-agent Coordination
TL;DR: In this paper, the authors reviewed some main results and progress in distributed multi-agent coordination, focusing on papers published in major control systems and robotics journals since 2006, and proposed several promising research directions along with some open problems that are deemed important for further investigations.
BookDOI
Distributed Control of Robotic Networks: A Mathematical Approach to Motion Coordination Algorithms
TL;DR: This self-contained introduction to the distributed control of robotic networks offers a broad set of tools for understanding coordination algorithms, determining their correctness, and assessing their complexity; and it analyzes various cooperative strategies for tasks such as consensus, rendezvous, connectivity maintenance, deployment, and boundary estimation.
References
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Journal ArticleDOI
Mobile Robotic Sensors for Environmental Monitoring using Gaussian Markov Random Field
TL;DR: It is proposed to employ Gaussian Markov random field represented on an irregular discrete lattice by using the stochastic partial differential equations method to model the physical spatial field and derives a GMRF-based approach to effectively predict the field at unmeasured locations, given available observations.
Journal ArticleDOI
Clustering and supervisory voltage control in power systems
TL;DR: A rule-based decentralized control strategy is proposed for effective management of bus voltages in the weakly coupled zones that are obtained as a result of the clustering process, which results in excellent identification of mutually decoupled sub-networks within a large power network.
Proceedings ArticleDOI
Distributed multi-robot coverage using micro aerial vehicles
Alessandro Renzaglia,Lefteris Doitsidis,Savvas A. Chatzichristofis,Agostino Martinelli,Elias B. Kosmatopoulos +4 more
TL;DR: This paper presents a solution to the problem of positioning a team of Micro Aerial Vehicles for a surveillance task in an environment of arbitrary and unknown morphology, and proposes a distributed extension of the previous work based on the Cognitive Adaptive Optimization algorithm.
Journal ArticleDOI
Distributed connected coverage control for groups of mobile agents
Xiaoli Li,Yugeng Xi +1 more
TL;DR: This article deals with the self-deployment problem of mobile agents to achieve a sensing coverage over the environment as large as possible, while keeping the underlying time-variant communication topology connected all the time.
Journal ArticleDOI
Distributed Environmental Monitoring With Finite Element Robots
TL;DR: A distributed finite element algorithm that allows swarms of mobile robots to persistently monitor environmental quantities such as temperature or salinity demonstrates that, as the number of robots increases, the memory and communication requirements of each individual robot decrease until reaching a minimum, after which the resolution of the environmental model increases.
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