基于ROS的服务机器人关键技术研究与实现

针对服务机器人服务业务的无监督实现问题,设计并实现了一种基于ROS(机器人操作系统)的智能服务机器人系统。该系统包括管理中心、机器人本体和客户端,采用基于ROS系统的node节点分布式架构。管理中心由服务器组成,管理中心是任务管理与执行调度的核心组成部分,负责对系统内各种资源进行管理和调度,机器人本体使用高性能核心处理器搭载环境传感器,通过基于EKF(扩展卡尔曼滤波)的SLAM算法解决移动机器人导航问题,客户端与机器人本体的信息交互采用基于rosbridge的节点通信技术。机器人系统通过语音交互技术实现了语音自主导航。本设计在实验室环境采用Turtlebot机器人进行实际测试,测试结果表明本方案可以初步实现机器人基础服务场景中的自主定位与导航及语音控制功能。     

基于开放式多智能体结构的分布式自主机器人系统

针对多机器人系统的分布式自主控制,本文首先提出了一种开放式的多智能体结构,给出了 设计原则和技术特点．然后面向真实世界的多机器人实时协作任务,采用多台自主移动机器 人构造了一个多机器人系统, 该系统集成了包括机器人视觉、传感器融合、无线通讯网络 以及基于行为控制等多项技术．最后采用基于行为融合的加权方法,实现了多机器人的编队 控制,实验结果表明了上述体系结构与方案的有效性．     

传感器网络基于移动信标改进的DV-Hop定位算法

无线传感器网络节点定位算法的精度多依赖于信标节点的密度,但信标节点成本高,约为普通节点的100倍以上,为了降低定位的成本,提出了一种基于移动信标和DV-Hop的无线传感器网络节点定位算法(MBWDV-Hop)。该算法在DV-Hop定位算法的基础上,利用一个移动的信标节点在网络中按预定的路径移动并不断地广播自己的位置信息,形成多个虚拟信标,未知节点记录到每个虚拟信标的跳数,并采用加权处理的方法计算平均跳距及其与各虚拟信标的距离,最后利用三边测量法计算未知节点的位置信息,实现节点精确定位。由于只采用一个移动信标,降低了定位的成本和布网的复杂度。最后通过仿真证明算法可以提高定位精度,降低定位成本,提高了定位的效率。     

基于局部信息的移动感知网覆盖方法

移动感知网是一个由许多带有传感器的自主移动机器人组成的分布式传感器网络。为了更好地部署这些移动机器人节点,形成最大化覆盖感知区域,提出了一种基于机器人局部信息的分布式感知网覆盖方法。每个节点利用与邻居节点之间的虚拟人工势场产生的虚拟作用力来控制移动节点的运动和节点间的避碰,使移动节点能够在允许的时间内,以较少的能量消耗移动到各自理想的位置。采用李亚普诺夫函数进行了感知网节点势场梯度的理论分析,用计算机仿真实验验证了该方法的有效性,并与模拟退火算法进行了性能比较。     

三维水下移动传感网多目标有向路径覆盖增强研究

水下传感网中实现多个移动目标的协同追踪任是一个技术难题,针对这个问题论文提出了一种分布式的多目标有向路径覆盖增强算法。在实际的三维水下传感网中,水下传感器节点会随着水流运动而移动,被追踪的目标具有自主行动能力。假设移动规律遵从Meandering Current Mobility模型,多个运动目标沿着基于概率的Random Walk移动模型轨迹运动。在论文提出的算法中,覆盖运动路径的传感器节点通过两跳邻居节点范围内的水下传感器节点协同决策来实现最大化路径覆盖,通过调整水下传感器节点自身的有向覆盖方向达到多目标轨迹路径有向覆盖率平均值最大,并使多目标轨迹路径有向覆盖率标准方差尽量小。最后论文通过MATLAB软件仿真来验证分布式覆盖增强算法的有效性,能够显著提高多个移动目标的路径覆盖率。     

一种新的自组装模块化群体机器人——对接机构设计与自组装控制

基于新型自组装模块化群体机器人Sambot,研究并实现了多个机器人之间的自主对接与自组装控制．首先, 提出了一种由对接卡扣与对接卡槽组成的新型对接机构,它可以使多个Sambot在一定对接偏差范围内,从前、后、 左、右4个方向同时进行对接;其次,采用基于行为的控制方法,仅依赖机器人自身红外传感器的局部感知和通信能力, 实现了对接机器人寻找目标、导航和自主对接等自组装行为;最后,成功完成了两个Sambot的自主对接与自组装控制实验． 实验结果表明,本文提出的自组装控制方法可以直接扩展到多个Sambot的情形,来构建任意构型的集合体机器人．     

一种基于分布式智能的网络机器人系统

提出了一种基于网络智能的分布式智能体系,构建并实现了基于分布式智能的网络机器人系统．通过赋予传感器以智能,将网络环境中的不同个体抽象为不同的智能节点．传感智能节点处理和传输多种层次的传感信息,实现传感智能的交互与共享．机器人节点以自主感知能力和“网络即插即用”机制为基础,根据任务的需要以及网络的传输状况,主动从网络中获取相应层次的智能和信息．实验表明,该体系有效地减轻了网络通信负担,提高了网络机器人系统的整体性能和智能水平．     

一种基于CKF的无线传感器网络分布式定位算法

为提高无线传感器网络节点定位的精度,降低算法计算复杂性,提出了一种基于容积卡尔曼滤波的无线传感器网络分布式节点定位算法。该算法假定移动锚节点按预定路径在传感区域移动,并周期性广播自身位置信标信息;每个未知位置节点首先收集多个锚节点信标信息及信号强度信息,然后估算出锚节点信标位置与未知节点的距离,最后在未知节点上运用容积卡尔曼滤波算法完成自身位置的分布式定位。仿真结果表明：本文所提算法具有优良的定位性能,定位精度和无迹卡尔曼滤波算法相当,明显优于极大似然估计定位算法,而计算复杂性则低于无迹卡尔曼滤波算法。     

无线传感器网络分布式的移动节点定位研究

针对WSN野外二维特定应用环境,提出了一种到主信标节点信号强度差定位算法（SSDLB）与运动预测定位算法（MPL）相结合的基于分布式的高覆盖率移动WSN节点定位算法,解决了在定位过程中未知节点在某定位时刻其邻居信标节点的个数小于3个的定位问题,并且避免了传统RSSI定位算法把信号强度值转化成距离再进行定位所带来的计算误差与计算开销,一定程度上提高了节点定位精度和覆盖率。仿真实验表明：此算法在较低的信标节点密度的条件下,能够达到较高的定位精度和定位覆盖率,与传统的RSSI算法相比定位性能有显著的提高。     

激光扫描全局定位传感器的研究

1.引言 自主导航定位是自主移动机器人(或自主移动载体)的基本功能之一.这种功能可以使自主移动机器人(Autonomous Mobile Robot)在作业环境中,依据自身的感知系统确定自身的位置,从而根据任务作出正确的行为决策和路径选择.全局定位就是通过采集环境中地理或人工标志的感知信息,在线地计算出载体在环境参考系中的位置,使机器人系统可以实现基于环境信息的自主导航定位.因此研究全局定位传感器实现移动机器人的自主导航定位对机器人学的发展和促进移动机器人的应用都具有十分重要的意义.     

大规模环境下拓扑地图在线创建与导航.doc

本文提出了一种新型的拓扑地图，该地图用激光的扇区特征和视觉的比例不变特征（SIFT）来联合表示节点。与传统地图相比，该地图在创建过程中不依赖任何人工路标和机器人的全局定位。机器人通过综合考虑单个节点的相似度和不同节点间的空间关系，利用隐马尔可夫模型来提高节点识别的准确率。实验表明，本文的拓扑地图不仅易于创建和维护，而且适用于机器人在大规模室内环境下的自主导航。     

基于情感与环境认知的移动机器人自主导航控制

将基于情感和认知的学习与决策模型引入到基于行为的移动机器人控制体系中, 设计了一种新的自主导航控制系统. 将动力学系统方法用于基本行为设计, 并利用ART2神经网络实现对连续的环境感知状态的分类, 将分类结果作为学习与决策算法中的环境认知状态. 通过在线情感和环境认知学习, 形成合理的行为协调机制. 仿真表明, 情感和环境认知能明显地改善学习和决策过程效率, 提高基于行为的移动机器人在未知环境中的自主导航能力     

Predicting the position of adjacent nodes with QoS in mobile ad hoc networks

The Mobile Ad Hoc Networks are a self-regulatory set of autonomous nodes which perform communication to all the other nodes within their communication ranges. The nodes which are not in straightforward range make use of in between nodes to perform communication with one another. In mobile ad hoc network, each and every autonomous node holds displacements and shifts based on the precise positions within the network. Hence the verification of node position is crucial in mobile ad hoc networks and it is mainly a great dispute during the existence of opponents focusing on damaging the system. The intention is to design a standard termed as Adjacent Node Location Confirmation (ANLC) for confirming the location of its transmitting adjacent nodes for interchanging the messages and confirms the location of the nodes in transmission within the network. Initially, the method focuses on finding the nodes based on which the transmission connection is set up or it is within the fixed distance. The distance is estimated based on message interchanges among the confirmer and its adjacent nodes in transmission. Soon after the estimation of distances the confirmer authenticates the location of nodes in transmission within the network based on straight balanced, traverse balanced and multi-lateration analysis. The analysis is performed based on QoS of the transmitting node choice for minimizing the delays and acquiring improved throughput. The performance of the designed scheme is estimated based on network throughput and delays.

     

Path planning in GPS-denied environments via collective intelligence of distributed sensor networks

This paper proposes a framework for reactive goal-directed navigation without global positioning facilities in unknown dynamic environments. A mobile sensor network is used for localising regions of interest for path planning of an autonomous mobile robot. The underlying theory is an extension of a generalised gossip algorithm that has been recently developed in a language-measure-theoretic setting. The algorithm has been used to propagate local decisions of target detection over a mobile sensor network and thus, it generates a belief map for the detected target over the network. In this setting, an autonomous mobile robot may communicate only with a few mobile sensing nodes in its own neighbourhood and localise itself relative to the communicating nodes with bounded uncertainties. The robot makes use of the knowledge based on the belief of the mobile sensors to generate a sequence of way-points, leading to a possible goal. The estimated way-points are used by a sampling-based motion planning algorithm to generate feasible trajectories for the robot. The proposed concept has been validated by numerical simulation on a mobile sensor network test-bed and a Dubin’s car-like robot.     

Learning action models for the improved execution of navigation plans

Most state-of-the-art navigation systems for autonomous service robots decompose navigation into global navigation planning and local reactive navigation. While the methods for navigation planning and local navigation themselves are well understood, the plan execution problem, the problem of how to generate and parameterize local navigation tasks from a given navigation plan is largely unsolved.

This paper describes how a robot can autonomously learn to execute navigation plans. We formalize the problem as a Markov Decision Process (MDP) and derive a decision theoretic action selection function from it. The action selection function employs models of the robot’s navigation actions, which are autonomously acquired from experience using neural networks or regression tree learning algorithms. We show, both in simulation and on an RWI B21 mobile robot, that the learned models together with the derived action selection function achieve competent navigation behavior.     

Efficient terrain coverage for deploying wireless sensor nodes on multi-robot system

Coverage and connectivity are the two main functionalities of wireless sensor network. Stochastic node deployment or random deployment almost always cause hole in sensing coverage and cause redundant nodes in area. In the other hand precise deployment of nodes in large area is very time consuming and even impossible in hazardous environment. One of solution for this problem is using mobile robots with concern on exploration algorithm for mobile robot. In this work an autonomous deployment method for wireless sensor nodes is proposed via multi-robot system which robots are considered as node carrier. Developing an exploration algorithm based on spanning tree is the main contribution and this exploration algorithm is performing fast localization of sensor nodes in energy efficient manner. Employing multi-robot system and path planning with spanning tree algorithm is a strategy for speeding up sensor nodes deployment. A novel improvement of this technique in deployment of nodes is having obstacle avoidance mechanism without concern on shape and size of obstacle. The results show using spanning tree exploration along with multi-robot system helps to have fast deployment behind efficiency in energy.     

基于多目标最优化方法的垂直切换判决

当多个移动节点在蜂窝网与无线局域网共存的异构无线网络环境中移动时,综合考虑移动节点的电池寿命、基站与接入点的负载以及移动节点花费3个因素,运用多目标最优化方法进行切换判决,为所有移动节点选择合适的目标网络,使整个网络资源得到合理利用。仿真结果表明,相比传统最强信号优先方法,该方法在剩余电池寿命总和与负载平衡方面具有较好的性能,在花费总和方面性能较差。     

Mission-based online generation of probabilistic monitoring models for mobile robot navigation using Petri nets

This paper presents a generic hybrid monitoring approach, which allows the detection of inconsistencies in the navigation of autonomous mobile robots using online-generated models. A mission on the context of the navigation corresponds to an autonomous navigation from a start to an end mission point. The operator defines this mission by selecting a final goal point. Based on this selection the monitoring models for the current mission must be generated online. The originalities of this work are (i) the association of classic state estimation based on a particle filter with a special class of Petri net in order to deliver an estimation of the next robot state (position) as well as the environment state (graph nodes) and to use both pieces of information to distinguish between external noise influences, internal component faults and global behaviour inconsistency (ii) the integration of the geometrical and the logical environment representation into the monitor model (iii) the online generation of the corresponding monitoring model for the present mission trajectory while the system is running. The model takes simultaneously into account the uncertainty of the robot and of the environment through a unified modelling of both. To show the feasibility of the approach we apply it to an intelligent wheelchair (IWC) as a special type of autonomous mobile robot.     

Lessons learned from the simulation experience of a three-tier multi-hop wireless Internet architecture

The mobile Internet provides mobile nodes with continuous access to the Internet resources. This type of service is guaranteed only if these mobile nodes are one-hop away from the edge of the Internet. Mobile ad hoc networks (MANET), however, enable multi-hop communication between mobile nodes. A more promising goal would be to connect MANET to the Internet, thus enabling mobile nodes to benefit from the multi-hop nature of MANET so they can to share access to the Internet resources with the stationary nodes. This paper aims to study a three-tier architecture to provide MANET nodes with multi-hop Internet connectivity, evaluate its performance through extensive experiments with respect to a set of decision parameters, and outline the lessons learned from these experiments.     

Markov Chain Analysis of Self-organizing Mobile Nodes

Self-organization of autonomous mobile nodes using bio-inspired algorithms in mobile ad hoc networks (manets) has been presented in earlier work of the authors. In this paper, the convergence speed of our force-based genetic algorithm (called fga) is provided through analysis using homogeneous Markov chains. The fga is run by each mobile node as a topology control mechanism to decide a corresponding node??s next speed and movement direction so that it guides an autonomous mobile node over an unknown geographical area to obtain a uniform node distribution while only using local information. The stochastic behavior of fga, like all ga-based approaches, makes it difficult to analyze the effects that various manet characteristics have on its convergence speed. Metrically transitive homogeneous Markov chains have been used to analyze the convergence of our fga with respect to various communication ranges of mobile nodes and also the number of nodes in various scenarios. The Dobrushin contraction coefficient of ergodicity is used for measuring convergence speed for Markov chain model of our fga. Two different testbed platforms are presented to illustrate effectiveness of our bio-inspired algorithm in terms of area coverage.