This paper addresses the problem of distributed state estimation in formations of vehicles with fixed *** or more vehicles have access to the measurements of their own state,while the rest must rely on locally availab...
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ISBN:
(纸本)9781509009107
This paper addresses the problem of distributed state estimation in formations of vehicles with fixed *** or more vehicles have access to the measurements of their own state,while the rest must rely on locally available relative measurements and limited communication to estimate their own *** proposed solution consists in the implementation of a local state observer,which is embedded in each vehicle,and a protocol specifying the observer updating *** local state observer structure is given,and protocols are presented with two scenarios considered:whether the vehicles' velocity measurements are available to embedded sensors or *** has been shown that,by using the given observer structure and the proposed protocols,the observers cooperate to work out the formation state estimates,which converge to the actual states of the vehicle formation in finite *** examples are included so as to demonstrate the effectiveness of the theoretical results.
With the wide use of RDF data, searching and ranking semantic data with SPARQL has become a research hot-spot. While there is no much research work on top-k join queries optimization in RDF native stores. This paper p...
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We investigate the trajectory tracking problem of vertical take-off and landing (VTOL) unmanned aerial vehicles (UAV), and propose a practical disturbance rejection control strategy. Firstly, the nonlinear error model...
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When controlling a complex networked system it is not feasible to control the full network because many networks, including biological, technological, and social systems, are massive in size and complexity. But neithe...
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When controlling a complex networked system it is not feasible to control the full network because many networks, including biological, technological, and social systems, are massive in size and complexity. But neither is it necessary to control the full network. In complex networks, the giant connected components provide the essential information about the entire system. How to control these giant connected components of a network remains an open question. We derive the mathematical expression of the degree distributions for four types of giant connected components and develop an analytic tool for studying the controllability of these giant connected components. We find that for both Erdős-Rényi (ER) networks and scale-free (SF) networks with p fraction of remaining nodes, the minimum driver node density to control the giant component first increases and then decreases as p increases from zero to one, showing a peak at a critical point p=pm. We find that, for ER networks, the peak value of the driver node density remains the same regardless of its average degree 〈k〉 and that it is determined by pm〈k〉. In addition, we find that for SF networks the minimum driver node densities needed to control the giant components of networks decrease as the degree distribution exponents increase. Comparing the controllability of the giant components of ER networks and SF networks, we find that when the fraction of remaining nodes p is low, the giant in-connected, out-connected, and strong-connected components in ER networks have lower controllability than those in SF networks.
This paper studies the limit set of multi-agent system with finite states, in which the system is converted into a linear system through an expansion of space. Then, the structure properties of the system matrix are i...
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This paper studies the limit set of multi-agent system with finite states, in which the system is converted into a linear system through an expansion of space. Then, the structure properties of the system matrix are investigated, and the relationships between the eigenvalues and the limit set are developed. As an application, the nilpotent problem of elementary cellular automata(ECA) known as algorithmically undecidable is considered, and all the nilpotent ECA are found out which consists of rules 0, 8, 64, 239, 253, 255.
In this paper,we discuss consensus problems for antagonistic networks with double integrator *** cases are analyzed:(1) undirected graphs with fixed topology on antagonistic networks;(2) undirected graphs with fixed t...
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ISBN:
(纸本)9781509009107
In this paper,we discuss consensus problems for antagonistic networks with double integrator *** cases are analyzed:(1) undirected graphs with fixed topology on antagonistic networks;(2) undirected graphs with fixed topology and time-delay on antagonistic *** both cases,distributed consensus protocols are proposed,with sufficient and necessary conditions *** is proved that the largest tolerable time-delay is only related to the largest eigenvalue of the graph ***,simulations are provided to demonstrate the obtained theoretical results.
In recommendation system,sparse data and cold-start user have always been a challenging *** a linear upper confidence bound(UCB) bandit approach as the item selection strategy based on the user historical ratings and ...
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In recommendation system,sparse data and cold-start user have always been a challenging *** a linear upper confidence bound(UCB) bandit approach as the item selection strategy based on the user historical ratings and user-item context,we model the recommendation problem as a multi-arm bandit(MAB)problem in this *** the engine to recommend while it learns,we adopt probabilistic matrix factorization(PMF) in this strategy learning phase after observing the *** particular,we propose a new approach to get the upper bound statistics out of latent feature *** the experiment,we use two public datasets(Netfilx and MovieLens) to evaluate our proposed *** model shows good results especially on cold-start users.
3D mapping for unknown environments is a crucial issue for mobile service robots,which has many important applications in navigation and path *** this paper,we use Microsoft kinect sensor mounted on a mobile robot to ...
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ISBN:
(纸本)9781509009107
3D mapping for unknown environments is a crucial issue for mobile service robots,which has many important applications in navigation and path *** this paper,we use Microsoft kinect sensor mounted on a mobile robot to obtain3 D *** the process,consistent frames of RGB-D data are captured and *** Closest Point(ICP)algorithm and incremental registration method are employed to perform the fine ***,we use OctoMap to store 3D map of octree representation,an efficient data ***,our results demonstrate the practical applicability and effectiveness of the proposed approach.
This paper presents an approach to uncertainty-optimal path planning for a mobile manipulator based on localizability and guarantee manipulability simultaneously. information matrix is used to indicate the localizabil...
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ISBN:
(纸本)9781467389600
This paper presents an approach to uncertainty-optimal path planning for a mobile manipulator based on localizability and guarantee manipulability simultaneously. information matrix is used to indicate the localizability or localization uncertainty in a known map and the cubic Bezier spline is used to represent the path for platform to check. Given a path of end-effector, this algorithm guarantees the manipulability greater than a given value, chooses the uncertainty of end-effector as the optimization index, uses particle swarm optimization algorithm to find an optimal path for platform to track. Simulations and experiments are presented that show the algorithm has the capability to reduce the uncertainty of platform and end-effector.
Current urban transportation system applications often deal with the scenarios of large scale multiple-layer road networks, with the requirement of qualified overall prediction accuracy. However, the common related st...
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ISBN:
(纸本)9781509018901
Current urban transportation system applications often deal with the scenarios of large scale multiple-layer road networks, with the requirement of qualified overall prediction accuracy. However, the common related studies mostly focus on the prediction of individual road segments or intersections. In this paper, we propose a generic method for large scale coupling urban traffic to forecast the future states of the overall network. In the preliminary stage, the features are extracted using unsupervised method. Subsequently, an ensemble learning via matching the historical data and the current information are implemented to predict the traffic state transition. The data set for experiment is composed of multiple layers of road network in Shanghai, which are collected by loop detectors every two minutes. We verify the effectiveness of the developed method by evaluating the experimental prediction performance using the actual data. The results also indicate that adding the interactive features of the coupling road network to the forecasting algorithm can generate superior prediction accuracy comparing with the independent prediction method.
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