多智能体系统一致性的递阶结构设计

针对多智能体系统的一致性协作,研究了以提高群体一致性收敛速度为目标的多层递阶拓扑结构设计问题.首先讨论了满足物理拓扑约束并具有更优性能的递阶拓扑结构存在的可能性.在此基础上,根据一致性问题的收敛时间性能指标,将递阶结构设计转化为一个与多层递阶系统中所有子图拉普拉斯矩阵特征值相关的优化问题.通过将连通图转换为若干点导出子图和它们之间的关联图,提出一个该优化问题的双层拓扑结构设计算法,并基于该算法发展了满足物理拓扑结构约束的多层递阶结构设计算法.仿真算例表明由此设计的递阶协作过程可有效加快多智能体群体一致性的收敛速度.     

基于多模式交互的多移动机器人分布式合作系统

本文研究合作型多移动机器人系统的分布式控制方法.为了保证多机器人间合作的实 时性和高效率,采用了一种分组策略,并提出了将局部感知和组内通信相集成的多模式机器人 间交互方法.考虑到任务的复杂性和真实环境的非结构化特点,构建了将递阶规划技术与基于 行为的反应式控制相结合的递阶混合式协调结构,并采用有限状态机模型实现了规划层与行为 层的协调机制.合作垃圾清运的实验结果证明了上述方法的有效性.     

基于相对方位的多机器人合作定位算法

研究了在未知环境中,利用扩展卡尔曼滤波方法融合内部传感器信息与机器人之间的相对方位观测量,同时定位队列中每个机器人的问题.通过机器人队列共享相对方位观测量,融合不同平台感知的信息,可有效地提高整个队列的定位精度.分析了该方法与机器人分布和运动的关系及存在的缺陷,针对这一问题,提出了改进措施,从而使该方法的可靠性和实用性得到增强.仿真实验验证了改进方法的有效性.     

基于ROS与Contiki的物联网环境下数据采集机器人设计

提出了一种基于ROS与OpenWrt、Contiki的新型物联网系统方案ROS-IOT.分为两个部分：物联网系统的搭建与此系统下数据采集机器人的设计.感知层传感节点采用Contiki协议栈实现传感节点的组网与数据传递;接入网关采用运行Openwrt操作系统的无线路由器,网关接入模块实现协议动态转换,设置转换地址池、数据汇聚、处理,并基于rosserial_embeddedLinux上递至ROS网络等功能,实现各层数据流通;应用层基于websocket技术设计了与ROS网络数据交互的web服务,可实现与感知层、机器人的双向交互.机器人采用运行ROS环境的树莓派作为主控设备,电机驱动板采用stm32单片机.机器人的软件设计采用基于ROS Topic与ROS_bridge的通讯机制,使得机器人更加容易地融入物联网系统,并且在此基础上拓展更多服务.     

基于热释电红外传感器的多节点定位系统研究与设计

针对热释电红外传感器(PIR)在人体探测领域中越来越广泛的应用,研究设计了一种基于PIR的检测定位系统,可实时完成对人员目标入侵探测区域时的检测与定位,并预推出人员目标的行进轨迹;该系统由多个PIR感知节点组成,每个感知节点通过传感在动、静两种状态下对探测区域进行信息采集;最终融合多节点与不同状态下传感器采集的数据,算出各个传感器的探测角度值,以交叉定位的算法,得到目标的定位坐标;经实验证明,该系统运行稳定,检测灵敏,定位效果很好,拓宽了热释电传感器在定位定向方面的使用范围。     

基于PIR Sensor的单目标跟踪系统的设计与实现

针对基于热释电红外传感器的人体跟踪系统在实际环境中,由于受环境噪声和硬件参数影响而误差较高的问题,本文提出了一种基于热释电红外传感器PIR Sensor(Pyroelectric Infrared Sensor)的高精度人体目标跟踪方案。该方案首先对检测区域内运动人体热辐射的红外信号特征进行了提取,然后利用PIR Sensor定位节点自身几何参数和探测数据,得到初步定位结果。最后通过Kalman滤波算法对初步定位结果进行滤波处理并更新目标的状态信息,实现对检测区域内人体目标的定位与跟踪。实验表明,该系统的跟踪误差与同类跟踪系统相比降低了71.96%,证明了该系统具有较高的跟踪精度。     

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.     

红外导航室内移动机器人的系统设计

移动机器人检测到电源电压较低时,给定位目标--充电装置发送无线电,充电装置收到无线电就发射红外线,对机器人进行导航.机器人不断采集红外信号,并用软件模拟脉宽调制的方法对驱动机器人双轮的电机进行实时控制,从而实现机器人对红外的跟踪.机器人检测到红外线信号较弱或者没有检测到红外线时,利用超声波、红外、红外光电等多种传感技术和经验丰富的专家系统进行避障、路径规划,无需图像处理,无需精确的环境模型,实现机器人以尽可能短的路径离开局部环境,快速跟踪红外线,最后准确定位于充电装置进行充电.     

基于地图的移动机器人自定位与导航系统

针对地图已知情况下的移动机器人大范围导航问题，研制了一个由地图编辑器模块、地图匹配与定位模块以及多层递阶规划模块三部分组成的移动机器人导航系统．地图编辑器负责导航地图的编辑；地图匹配与定位模块利用里程计和激光雷达数据实现基于地图匹配的自定位；多层递阶规划模块将基于拓扑地图的全局规划、基于栅格地图的局部规划和底层的行为控制功能有机结合．通过室内定位和大范围导航实验评估了本系统的有效性和准确性．     

激光与单目视觉融合的移动机器人运动目标跟踪

针对室内环境下的移动机器人运动目标跟踪问题,提出一种基于激光与单目视觉传感信息融合的机器人定位和目标运动估计方法.首先,利用激光传感信息实现对目标的检测,并完成机器人定位与环境建图;然后,设计一种基于单目视觉传感器的目标位置估计算法,获得目标的距离和角度信息;为了实现两类传感信息的有效融合,将激光与单目视觉进行联合标定,得到二者的相对位姿关系,基于此,将激光与单目视觉提取的目标距离和角度通过具有最优重要性函数和权重的粒子滤波器进行融合,实现对目标运动状态的准确估计.实验结果表明该方法具有良好的跟踪性能.     

Tracking multiple moving targets with swarms of mobile robots

This paper presents a distributed approach to enable mobile robot swarms to track multiple targets moving unpredictably. The proposed approach consists of two constituent algorithms: local interaction and target tracking. When the robots are faster than the targets, Lyapunov theory can be applied to show that the robots converge asymptotically to each vertex of the desired equilateral triangular configurations while tracking the targets. Toward practical implementation of the algorithms, it is important to realize the observation capability of individual robots in an inexpensive and efficient way. A new proximity sensor that we call dual rotating infrared (DRIr) sensor is developed to meet these requirements. Both our simulation and experimental results employing the proposed algorithms and DRIr sensors confirm that the proposed distributed multi-target tracking method for a swarm of robots is effective and easy to implement.     

Fuzzy target tracking control of autonomous mobile robots by using infrared sensors

The theme of this paper is to design a real-time fuzzy target tracking control scheme for autonomous mobile robots by using infrared sensors. At first two mobile robots are setup in the target tracking problem, where one is the target mobile robot with infrared transmitters and the other one is the tracker mobile robot with infrared receivers and reflective sensors. The former is designed to drive in a specific trajectory. The latter is designed to track the target mobile robot. Then we address the design of the fuzzy target tracking control unit, which consists of a behavior network and a gate network. The behavior network possesses the fuzzy wall following control (FWFC) mode, fuzzy target tracking control (FTTC) mode, and two fixed control modes to deal with different situations in real applications. Both the FWFC and FTTC are realized by the fuzzy sliding-mode control scheme. A gate network is used to address the fusion of measurements of two infrared sensors and is developed to recognize which situation is belonged to and which action should be executed. Moreover, the target tracking control with obstacle avoidance is also investigated in this paper. Both computer simulations and real-time implementation experiments of autonomous target tracking control demonstrate the effectiveness and feasibility of the proposed control schemes.     

Soft robot review

Soft robots are often inspired from biological systems which consist of soft materials or are actuated by electrically activated materials. There are several advantages of soft robots compared to the conventional robots; safe human-machine interaction, adaptability to wearable devices, simple gripping system, and so on. Due to the unique features and advantages, soft robots have a considerable range of applications. This article reviews state-of-the-art researches on soft robots and application areas. Actuation systems for soft robots can be categorized and analyzed into three types: variable length tendon, fluidic actuation, and electro-active polymer (EAP). The deformable property of soft robots restricts the use of many conventional rigid sensors such as encoders, strain gauges, or inertial measurement units. Thus, contactless approaches for sensing and/or sensors with low modulus are preferable for soft robots. Sensors include low modulus (< 1 MPa) elastomers with liquid-phase material filled channels and are appropriate for proprioception which is determined by the degree of curvature. In control perspective, novel control idea should be developed because the conventional control techniques may be inadequate to handle soft robots. Several innovative techniques and diverse materials & fabrication methods are described in this review article. In addition, a wide range of soft robots are characterized and analyzed based on the following sub-categories; actuation, sensing, structure, control and electronics, materials, fabrication and system, and applications.     

3-D relative positioning sensor for indoor flying robots

Swarms of indoor flying robots are promising for many applications, including searching tasks in collapsing buildings, or mobile surveillance and monitoring tasks in complex man-made structures. For tasks that employ several flying robots, spatial-coordination between robots is essential for achieving collective operation. However, there is a lack of on-board sensors capable of sensing the highly-dynamic 3-D trajectories required for spatial-coordination of small indoor flying robots. Existing sensing methods typically utilise complex SLAM based approaches, or absolute positioning obtained from off-board tracking sensors, which is not practical for real-world operation. This paper presents an adaptable, embedded infrared based 3-D relative positioning sensor that also operates as a proximity sensor, which is designed to enable inter-robot spatial-coordination and goal-directed flight. This practical approach is robust to varying indoor environmental illumination conditions and is computationally simple.     

基于输出反馈线性化的多移动机器人目标包围控制

针对受非完整约束的多移动机器人系统的移动目标包围控制问题, 提出一种基于输出反馈线性化的局部协同控制方法. 利用机器人与邻居节点和目标的相对距离信息、角度信息以及机器人自身的方位角信息设计协同控制器. 该方法无需事先指定包围编队形状, 可实现对移动目标的速度估计, 且保证机器人之间的障碍规避. 严格的理论分析证明了移动目标指数收敛到多移动机器人构成的凸包内部. 最后, 仿真结果验证了所提控制方法的有效性.     

Fall detection using single-tree complex wavelet transform

The goal of Ambient Assisted Living (AAL) research is to improve the quality of life of the elderly and handicapped people and help them maintain an independent lifestyle with the use of sensors, signal processing and telecommunications infrastructure. Unusual human activity detection such as fall detection has important applications. In this paper, a fall detection algorithm for a low cost AAL system using vibration and passive infrared (PIR) sensors is proposed. The single-tree complex wavelet transform (ST-C$\mathbb{C}$WT) is used for feature extraction from vibration sensor signal. The proposed feature extraction scheme is compared to discrete Fourier transform and mel-frequency cepstrum coefficients based feature extraction methods. Vibration signal features are classified into “fall” and “ordinary activity” classes using Euclidean distance, Mahalanobis distance, and support vector machine (SVM) classifiers, and they are compared to each other. The PIR sensor is used for the detection of a moving person in a region of interest. The proposed system works in real-time on a standard personal computer.     

Multi-robot multiple hypothesis tracking for pedestrian tracking

In this paper the problem of tracking walking people with multiple moving robots is tackled. For this purpose we present an adaptation to the Multiple Hypothesis Tracking method, which unlike classic MHT, allows for one-to-many associations between targets and measurements in each hypothesis production cycle and is thus capable of operating in a scenario involving multiple sensors. Derivation of hypotheses probabilities accounts for the continuously changing overlapping areas in fields of view of the robots sensors and for detection uncertainty. In the context of three experiments involving people walking among moving robots, the successful integration of our tracking algorithm to a real-world setup is assessed.     

采用磁传感器的餐厅服务机器人导引控制方法

针对餐厅环境复杂、定位精度低等问题,提出了一种以磁传感器为核心的餐厅服务机器人导引控制方法。该方法通过安装在机器人底盘的磁传感器感知铺设在地面下的磁条,使得机器人能够平滑、准确地沿着磁条运动,在运动的过程中,结合地标模块感知到的地标信息,获取精准的位置信息和工作状态。最后将该方法应用于一种实际餐厅服务机器人,实现了机器人在餐厅内的精准定位与导航。实验结果表明,本文所提方法性能可靠、成本较低、实用性较强,具有较广阔的应用前景。     

Planning exploration strategies for simultaneous localization and mapping

In this paper, we present techniques that allow one or multiple mobile robots to efficiently explore and model their environment. While much existing research in the area of Simultaneous Localization and Mapping (SLAM) focuses on issues related to uncertainty in sensor data, our work focuses on the problem of planning optimal exploration strategies. We develop a utility function that measures the quality of proposed sensing locations, give a randomized algorithm for selecting an optimal next sensing location, and provide methods for extracting features from sensor data and merging these into an incrementally constructed map.We also provide an efficient algorithm driven by our utility function. This algorithm is able to explore several steps ahead without incurring too high a computational cost. We have compared that exploration strategy with a totally greedy algorithm that optimizes our utility function with a one-step-look ahead.The planning algorithms which have been developed operate using simple but flexible models of the robot sensors and actuator abilities. Techniques that allow implementation of these sensor models on top of the capabilities of actual sensors have been provided.All of the proposed algorithms have been implemented either on real robots (for the case of individual robots) or in simulation (for the case of multiple robots), and experimental results are given.