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Decentralized, Adaptive Coverage Control for Networked Robots
, 2007
"... A decentralized, adaptive control law is presented to drive a network of mobile robots to an optimal sensing configuration. The control law is adaptive in that it uses sensor measurements to learn an approximation of the distribution of sensory information in the environment. It is decentralized in ..."
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Cited by 44 (7 self)
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A decentralized, adaptive control law is presented to drive a network of mobile robots to an optimal sensing configuration. The control law is adaptive in that it uses sensor measurements to learn an approximation of the distribution of sensory information in the environment. It is decentralized in that it requires only information local to each robot. The controller is then improved upon by implementing a consensus algorithm in parallel with the learning algorithm, greatly increasing parameter convergence rates. Convergence and consensus of parameters is proven. Finally, several variations on the learning algorithm are explored with a discussion of their stability in closed loop. The controller with and without parameter consensus is demonstrated in numerical simulations. These techniques are suggestive of broader applications of adaptive control methodologies to decentralized control problems in unknown dynamical environments. 1
Distributed control of robotic networks: a mathematical approach to motion coordination algorithms
, 2009
"... (i) You are allowed to freely download, share, print, or photocopy this document. (ii) You are not allowed to modify, sell, or claim authorship of any part of this document. (iii) We thank you for any feedback information, including errors, suggestions, evaluations, and teaching or research uses. 2 ..."
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Cited by 41 (1 self)
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(i) You are allowed to freely download, share, print, or photocopy this document. (ii) You are not allowed to modify, sell, or claim authorship of any part of this document. (iii) We thank you for any feedback information, including errors, suggestions, evaluations, and teaching or research uses. 2 “Distributed Control of Robotic Networks ” by F. Bullo, J. Cortés and S. Martínez
An overview of recent progress in the study of distributed multiagent coordination
, 2012
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Decentralized, adaptive control for coverage with networked robots
 In Robotics and Automation, 2007 IEEE International Conference on
, 2007
"... AbstractA decentralized, adaptive control law is presented to drive a network of mobile robots to a nearoptimal sensing configuration. The control law is adaptive in that it integrates sensor measurements to provide a converging estimate of the distribution of sensory information in the environme ..."
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Cited by 31 (10 self)
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AbstractA decentralized, adaptive control law is presented to drive a network of mobile robots to a nearoptimal sensing configuration. The control law is adaptive in that it integrates sensor measurements to provide a converging estimate of the distribution of sensory information in the environment. It is decentralized in that it requires only information local to each robot. A Lyapunovtype proof is used to show that the control law causes the network to converge to a nearoptimal sensing configuration, and the controller is demonstrated in numerical simulations. This technique suggests a broader application of adaptive control methodologies to decentralized control problems in unknown dynamical environments.
A Gradient Optimization Approach to Adaptive multirobot control
, 2009
"... This thesis proposes a unified approach for controlling a group of robots to reach a goal configuration in a decentralized fashion. As a motivating example, robots are controlled to spread out over an environment to provide sensor coverage. This example gives rise to a cost function that is shown to ..."
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Cited by 7 (2 self)
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This thesis proposes a unified approach for controlling a group of robots to reach a goal configuration in a decentralized fashion. As a motivating example, robots are controlled to spread out over an environment to provide sensor coverage. This example gives rise to a cost function that is shown to be of a surprisingly general nature. By changing a single free parameter, the cost function captures a variety of different multirobot objectives which were previously seen as unrelated. Stable, distributed controllers are generated by taking the gradient of this cost function. Two fundamental classes of multirobot behaviors are delineated based on the convexity of the underlying cost function. Convex cost functions lead to consensus (all robots move to the same position), while any other behavior requires a nonconvex cost function. The multirobot controllers are then augmented with a stable online learning mechanism to adapt to unknown features in the environment. In a sensor coverage application, this allows robots to learn where in the environment they are most needed, and to aggregate in those areas. The learning mechanism uses communica
Global stabilization of complex networks with . . .
, 2009
"... ... for instance, to a chaotic trajectory of the uncoupled systems, if and only if the pinned vertex set can access all other vertices in the digraph. Furthermore, in the bigraph case, the analytical estimation of the stabilizability’s lower bound suggests that an optimal pinning strategy should tak ..."
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Cited by 6 (1 self)
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... for instance, to a chaotic trajectory of the uncoupled systems, if and only if the pinned vertex set can access all other vertices in the digraph. Furthermore, in the bigraph case, the analytical estimation of the stabilizability’s lower bound suggests that an optimal pinning strategy should take not only the vertex degree, but also the shortest path between pairs of vertices into considerations.
Cooperative Control with Adaptive Graph Laplacians for Spacecraft Formation Flying
"... This paper investigates exact nonlinear dynamics and cooperative control for spacecraft formation flying with Earth oblateness (J2 perturbation) and atmospheric drag effects. The nonlinear dynamics for chief and deputy motions are derived by using Gauss’ variational equation and the EulerLagrangia ..."
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Cited by 3 (0 self)
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This paper investigates exact nonlinear dynamics and cooperative control for spacecraft formation flying with Earth oblateness (J2 perturbation) and atmospheric drag effects. The nonlinear dynamics for chief and deputy motions are derived by using Gauss’ variational equation and the EulerLagrangian formulation, respectively. The proposed cooperative control employs adaptive timevarying Laplacian gains. The tracking and diffusive coupling gains are adapted by the synchronization/tracking errors and distancebased connectivity, thereby defining a timevarying network topology. Moreover, the proposed method relaxes the network structure requirement and permits an unbalanced graph. Nonlinear stability is proven by contraction analysis and incremental inputtostate stability. Numerical examples show the effectiveness of the proposed method.
Mean Field LQG Control in LeaderFollower Stochastic MultiAgent Systems: Likelihood Ratio Based Adaptation
"... Abstract—We study large population leaderfollower stochastic multiagent systems where the agents have linear stochastic dynamics and are coupled via their quadratic cost functions. The cost of each leader is based on a tradeoff between moving toward a certain reference trajectory which is unknown ..."
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Abstract—We study large population leaderfollower stochastic multiagent systems where the agents have linear stochastic dynamics and are coupled via their quadratic cost functions. The cost of each leader is based on a tradeoff between moving toward a certain reference trajectory which is unknown to the followers and staying near their own centroid. On the other hand, followers react by tracking a convex combination of their own centroid and the centroid of the leaders. We approach this large population dynamic game problem by use of socalled Mean Field (MF) linearquadraticGaussian (LQG) stochastic control theory. In this model, followers are adaptive in the sense that they use a likelihood ratio estimator (on a sample population of the leaders ’ trajectories) to identify the member of a given finite class of models which is generating the reference trajectory of the leaders. Under appropriate conditions, it is shown that the true reference trajectory model is identified by each follower in finite time with probability one as the leaders ’ population goes to infinity. Furthermore, we show that the resulting sets of mean field control laws for both leaders and adaptive followers possess an almost sureNash equilibrium property for a system with population where goes to zero as goes to infinity. Numerical experiments are presented illustrating the results. Index Terms—Adaptive control, leaderfollower collective behavior, likelihood ratio based adaptation, mean field (MF) stochastic control, Nash equilibria, stochastic optimal control. I.