Distributed Connectivity Control of Mobile Networks

Control of mobile networks raises fundamental and novel problems in controlling the structure of the resulting dynamic graphs. In particular, in applications involving mobile sensor networks and multiagent systems, a great new challenge is the development of distributed motion algorithms that guarantee connectivity of the overall network. Motivated by the inherently discrete nature of graphs as combinatorial objects, we address this challenge using a key control decomposition. First, connectivity control of the network structure is performed in the discrete space of graphs and relies on local estimates of the network topology used, along with algebraic graph theory, to verify link deletions with respect to connectivity. Tie breaking, when multiple such link deletions can violate connectivity, is achieved by means of gossip algorithms and distributed market-based control. Second, motion control is performed in the continuous configuration space, where nearest-neighbor potential fields are used to maintain existing links in the network. Integration of the earlier controllers results in a distributed, multiagent, hybrid system, for which we show that the resulting motion always ensures connectivity of the network, while it reconfigures toward certain secondary objectives. Our approach can also account for communication time delays as well as collision avoidance and is illustrated in nontrivial computer simulations.      

Modeling and Adaptive Neural Network Control for a Soft Robotic Arm With Prescribed Motion Constraints

This paper presents a dynamic model and performance constraint control of a line-driven soft robotic arm. The dynamics model of the soft robotic arm is established by combining the screw theory and the Cosserat theory. The unmodeled dynamics of the system are considered, and an adaptive neural network controller is designed using the backstepping method and radial basis function neural network. The stability of the closed-loop system and the boundedness of the tracking error are verified using Lyapunov theory. The simulation results show that our approach is a good solution to the motion constraint problem of the line-driven soft robotic arm.      

Vision-Based Robotic Motion Control for Non-autonomous Environment

A visual servo control system with SOPC structure is implemented on a retrofitted Mitsubishi Movemaster RV-M2 robotic system. The hardware circuit has the functions of quadrature encoder decoding, limit switch detecting, pulse width modulation (PWM) generating and CMOS image signal capturing. The software embedded in Nios II micro processor has the functions of using UART to communicate with PC, robotic inverse kinematics calculation, robotic motion control schemes, digital image processing and gobang game AI algorithms. The digital hardware circuits are designed by using Verilog language, and programs in Nios II micro processor are coded with C language. An Altera Statrix II EP2S60F672C5Es FPGA chip is adopted as the main CPU of the development board. A CMOS color image sensor with 356 ×292 pixels resolution is selected to catch the environment time-varying change for robotic vision-based servo control. The system performance is evaluated by experimental tests. A gobang game is planned to reveal the visual servo robotic motion control objective in non-autonomous environment. Here, a model-free intelligent self-organizing fuzzy control strategy is employed to design the robotic joint controller. A vision based trajectory planning algorithm is designed to calculate the desired angular positions or trajectory on-line of each robotic joint. The experimental results show that this visual servo control robot has reliable control actions.     

基于图神经网络的最大化代数连通度算法

随着智能体数量的增加, 多智能体系统中潜在的通信链路数量呈指数级增长. 过多冗余链路的存在给系统带来了大量的能源浪费和维护成本, 而盲目地去除链路又会降低系统的稳定性和安全性. 代数连通度是衡量图连通性的重要指标之一. 然而, 传统的半正定规划(SDP)方法和启发式算法在求解大规模场景下的最大化代数连通度问题时非常耗时. 在本文中, 我们提出了一种监督式的图神经网络模型来优化多智能体系统的代数连通度. 我们将传统的SDP方法应用于小规模任务场景中, 得到足够丰富的训练样本和标签. 在此基础上, 我们训练了一个图神经网络模型, 该模型可用于更大规模的任务场景中. 实验结果表明, 当需要去除15条边时, 我们的模型的平均性能达到了传统SDP方法的98.39%. 此外, 我们的模型计算时间极其有限, 可以推广到实时场景中去.     

基于卷积神经网络的Leap Motion运动数据优化网络

为提高Leap Motion设备的采集精准度,解决自遮挡、采样频率不稳定等设备固有问题,首先,设计了使用Leap Motion和动作捕捉设备的手部多模态同步运动采集方案,采集了日常动作数据集;其次,提出了基于卷积神经网络(convolutional neural network,CNN)的Leap Motion手部运动...     

Connectivity and Inference Problems for Temporal Networks

Many network problems are based on fundamental relationships involving time. Consider, for example, the problems of modeling the flow of information through a distributed network, studying the spread of a disease through a population, or analyzing the reachability properties of an airline timetable. In such settings, a natural model is that of a graph in which each edge is annotated with a time label specifying the time at which its endpoints “communicated.” We will call such a graph a temporal network. To model the notion that information in such a network “flows” only on paths whose labels respect the ordering of time, we call a path time-respecting if the time labels on its edges are non-decreasing. The central motivation for our work is the following question: how do the basic combinatorial and algorithmic properties of graphs change when we impose this additional temporal condition? The notion of a path is intrinsic to many of the most fundamental algorithmic problems on graphs; spanning trees, connectivity, flows, and cuts are some examples. When we focus on time-respecting paths in place of arbitrary paths, many of these problems acquire a character that is different from the traditional setting, but very rich in its own right. We provide results on two types of problems for temporal networks. First, we consider connectivity problems, in which we seek disjoint time-respecting paths between pairs of nodes. The natural analogue of Menger's Theorem for node-disjoint paths fails in general for time-respecting paths; we give a non-trivial characterization of those graphs for which the theorem does hold in terms of an excluded subdivision theorem, and provide a polynomial-time algorithm for connectivity on this class of graphs. (The problem on general graphs is NP-complete.) We then define and study the class of inference problems, in which we seek to reconstruct a partially specified time labeling of a network in a manner consistent with an observed history of information flow.     

带有控制约束的离散时间系统的可控性可达性强可连性

本文研究带有控制约束的离散时间线性系统的可控性、可达性及强可连性。给出判别这些性质的充分必要条件,并指出它们之间的关系。     

二进制递归网络的连通性

二进制递归网络是超立方体的一类特殊变体,它具有很多良好的网络特性。网络的连通性是衡量网络结构通信能力的一个重要性能,虽然到目前为止已知的一些二进制递归网 络的连通性都已被研究过,但这些研究只是针对个体进行的,并不能代表所有二进制递归网络的连通特性。本文通过证明任何一个二进制递归网络中的每对顶点之间只能存在在”条顶点不交路,得到了整个二进制递归网络的点和边连通度皆为”的重要结论。     

Potential Fields for Maintaining Connectivity of Mobile Networks

The control of mobile networks of multiple agents raises fundamental and novel problems in controlling the structure of the resulting dynamic graphs. In this paper, we consider the problem of controlling a network of agents so that the resulting motion always preserves the connectivity property of the network. In particular, the connectivity condition is translated to differentiable constraints on individual agent motion by considering the dynamics of the Laplacian matrix and its spectral properties. Artificial potential fields are then used to drive the agents to configurations away from the undesired space of disconnected networks while avoiding collisions with each other. We conclude by illustrating a class of interesting problems that can be achieved while preserving connectivity constraints.     

非完整约束下的机器人运动规划算法

由非完整约束定义提出一种改进RRT(快速搜索随机树)算法,解决受动力学约束的移动机器人运动规划问题.该算法将移动机器人的非完整约束条件与RRT搜索算法相结合.针对RRT算法在全局状态空间均匀随机搜索导致算法无谓耗费代价大的缺陷,引入目标偏向思想,并选择计算复杂度低的距离参数提高求解速度.通过几类典型的非完整约束下的机器...     

Ad hoc网络与Internet互联研究

Adhoc网络是一种特殊的无线网络,具有无中心、多跳、自组织的特性,这些特性为其获得访问Internet的能力来了很大麻烦。Adhoc节点如何配置IP地址,如何获得网关信息,如何提高其性能,如何保证访问Internet时的安全性,如何适应IP协议族中的各种协议和服务例如NAT和DNS,都受到其特性的影响。解决上述问题的一般方法得到了讨论并指出了发展方向。     

基于几何约束的三次代数曲线插值

尽管三次参数曲线在曲线曲面造型中扮演着主要角色,但是计算几何专家也一直没有放弃对三次代数曲线的性质及应用进行研究。该文首先综述了近年来有关三次代数曲线研究的最新进展,对各主要方法的优缺点进行了客观的评价。然后提出了一种基于几何约束的三次代数曲线的插值方法,该方法守完全通过几何量如控制顶点、切线和曲率来控制三次代数曲线的形状,使得对三次代数曲线的编辑与对三次B－样条曲线的编辑一样灵活方便。该文提出的代数曲线的结构有两种,一种是插值平面上四点及两端点切线的三次代数曲线;另一种是插值两端点、两切线及两曲率的三次代数曲线。在第二种情况下对曲率的情况进行了详细的分类。并且从理论上对曲线的连续性及保凸性进行了严格的证明。     

Algebraic and PDE Approaches for Lattice Scale-Spaces with Global Constraints

This paper begins with analyzing the theoretical connections between levelings on lattices and scale-space erosions on reference semilattices. They both represent large classes of self-dual morphological operators that exhibit both local computation and global constraints. Such operators are useful in numerous image analysis and vision tasks including edge-preserving multiscale smoothing, image simplification, feature and object detection, segmentation, shape and motion analysis. Previous definitions and constructions of levelings were either discrete or continuous using a PDE. We bridge this gap by introducing generalized levelings based on triphase operators that switch among three phases, one of which is a global constraint. The triphase operators include as special cases useful classes of semilattice erosions. Algebraically, levelings are created as limits of iterated or multiscale triphase operators. The subclass of multiscale geodesic triphase operators obeys a semigroup, which we exploit to find PDEs that can generate geodesic levelings and continuous-scale semilattice erosions. We discuss theoretical aspects of these PDEs, propose discrete algorithms for their numerical solution which converge as iterations of triphase operators, and provide insights via image experiments.     

基于代数连通度的域内节能路由算法

通过节能路由算法减少网络能耗是网络中需要解决的一个关键性的科学问题。如今已有的节能方案都是在已知流量矩阵的前提下研究网络节能,由于实时流量难以获取,使得这些方案都难以在实际中部署。因此,文中提出一种基于代数连通度的域内节能方案(Intra-domain Energy Efficient Routing Scheme Based on Algebraic Connectivity,EERSBAC)。EERSBAC不需要网络中的实时流量矩阵,仅依靠网络中的拓扑结构就可以实现节能。首先,提出链路关键度模型,利用链路关键度模型计算出网络中所有链路的重要程度;然后,提出代数连通度模型,利用代数连通度模型可以定量的衡量网络的连通性能。实验结果表明,EERSBAC不仅能够降低网络能耗,而且具有较小的路径拉伸度。     

无线传感器网络的连通模型

针对无线传感器网络连通性研究中缺乏数学模型的问题,在对网络节点的连通关系进行数学描述的基础上,提出一种无线传感器网络的连通模型,模型将单个节点的状态信息表示为一个向量,包含节点的工作状态、位置状态、能量状态及节点之间连通关系信息,整个网络表示为由各节点状态向量组成的矩阵.通过举例说明了模型在分析网络连通拓扑结构和网络通讯能量控制问题上的适用性.该模型的提出为从数学角度描述和评估无线传感器网络的连通性能提供了一个平台.关     

Distributed Enforcement of Integrity Constraints

This paper deals with distributed transactions that employ self-stabilization in the sense that the transaction is possibly extended by corrective actions (stabilizers) to repair violated integrity constraints. The implementation of such an extended distributed transaction is discussed in both macro (inter-transaction) and micro (intra-transaction) aspects. In the macro level we discuss the distributed stabilizers execution protocol, the relationship with the 2PC algorithm (the reinfection problem) and cooperative constraint evaluation and transaction termination. The Micro-level issues discussed relate to the interaction among different stabilizers. Three different alternative protocols are discussed: the basic S-Lock protocol, the extended S-Lock protocol, and the cluster graph based protocol. These protocols are compared and discussed. The paper concludes with discussion of some general properties such as:determinism and termination.     

Algebraic Connectivity Estimation Based on Decentralized Inverse Power Iteration

In this work, we propose a new scheme to estimate the algebraic connectivity of the graph describing the network topology of a multi‐agent system. We consider network topologies modeled by undirected graphs. The main idea is to propose a new decentralized conjugate gradient algorithm and a decentralized compound inverse power iteration scheme. The matrix inversion computation in this scheme is replaced by solving the non‐homogeneous linear equations relying on the proposed decentralized conjugate gradient algorithm. With this scheme, we can achieve a fast convergence rate in estimating the algebraic connectivity by setting the parameter μ properly. Simulation results demonstrate the effectiveness of the proposed scheme.     

基于时空约束的运动编辑和运动重定向

近年来兴起的运动捕获已成为人体动画中最有应用前景的技术之一，目前运动捕获手段很多，但是通常成本高，而且捕获到的运动类型比较单一，为了提高运动捕获数据的重用性，生成与复杂场景协调的多样的动画，必须对捕获的运动数据进行编辑和重定向处理，介绍了一种基于时空约束的运动编辑和运动重定向方法，通过规定一组时空约束条件，建立相应的目标函数，采用逆向运动学和数值优化方法求解出满足约束条件的运动姿势，实验结果表明，该方法可以生成多种满足不同场景婪泊逼真运动，提出了数据的重用性。     

从表观运动来看运动的基本约束

本文综述了生物学和心理物理学中有关运动视觉的发现,并将它们总结为层次结构基元模型和九条运动基本约束.给出了一个以此为基础的运动分析算法和初步的实验结果.