This paper addresses the decentralized task allocation problem of multirobot systems, in which the objective is to maximize the total task assignments, i.e., the number of tasks that can be successfully executed by al...
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ISBN:
(纸本)9788993215243
This paper addresses the decentralized task allocation problem of multirobot systems, in which the objective is to maximize the total task assignments, i.e., the number of tasks that can be successfully executed by all robotic vehicles under the time constraints of tasks and battery limits of vehicles. Based on the state-of-the-art performance impact (PI) algorithm, a novel extension named PI for minimizing traveling time (PI-minTravel) is proposed in this paper. With the proposed PI-minTravel, tasks that are close enough to the last task of each vehicle are assigned to the vehicle first, so that the total traveling time of all vehicles can be minimized. Due to the limited fuel of each vehicle, less traveling time will leave more time to execute tasks, then more tasks can be executed, especially when the ratio of tasks to vehicles is high. Extensive simulation results show that the proposed PI-minTravel can assign more tasks and converge within fewer iterations compared with PI algorithm, while it can assign fewer tasks but converge within much fewer iterations compared with PI for maximizing assignments (PI-maxAss) algorithm.
Purpose - Coexistence of various wireless access networks and the ability of mobile terminals to switch between them make an optimal selection of serving networks for multicast groups a challenging problem. Since opti...
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Purpose - Coexistence of various wireless access networks and the ability of mobile terminals to switch between them make an optimal selection of serving networks for multicast groups a challenging problem. Since optimal network selection requires large dimensions of data to be collected from several network locations and sent between several network components, the scalability can easily become a bottleneck in large-scale systems. Therefore, reducing data exchange within heterogeneous wireless networks is important. The paper aims to discuss these issues. Design/methodology/approach - The authors study the decision-making process and the data that need to be sent between different network components. To analyze the operation of the wireless heterogeneous network, the authors built a mathematical model of the network. The objective is defined as a minimization of multicast streams in the system. To evaluate the heuristic solutions, the authors define the upper and lower bounds to their operation. Findings - The proposed heuristic solutions substantially reduce the usage of bandwidth in mobile networks and exchange of information between the network components. Originality/value - The authors proposed the approach that allows network selection in a decentralized manner with only limited information shared among the decision makers. The authors studied how different sets of information available to decision makers influenced the performance of the system. The work also investigates the usage of multiple paths for multicast in heterogeneous mobile environments.
We propose a novel distributed generalized expectation maximization (EM) method for non-cooperative target localization using a wireless sensor network. We consider the scenario where few or no sensors receive line-of...
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ISBN:
(纸本)9781479916344
We propose a novel distributed generalized expectation maximization (EM) method for non-cooperative target localization using a wireless sensor network. We consider the scenario where few or no sensors receive line-of-sight signals from the target. Each sensor is able to measure the time difference of arrival of the target's signal with respect to a reference node, as well as the angle of arrival of the target's signal. We show that our distributed algorithm converges, and simulation results suggest that our method achieves an accuracy close to the centralized EM algorithm. We apply the distributed EM algorithm to a set of experimental measurements, which confirm that the algorithm is able to localize a RF target in a realistic non-line-of-sight scenario.
We propose distributed algorithms for high-dimensional sparse optimization. In many applications, the parameter is sparse but high-dimensional. This is pathological for existing distributed algorithms as the latter re...
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ISBN:
(纸本)9781479999880
We propose distributed algorithms for high-dimensional sparse optimization. In many applications, the parameter is sparse but high-dimensional. This is pathological for existing distributed algorithms as the latter require an information exchange stage involving transmission of the full parameter, which may not be sparse during the intermediate steps of optimization. The novelty of this work is to develop communication efficient algorithms using the stochastic Frank-Wolfe (sFW) algorithm, where the gradient computation is inexact but controllable. For star network topology, we propose an algorithm with low communication cost and establishes its convergence. The proposed algorithm is then extended to perform decentralized optimization on general network topology. Numerical experiments are conducted to verify our findings.
This paper proposes a new type of range-free localization method based on affine transformation. Nodes extract subgraphs with a grid topology from a sensor network and assign x-y coordinates to themselves in a decentr...
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ISBN:
(纸本)9781467324472;9781467324458
This paper proposes a new type of range-free localization method based on affine transformation. Nodes extract subgraphs with a grid topology from a sensor network and assign x-y coordinates to themselves in a decentralized manner. The nodes estimate their positions using an affine transformation based on the mapping of the physical positions and the x-y coordinates of three anchors in an extracted graph. In contrast with multilateration-based localization methods, the proposed method works well even in a non-convex hull deployment, such as a terrain with big regions without sensors. We provide a theoretical analysis and simulation results. We also present a strategy for minimizing the position estimation error and maximizing the coverage of the proposed method. In the simulation results, the position estimation error is 0.18 (normalized by the radio communication range) and the coverage is almost 100% in a non-convex hull deployment.
Under appropriate cooperation protocols and parameter choices, fully decentralized solutions for stochastic optimization have been shown to match the performance of centralized solutions and result in linear speedup (...
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ISBN:
(纸本)9781509066315
Under appropriate cooperation protocols and parameter choices, fully decentralized solutions for stochastic optimization have been shown to match the performance of centralized solutions and result in linear speedup (in the number of agents) relative to noncooperative approaches in the strongly-convex setting. More recently, these results have been extended to the pursuit of first-order stationary points in non-convex environments. In this work, we examine in detail the dependence of second-order convergence guarantees on the spectral properties of the combination policy for non-convex multi agent optimization. We establish linear speedup in saddle-point escape time in the number of agents for symmetric combination policies and study the potential for further improvement by employing asymmetric combination weights. The results imply that a linear speedup can be expected in the pursuit of second-order stationary points, which exclude local maxima as well as strict saddle-points and correspond to local or even global minima in many important learning settings.
A novel decentralized method for harmonics detection and suppression is proposed. Compared with the traditional centralized style, harmonics optimal estimation task in micro-grid system is scattered to smart phasor me...
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ISBN:
(纸本)9781538635247
A novel decentralized method for harmonics detection and suppression is proposed. Compared with the traditional centralized style, harmonics optimal estimation task in micro-grid system is scattered to smart phasor measurement unit (PMU) nodes without a monitoring host. Similar to the structure, mechanism and characteristics of biological communities, a smart PMU can communicate with adjacent nodes and operate collaboratively to complete harmonics optimal estimation in a new fully distributed flat network. The task is formulated as a constrained optimization problem conducted using basic physical equations and solved by decentralized approach with varying penalty parameter. Convergence property of the novel method is analyzed theoretically. Simulation results of the harmonics estimation in micro-grid system illustrate the effectiveness of the proposed method.
This paper introduces algorithms for the decentralized low-rank matrix completion problem. Assume a low-rank matrix W - [W-1, W-2, ..., W-L]. In a network, each agent l observes some entries of W-l. In order to recove...
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ISBN:
(纸本)9781467300469
This paper introduces algorithms for the decentralized low-rank matrix completion problem. Assume a low-rank matrix W - [W-1, W-2, ..., W-L]. In a network, each agent l observes some entries of W-l. In order to recover the unobserved entries of W via decentralized computation, we factorize the unknown matrixWas the product of a public matrix X, common to all agents, and a private matrix Y = [Y-1, Y-2, ..., Y-L], where Y-l is held by agent l. Each agent l alternatively updates Y-l and its local estimate of X while communicating with its neighbors toward a consensus on the estimate. Once this consensus is ( nearly) reached throughout the network, each agent l recoversW(l) = XYl and thus W is recovered. The communication cost is scalable to the number of agents, and W-l and Y-l are kept private to agent l to a certain extent. The algorithm is accelerated by extrapolation and compares favorably to the centralized code in terms of recovery quality and robustness to rank over-estimate.
When using multiple robotic agents for waypoint-based exploration or coverage tasks, collision avoidance between agents is an important issue. With a centralized planner, this issue arises only at one central instance...
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ISBN:
(纸本)9781479977871
When using multiple robotic agents for waypoint-based exploration or coverage tasks, collision avoidance between agents is an important issue. With a centralized planner, this issue arises only at one central instance. However, when using asynchronous and/or decentralized algorithms, decentralized methods for collision avoidance need to be used. We propose a novel approach based on the asynchronous backtracking (ABT) algorithm which provides decentralized constraint satisfaction called continuous ABT(C-ABT). C-ABT is a continuous extension to ABT intended as a collision avoidance layer for existing multi-agent waypoint navigation. It extends ABT in the sense that participating agents know when they found a valid solution. In this case, agents can safely move to their selected waypoints. While ABT only finds one static solution, C-ABT is suited for continuously providing new waypoints for all agents. By using the concept of local neighborhoods, C-ABT is also scalable to swarms of arbitrary size.
In this paper we address the trajectory tracking problem for groups of mobile robots. We consider trajectories described by completely arbitrary shaped closed curves. The proposed control strategy is a completely dece...
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ISBN:
(纸本)9781424466757
In this paper we address the trajectory tracking problem for groups of mobile robots. We consider trajectories described by completely arbitrary shaped closed curves. The proposed control strategy is a completely decentralized algorithm, and does not require any global synchronization. The desired behavior is obtained by means of some properly designed artificial potential functions.
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