基于双向测距的协同定位系统误差分析

针对协同定位系统中影响节点定位精度受测距误差影响的问题,主要研究和分析了测距误差传播以及移动场景误差对协同定位系统的影响.采用基于GPS(Global Positioning System)和超带宽双程测距的低成本协同定位系统,分析了协同定位系统中的误差传播过程,推导了测距协方差阵和定位协方差阵.在不同场景下进行了仿真,经分析得出节点间几何关系是影响系统定位误差的最重要因素.该结论对于协同定位系统中移动节点的定位精度提升具有一定的指导价值.     

VARIABLE PATROL PLANNING OF MULTI-ROBOT SYSTEMS BY A COOPERATIVE AUCTION SYSTEM

A cooperative auction system (CAS) is proposed to solve the large-scale multi-robot patrol planning problem. Each robot picks its own patrol points via the cooperative auction system and the system continuously re-auctions, based on the team work performance. The proposed method not only works in static environments but also considers variable path planning when the number of mobile robots increases or decreases during patrol. From the results of the simulation, the proposed approach demonstrates decreased time complexity, a lower routing path cost, improved balance of workload among robots, and the potential to scale to a large number of robots and is adaptive to environmental perturbations when the number of robots changes during patrol.     

传感器网络中利用反演集合估计的机器人定位方法

针对大型协作环境中移动机器人的全局定位问题,提出根据机器人车载传感器、环境传感器以及其他机器人的实时数据估计移动机器人的位置。首先,提出的方法整合大量不同类型传感器,从最简单传感器到最复杂传感器;然后,考虑了测量值数量可变、通用测角测量、受容错约束的测量统计知识等约束条件,将非线性边界误差估计问题看作一种反演集合。最后处理特定类型的异常值和不精确环境下的模型误差。完成了误差和异常值的处理,就基本上获得了定位图,解决了移动机器人的定位问题。提出的方法利用实物实验进行验证。测试区域装备有多个传感器、固定在墙顶部的摄像机以及位于机器人上的可见标记。实验结果表明提出的方法在协作环境中具有明显优势,处理异常值更加可靠。     

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).     

基于多AUV协作的稀疏水下传感网定位技术研究

如何在稀疏部署的水下传感器网络中实现传感器节点的高效定位是一个研究热点.提出了一种基于多个移动AUV协作的水下传感器网络内节点定位机制,利用AUV的精确自导航功能实现对网内未知位置节点的定位协助.提出的协作定位算法扩展了水下传感器网络的网内节点位置迭代估计方法,将信标节点和多AUV联合作为定位参考点,然后推导了基于最小二乘法的定位估计方程.仿真结果验证了该方法可以在定位节点比例、归一化定位误差和平均置信度等几个方面提高定位性能.     

利用FastSLAM框架的多自治水下航行器同时定位与跟踪算法

协同定位是共融机器人研究领域的重要问题.协同定位方案的制定受限于机器人间信息交互的能力.针对长时间通讯中断时多自治水下航行器(AUV)协同定位精度明显下降的问题,借鉴同时定位与制图(SLAM)方法,提出了基于FastSLAM框架的同时定位与跟踪(SLAT)算法.将主AUV视为非合作目标,在从AUV上建立起一个关于主AUV的运动估计器,利用从AUV上声呐传感器实时获取的相对量测信息,在对主AUV运动状态估计的同时,完成对从AUV自定位精度的提升.仿真实验结果表明,在长时间通讯中断发生的条件约束下,相比于传统的航位推算方法,所提出的SLATF1.0和2.0算法能够有效减小定位误差,2.0算法对于探测精度变化等因素的影响具有更好适应性.     

基于Kinect的三维视觉里程计的设计

针对移动服务机器人在未知环境下三维路径估计的问题,设计了一种基于Kinect的实时估计机器人运动轨迹的方法。该方法采用Kinect获取机器人运动过程中连续帧的彩色和深度信息,首先,提取并匹配目标帧和参考帧的SURF的特征点;然后,结合深度信息利用经典P3P问题的方法及改进的随机采样一致性(RANSAC)算法计算机器人的初始6自由度(DOF)位姿;最后,通过非线性最小二乘算法最小化初始位姿内点的双向投影误差来提高位姿精度,进而得到机器人的运动轨迹。同时对比了不同特征点及描述符结合下的里程计精度。实验结果表明,所提方法能够将里程计误差降低到3.1%,且能够满足实时要求,可为机器人同时定位与地图创建提供重要的先验信息。     

非完整移动机器人目标环绕动态反馈线性化控制

本文针对多个非完整移动机器人对静止或运动目标的环绕追踪问题进行研究.每个机器人仅通过自身和其相邻的机器人的位置与方向信息以及所追踪的目标的位置信息来协调其运动.首先,提出了一种基于动态反馈线性化方法的分布式控制策略,并引入一个控制机器人之间相对角间距的非线性函数,控制机器人间的相对角间距.使多个机器人能够以期望的与目标之间的相对距离、环绕速度和机器人之间的相对角间距对目标进行追踪.然后,利用Lyapunov工具对控制算法进行了渐近稳定性和收敛性分析.最后构建了多移动机器人实验平台,进行了数值仿真和实验验证,仿真和实验的运行结果表明了所提出算法的有效性.     

A Case Study of the Collision-Avoidance Problem Based on Bernstein–Bézier Path Tracking for Multiple Robots with Known Constraints

In this paper a case study of a new, cooperative, collision-avoidance method for multiple, nonholonomic robots based on Bernstein–Bézier curves is given. In the presented examples the velocities and accelerations of the mobile robots are constrained and the start and the goal velocity are defined for each robot. This means that the proposed method can be used as a subroutine in a huge path-planning problem in real time, in a way to split the whole path into smaller partial paths. The reference path of each robot, from the start pose to the goal pose, is obtained by minimizing the penalty function, which takes into account the sum of all the path lengths subjected to the distances between the robots, which should be bigger than the minimum distance defined as the safety distance, and subjected to the velocities and accelerations which should be lower than the maximum allowed for each robot. When the reference paths are defined the model-predictive trajectory tracking is used to define the control. The prediction model derived from the linearized tracking-error dynamics is used to predict future system behavior. The control law is derived from a quadratic cost function consisting of the system tracking error and the control effort. The proposed method was tested with a simulation and with a real-time experiment in which four robots were used.     

基于协助校正方法的多机器人主动同时定位与建图

协作策略是多机器人主动同时定位与建图(SLAM)的关键。文中提出一种多机器人相互校正的协作策略, 称为协助校正。 该方法通过优化机器人对陆标的观测来提高定位与建图的精度, 共包括弱协助校正和强协助校正两种模式。 前者是一种间接的协助模式, 可应用于所有机器人自身定位均不准确的情形。 后者是一种直接的协助模式, 由自身定位精度较高的机器人主动校正其它机器人及相应陆标。 文中将这两种协助校正模式利用状态机统一到多机器人主动SLAM应用中。在仿真实验中将协助校正与其它多机器人主动SLAM方法进行对比以验证其精度优势, 并与单机器人主动SLAM对比以验证其导航代价极低的优势。最后在两台Poineer3-DX移动机器人上进行真实环境实验,实验结果证实协助校正方法可在实际应用中有效提高多机器人主动SLAM的探索效率和精度。     

On a bio-inspired hybrid pheromone signalling for efficient map exploration of multiple mobile service robots

This paper presents a novel bio-inspired hybrid communication framework that incorporates the repelling behaviour of anti-aphrodisiac pheromones and attractive behaviour of pheromones for efficient map exploration of multiple mobile service robots. The proposed communication framework presents a scheme for robots to efficiently serve large areas of map, while cooperating with each other through proper pheromone deposition. This eliminates the need of explicitly programming each service robot to serve particular areas of the map. The paths taken by robots are represented as nodes across which pheromones are deposited. This reduces the search space for tracking pheromones and reduces data size to be communicated between robots. A novel pheromone deposition model is presented which takes into account the uncertainty in the robot’s position. This eliminates robots to deposit pheromones at wrong places when localization fails. The framework also integrates the pheromone signalling mechanism in landmark-based Extended Kalman Filter (EKF) localization and allows the robots to capture areas or sub-areas of the map, to improve the localization. A scheme to resolve conflicts through local communication is presented. We discuss, through experimental and simulation results, two cases of floor cleaning task, and surveillance task, performed by multiple robots. Results show that the proposed scheme enables multiple service robots to perform cooperative tasks intelligently without any explicit programming.     

A Localization Method Based on Map-Matching and Particle Swarm Optimization

This paper presents a novel localization method for small mobile robots. The proposed technique is especially designed for the Robot@Factory, a new robotic competition which is started in Lisbon in 2011. The real-time localization technique resorts to low-cost infra-red sensors, a map-matching method and an Extended Kalman Filter (EKF) to create a pose tracking system that performs well. The sensor information is continuously updated in time and space according to the expected motion of the robot. Then, the information is incorporated into the map-matching optimization in order to increase the amount of sensor information that is available at each moment. In addition, the Particle Swarm Optimization (PSO) relocates the robot when the map-matching error is high, meaning that the map-matching is unreliable and the robot gets lost. The experiments presented in this paper prove the ability and accuracy of the presented technique to locate small mobile robots for this competition. Extensive results show that the proposed method presents an interesting localization capability for robots equipped with a limited amount of sensors, but also less reliable sensors.     

霍夫空间中多足球机器人协作目标定位算法

针对嵌入式仿人足球机器人提出一种霍夫空间中的多机器人协作目标定位算法。机器人利用实验场地中的标志物采用基于三角几何定位方法进行自定位,把机器人多连杆模型进行简化,通过坐标系位姿变换把图像坐标系转换到世界坐标系中,实现机器人目标定位;在多机器人之间建立ZigBee无线传感器网络进行通信,把多个机器人定位的坐标点进行霍夫变换,在霍夫空间中进行最小二乘法线性拟合,获取最优参数,然后融合改进后的粒子滤波实现对目标小球的跟踪;最后在21自由度的仿人足球机器人上进行仿真和实验。数据结果表明,这种多机器人协作的定位算法的精度提高了约48%,在满足实时性的前提下,对目标的跟踪效果也得到了改善。     

Cooperative localization method for multi-robot based on PF-EKF

A method of cooperative localization for multi-robot in an unknown environment is described. They share information and perform localization by using relative observations and necessary communication. At initial time, robots do not know their positions. Once the robot that can obtain the absolute position information has its position, other robots use particle filter to fuse relative observations and maintain a set of samples respectively representing their positions. When the particles are close to s Gsussian distribution after a number of steps, we switch to an EKF to track the pose of the robots. Simulation results and real experiment show that PF-EKF method combines the robustness of PF and the efficiency of EKF. Robots can share the absolute position information and effectively localize themselves in an unknown environment.     

起伏地形环境中多移动机器人协作运输策略

对多机器人协作运输时环境地形起伏不平,运输过程中机器人和物体发生滑动的问题,提出了起伏地形环境中的协作运输策略.将观测者—推动者模式的多机器人协作推箱子任务转换为多机器人编队任务,通过基于几何规划的虚拟目标序列跟踪方法实现被推动物体脱离运动方向时运输编队的重新形成.协作运输策略中的观测者机器人采用平缓地形优先(navigate plain first to destination,NPFD)导航策略指导编队运动.     

Cooperative localization and control of multiple heterogeneous robots using a string formation

This paper is on cooperative localization and control of multiple heterogeneous robots utilizing a string formation. This formation is preferred, since robots can move along a narrow passage utilizing this formation. Dead reckoning localization based on inertial measurement units leads to accumulated localization error. To avoid the error accumulation in dead reckoning localization, this paper introduces the last-move strategy for multiple heterogeneous robots. In the last-move strategy, a single robot is selected for maneuvering, and it turns on its bearing-range sensors for a short amount of time, in order to locate itself. While the selected robot moves, all other robots stop moving and perform as static landmarks for the moving robot. A robot may not maintain its desired course, in the case where environmental disturbance is severe. We thus develop a control strategy for avoiding obstacles while estimating the disturbance direction at a robot's location. To the best of our knowledge, this paper is novel in localization and control of a team of heterogeneous robots, considering the case where environmental disturbance is severe. The proposed localization process is energy-efficient, thus is suitable for practical applications. The performance of the proposed schemes is demonstrated utilizing MATLAB simulations.     

Cooperative adaptive consensus tracking for multiple nonholonomic mobile robots

This paper addresses the cooperative adaptive consensus tracking for a group of multiple nonholonomic mobile robots, where the nonholonomic robot model is assumed to be a canonical vehicle having two actuated wheels and one passive wheel. By integrating a kinematic controller and a torque controller for the nonholonomic robotic system, a cooperative adaptive consensus tracking strategy is developed for the uncertain dynamic models using Lyapunov-like analysis in combination with backstepping approach and sliding mode technique. A key feature of the developed adaptive consensus tracking algorithm is the introduction of a directed network topology into the control constraints based on algebraic graph theory to characterise the communication interaction among robots, which plays an important role in realising the cooperative consensus tracking with respect to a specific common reference trajectory. Furthermore, a novel framework is proposed for developing a unified methodology for the convergence analysis of the closed-loop control systems, which can fully ensure the desired adaptive consensus tracking for multiple nonholonomic mobile robots. Subsequently, illustrative examples and numerical simulations are provided to demonstrate and visualise the theoretical results.     

Autonomic configuration and recovery in a mobile agent‐based distributed event monitoring system

In this paper we present a framework for building policy‐based autonomic distributed agent systems. The autonomic mechanisms of configuration and recovery are supported through a distributed event processing model and a set of policy enforcement mechanisms embedded in an agent framework. Policies are event‐driven rules derived from the system's functional and non‐functional requirements. Agents in the network monitor the system state for policy violation conditions, generate appropriate events, and communicate them to other agents for cooperative filtering, aggregation, and handling. A set of agents perform policy enforcement actions whenever events signifying any policy violation conditions occur. Policies are defined using a specification framework based on XML. The policy enforcement agents interpret the policies given in XML. We illustrate the utility of this framework in the context of an agent‐based distributed network monitoring application. We also present an experimental evaluation of our approach. Copyright © 2006 John Wiley & Sons, Ltd.     

Finite-time tracking control for multiple non-holonomic mobile robots based on visual servoing

This paper investigates finite-time tracking control problem of multiple non-holonomic mobile robots via visual servoing. It is assumed that the pinhole camera is fixed to the ceiling, and camera parameters are unknown. The desired reference trajectory is represented by a virtual leader whose states are available to only a subset of the followers, and the followers have only interaction. First, the camera-objective visual kinematic model is introduced by utilising the pinhole camera model for each mobile robot. Second, a unified tracking error system between camera-objective visual servoing model and desired reference trajectory is introduced. Third, based on the neighbour rule and by using finite-time control method, continuous distributed cooperative finite-time tracking control laws are designed for each mobile robot with unknown camera parameters, where the communication topology among the multiple mobile robots is assumed to be a directed graph. Rigorous proof shows that the group of mobile robots converges to the desired reference trajectory in finite time. Simulation example illustrates the effectiveness of our method.     

基于视觉SLAM–伺服框架的移动机器人指令滤波反步控制

针对移动机器人位姿镇定问题, 本文提出基于视觉同时定位与建图(simultaneous localization and mapping, SLAM)–伺服框架的指令滤波反步控制策略. 具体而言, 通过加速度层控制器设计进而积分得到的光滑速度信号, 减小SLAM视觉模块的预测位姿误差; 继而应用指令滤波器简化控制器设计的复杂求导运算, 减轻计算负担; 此外, SLAM模块利用运动信息与视觉信息的融合解决未知尺度问题, 降低未知深度造成的控制器设计复杂度. 通过李雅普诺夫理论可以证明闭环系统的稳定性. 仿真和实验结果最终验证了本文算法的有效性.