Development of A Behavior‐Based Cooperative Search Strategy for Distributed Autonomous Mobile Robots Using Zigbee Wireless Sensor Network

To achieve efficient and objective search tasks in an unknown environment, a cooperative search strategy for distributed autonomous mobile robots is developed using a behavior‐based control framework with individual and group behaviors. The sensing information of each mobile robot activates the individual behaviors to facilitate autonomous search tasks to avoid obstacles. An 802.15.4 ZigBee wireless sensor network then activates the group behaviors that enable cooperative search among the mobile robots. An unknown environment is dynamically divided into several sub‐areas according to the locations and sensing data of the autonomous mobile robots. The group behaviors then enable the distributed autonomous mobile robots to scatter and move in the search environment. The developed cooperative search strategy successfully reduces the search time within the test environments by 22.67% (simulation results) and 31.15% (experimental results).     

基于机器人群的主动传感器网络自组织的运动规划

主动传感器网络的自组织通常要求移动节点群(机器人群)通过障碍物环境移动到指定地点后, 重新调整并按预定布局组网. 在网络的自组织过程中要保证每个移动节点(机器人)与整个网络之间的连通性. 在对移动机器人的保持连通性进行优化的基础上, 提出了单步位置预测与群体势场相结合的分布式运动规划方法进行主动传感器网络的部署和重置, 证明了机器人运动控制的稳定性和网络的连通性保持, 进行了有和无障碍物环境下超过40个机器人的仿真, 结果表明该方法适用于大规模的主动传感器网络重置, 并对不同规模的网络具有可扩展性.     

基于迁移学习的移动机器人单帧图像坡度检测算法

针对未知环境下移动机器人平稳上坡控制对坡度感知精度的要求,本文提出了一种基于迁移学习的移动机器人单帧图像坡度检测算法.利用室内图像标准数据集训练深度卷积神经场-全连接超像素池化网络(deep convolutional neural field-fully connected superpixel pooling ne...     

基于移动通讯网络和机器人群的分布式主动传感系统实验平台

描述了一个基于移动通讯网络和机器人群的分布式主动传感系统实验平台.该平台实现了基于GPRS/CDMA和ad hoc网络的远程数据传输、具有力反馈的双向遥操作和遥控机器人群的多种控制方式.     

不确定动态环境下移动机器人的完全遍历路径规划

基于生物激励神经网络、滚动窗口和启发式搜索,提出了一种新的完全遍历路径规划方法．该方法用Grossberg的生物神经网络实现移动机器人的局部环境建模,将滚动窗口的概念引入到局部路径规划,由启发式算法决定滚动窗口内的局域路径规划目标．该方法能在不确定动态环境中有效地实现机器人自主避障的完全遍历路径规划．仿真研究证明了该方法的可用性和有效性．     

移动机器人编队自修复的切换拓扑控制

针对机器人缺失后的移动机器人编队自修复问题, 构建了结合切换拓扑和交互动力模型的移动机器人编队模型, 通过分析机器人缺失后的拓扑变化情况, 提出了网络切换拓扑控制, 该算法利用递归实现自修复, 并且是收敛的. 通过设计相应的分布式算法, 本文将拓扑控制转化为基于局部交互的递归自修复个体控制, 证明了编队自修复个体控制的稳定性. 最后针对编队任务, 通过仿真验证了切换拓扑控制的有效性, 和其他方法比较具有低恢复时间和低功率消耗的优点.     

室外未知环境下的AGV地貌主动探索感知

智能机器人对复杂地貌环境的识别一直是机器人应用领域研究的前沿问题,移动机器人在不同的地貌上采取的运动方式并非一成不变,所以选择的运动方式对于迅速准确识别所处地貌的类型至关重要。针对该问题本文提出了一种基于贝叶斯框架的主动感知探索方法,使移动机器人能够主动探索有兴趣的运动方式并且感知识别和运动之间的匹配关系,可以优化在地貌识别之中的模糊不确定性;为了进一步验证实验的可靠性,还使用了被动感知策略来比较和分析不同策略之间的差异。实验结果表明:主动感知方法能够规划出有效的地貌识别动作序列,能够引导移动机器人主动感知目标地貌,该框架对于室外未知环境下主动感知后的地貌识别效果优于被动感知。     

Motion-based identification of multiple mobile robots using trajectory analysis in a well-configured environment with distributed vision sensors

Networked mobile robots are able to determine their poses (i.e., position and orientation) with the help of a well-configured environment with distributed sensors. Before localizing the mobile robots using distributed sensors, the environment has to have information on each of the robots?? prior knowledge. Consequently, if the environment does not have information on the prior knowledge of a certain mobile robot then it will not determine its current pose. To solve this restriction, as a preprocessing step for indoor localization, we propose a motion-based identification of multiple mobile robots using trajectory analysis. The proposed system identifies the robots by establishing the relation between their identities and their positions, which are estimated from their trajectories related to each of the paths generated as designated signs. The primary feature of the proposed system is the fact that networked mobile robots are quickly and simultaneously able to determine their poses in well-configured environments. Experimental results show that our proposed system simultaneously identifies multiple mobile robots, and approximately estimates each of their poses as an initial state for autonomous localization.     

神经网络PID 控制的机器人视觉反馈跟踪

探讨针对视觉空间的非完整移动机器人的跟踪控制问题。在不校准摄像机视觉参数的情况下,利用视觉反馈得到的信息,设计出非完整移动机器人轨迹跟踪的神经网络控制器。将BP网络与PID控制相结合,避免复杂的公式推导,解决参数不校准下的控制问题,并很好的实现跟踪。仿真结果证明了文中方法的有效性。     

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。     

A parallel processing architecture for sensor-based control of intelligent mobile robots

Parallel processing plays an important role in sensor-based control of intelligent mobile robots. This paper describes the design and implementation of a parallel processing architecture used for real-time, sensor-based control of mobile robots. This architecture takes the form of a network of sensing and control nodes, based on a novel module that we call Locally Intelligent Control Agent (LICA). It is a hybrid control architecture containing low-level feedback control loops and high-level decision making components. All the sensing, planning, and control tasks for intelligent control of a mobile robot are distributed across such a network, and operate in parallel. It has been used successfully in many experiments to perform planning and navigation tasks in real-time. Such a generic architecture can be readily applied to many diverse applications.     

Line and circle formation of distributed physical mobile robots

The formation problem of distributed mobile robots was studied in the literature for idealized robots. Idealized robots are able to instantaneously move in any directions, and are equipped with perfect range sensors. In this study, we address the formation problem of distributed mobile robots that are subject to physical constraints. Mobile robots considered in this study have physical dimensions and their motions are governed by physical laws. They are equipped with sonar and infrared range sensors. The formation of lines and circles is investigated in detail. It is demonstrated that line and circle algorithms developed for idealized robots do not work well for physical robots. New line and circle algorithms, with consideration of physical robots and sensors, are presented and validated through extensive simulations. © 1997 John Wiley & Sons, Inc.     

Robot and sensor networks for first responders

The need to collect, integrate, and communicate information effectively in emergency response scenarios exceeds the state of the art in information technology. This emergency response problem provides an interesting and important test bed for studying networks of distributed mobile robots and sensors. Here, we describe the component technologies required to deploy a networked-robot system that can augment human firefighters and first responders, significantly enhancing their firefighting capabilities. In a burning building at a firefighting training facility, we deployed a network of stationary Mote sensors, mobile robots with cameras, and stationary radio tags to test their ability to guide firefighters to targets and warn them of potential dangers. Our long-term vision is a physical network that can sense, move, compute, and reason, letting network users (firefighters and first responders) Google for physical information - that is, information about the location and properties of physical objects in the real world.     

基于生物认知的移动机器人路径规划方法

针对移动机器人在非结构环境下的导航任务,受哺乳动物空间认知方式的启发,提出一种基于生物认知进行移动机器人路径规划的方法．结合认知地图特性,模拟海马体的情景记忆形成机理,构建封装了场景感知、状态神经元及位姿感知相关信息的情景认知地图,实现了机器人对环境的认知．基于情景认知地图,以最小事件距离为准则,提出事件序列规划算法用于实时导航过程．实验结果表明,该控制算法能使机器人根据不同任务选择最佳规划路径．     

A bounded controller for multirobot navigation while maintaining network connectivity in the presence of obstacles

Maintaining the connectivity of networked robots is a challenge in multirobot applications. In this paper, this challenging problem is addressed through the development of a novel controller that can guarantee that robots will approach their individual desired positions while maintaining existing network topology and avoiding obstacles. A new concept of connectivity constraint, along with a continuous modeling approach to obstacle avoidance, is utilized in building the navigation function. The designed potential field integrates the navigation requirement, connectivity constraint, and obstacle avoidance simultaneously, based on which a bounded control input is generated for multirobot control. It is shown that if the initial configurations of the robots are connected and the desired configuration is reachable, the proposed controller is capable of driving multirobots to their individual goal positions conditionally while keeping the underlying network connected. Simulations and experiments are finally performed using a group of mobile robots to demonstrate the effectiveness of the proposed controller.     

A virtual structure approach to formation control of unicycle mobile robots using mutual coupling

In this article, the formation control problem for unicycle mobile robots is studied. A distributed virtual structure control strategy with mutual coupling between the robots is proposed. The rationale behind the introduction of the coupling terms is the fact that these introduce additional robustness of the formation with respect to perturbations as compared to typical leader–follower approaches. The applicability of the proposed approach is shown in simulations and experiments with a group of wirelessly controlled mobile robots.     

Multi-robot nonlinear model predictive formation control: Moving target and target absence

This paper describes a novel approach in formation control for mobile robots in the active target tracking problem. A nonlinear model predictive formation controller (NMPFC) for target perception was implemented to converge a group of mobile robots toward a desired target. The team must also maintain a desired formation following a target while it is moving, or follow a leader in the case of target’s absence. The structure details of the controller, as well as a mathematical analysis of the formation model used, are presented. Furthermore, results of simulations and experiments with real robots are presented and discussed.     

基于移动Agent和Web的分布式网络管理系统

分析了传统基于SNMP和Client/Server的集中式管理模式的局限性.在介绍了移动Agent的概念、特点和技术优势的基础上,提出了一种不同于传统管理模式.基于移动Agent和Web的分布式网络管理系统的设计思想和体系结构,最后阐述了在Aglet平台下的具体实现.利用移动Agent和Web技术对网络进行分布式管理,能够有效地增强网络管理的灵活性和可扩展性,并极大地提高网络管理的效率.     

一种动态未知环境下的机器人路径搜索方法

提出了一种新的路径搜索算法——"触觉感知法"来实现机器人在未知静态与动态环境情况下的路径搜索。该方法不需要提供地图信息,机器人仅收集目标点的距离和方位信息以及通过自带传感器作为触觉器收集周围局部环境信息。机器人以BP神经网络作为决策器,经过训练,可以在静态和动态环境中搜索出一条光滑无碰撞且便捷并能有效避开动态障碍物的运动轨迹。对所提出的方法进行了仿真实验,仿真结果表明算法在静态和动态环境下均能有高效率的路径搜索表现。     

A geometric approach to deploying robot swarms

We discuss the fundamental problems and practical issues underlying the deployment of a swarm of autonomous mobile robots that can potentially be used to build mobile robotic sensor networks. For the purpose, a geometric approach is proposed that allows robots to configure themselves into a two-dimensional plane with uniform spatial density. Particular emphasis is paid to the hole repair capability for dynamic network reconfiguration. Specifically, each robot interacts selectively with two neighboring robots so that three robots can converge onto each vertex of the equilateral triangle configuration. Based on the local interaction, the self-configuration algorithm is presented to enable a swarm of robots to form a communication network arranged in equilateral triangular lattices by shuffling the neighbors. Convergence of the algorithms is mathematically proved using Lyapunov theory. Moreover, it is verified that the self-reparation algorithm enables robot swarms to reconfigure themselves when holes exist in the network or new robots are added to the network. Through extensive simulations, we validate the feasibility of applying the proposed algorithms to self-configuring a network of mobile robotic sensors. We describe in detail the features of the algorithm, including self-organization, self-stabilization, and robustness, with the results of the simulation.