基于声纳环传感器的机器人避障研究

任何一种移动机器人要实现未知环境自主导航都必须有效而可靠地感知环境信息,而超声波传感器在检测障碍物距离信息方面应用十分广泛。介绍了旅行家II号声纳环传感系统的设计与实现原理,并对声纳的精度进行了测试。在此基础上,提出了移动机器人一种简单避障策略,并运用2种基本避障实验:静态避障和动态避障,验证了该避障策略的正确性和有效性。     

基于传感器信息的水下机器人动态避障研究

提出一种新的基于传感器信息的自治式水下机器人(AUV)动态避障方法。介绍了传感器的工作原理。通过栅格法把传感器采集的AUV运行环境障碍信息进行合理描述,并预测动态障碍物的速度,保证AUV能够根据传感器信息躲避障碍物,达到航行要求。最后,通过仿真实验对机器人自主避障能力进行了验证。     

Self-localization and obstacle avoidance for a mobile robot

This article presents a fast self-localization method based on ZigBee wireless sensor network and laser sensor, an obstacle avoidance algorithm based on ultrasonic sensors for a mobile robot. The positioning system and positioning theory of ZigBee which can obtain a rough global localization of the mobile robot are introduced. To realize accurate local positioning, a laser sensor is used to extract the features from environment, then the environmental features and global reference map can be matched. From the matched environmental features, the position and orientation of the mobile robot can be obtained. To enable the mobile robot to avoid obstacle in real-time, a heuristic fuzzy neural network is developed by using heuristic fuzzy rules and the Kohonen clustering network. The experiment results show the effectiveness of the proposed method.     

智能移动机器人的超声避障研究

智能移动机器人是机器人研究领域的重要方向,是当前机器人领域中最活跃的研究主题之一.在分析了智能移动机器人避障常用传感器的基础上,提出了基于多超声传感器的移动机器人的超声避障系统.介绍了超声避障系统的模糊控制规则和非模糊化,并给出了实验结果.实验结果表明,模糊控制机理和策略易于接受和理解,便于应用开发,模糊避障算法对环境有很大的适应性,机器人在不同的环境条件下实现了避障.     

Path planning with obstacle avoidance based on visibility binary tree algorithm

In this paper, a novel method for robot navigation in dynamic environments, referred to as visibility binary tree algorithm, is introduced. To plan the path of the robot, the algorithm relies on the construction of the set of all complete paths between robot and target taking into account inner and outer visible tangents between robot and circular obstacles. The paths are then used to create a visibility binary tree on top of which an algorithm for shortest path is run. The proposed algorithm is implemented on two simulation scenarios, one of them involving global knowledge of the environment, and the other based on local knowledge of the environment. The performance are compared with three different algorithms for path planning.     

红外传感器在教育机器人避障电路设计中的应用

红外传感器在家用电器遥控和机器人避障方面有着广泛的应用,笔者根据红外传感器的这一特性设计出一款教育机器人避障电路,介绍了红外传感器在机器人避障电路中的应用,给出红外传感器与单片机系统的硬件接口电路和避障软件的编程思路,并通过测试,得出了影响红外传感器避障的相关因素,并提出了改进的方法和措施.     

An obstacle avoidance algorithm for robot manipulators based on decision-making force

Obstacle avoidance is a significant skill not only for mobile robots but also for robot manipulators working in unstructured environments. Various algorithms have been proposed to solve off-line planning and on-line adaption problems. However, it is still not able to ensure safety and flexibility in complex scenarios. In this paper, a novel obstacle avoidance algorithm is proposed to improve the robustness and flexibility. The method contains three components: A closed-loop control system is used to filter the preplanned trajectory and ensure the smoothness and stability of the robot motion; the dynamic repulsion field is adopted to fulfill the robot with primitive obstacle avoidance capability; to mimic human’s complex obstacle avoidance behavior and instant decision-making mechanism, a parametrized decision-making force is introduced to optimize all the feasible motions. The algorithms were implemented in planar and spatial robot manipulators. The comparative results show the robot can not only track the task trajectory smoothly but also avoid obstacles in different configurations.     

Fuzzy neural networks for obstacle pattern recognition and collision avoidance of fish robots

The problems of detection and pattern recognition of obstacles are the most important concerns for fish robots’ path planning to make natural and smooth movements as well as to avoid collision. We can get better control results of fish robot trajectories if we obtain more information in detail about obstacle shapes. The method employing only simple distance measuring IR sensors without cameras and image processing is proposed. The capability of a fish robot to recognize the features of an obstacle to avoid collision is improved using neuro-fuzzy inferences. Approaching angles of the fish robot to an obstacle as well as the evident features such as obstacles’ sizes and shape angles are obtained through neural network training algorithms based on the scanned data. Experimental results show the successful path control of the fish robot without hitting on obstacles.     

基于超声测距的工业机器人避障控制系统设计

工业机器人对于自身与障碍物之间的距离测量不准确,无法根据二者距离判断行驶路径,导致避障效果较差。为此基于超声测距技术设计了一种新的工业机器人避障控制系统,从硬件和软件两方面对避障效果进行优化设计。将具有脉冲信号的超声换能器安装在单片机的操作模块中,增加MOS功率器件作为超声换能器的驱动元件,应用EA1、EA2和EA3三种型号的误差放大器,将回波信号稳定放大到电路中,利用运算放大器和RC网络电路实现滤波放大,利用信号检测电路实现检测。利用超声波传感器精准测量出自己与障碍物之间的距离,通过建立模糊数据库、定位漫反射信息、提取不同方向的测距信息实现工业机器人避障控制。实验结果表明,设计的基于超声测距的工业机器人避障控制系统能够准确测量出与障碍物之间的距离,避障控制准确率平均值达97.4%,能够实现工业机器人避障防碰撞操作。     

Fuzzy logic system based prediction effort: A case study on the effects of tire parameters on contact area and contact pressure

Various methodologies of artificial intelligence have been recently used for estimating performance parameters of soil working machines and off-road vehicles. Due to nonlinear and stochastic features of soil–wheel interactions, application of knowledge-based Mamdani max–min fuzzy expert system for estimation of contact area and contact pressure is described in this paper. Fuzzy logic model was constructed by use of the experience of contact area and contact pressure utilizing data obtained from series of experimentations in soil bin facility and a single-wheel tester. Two paramount tire parameters: wheel load and tire inflation pressure are the input variables for our model, each has five membership functions. As a fundamental aspect of the fuzzy logic based prediction systems, a set of fuzzy if-then rules were used in accordance with fuzzy logic principles. 25 linguistic if-then rules were included to develop a complicated highly intelligent predicting model based on Centroid method at defuzzification stage. The model performance was assessed on the basis of several statistical quality criteria. Mean relative error lower than 10%, satisfactory scattering around unity-slope line (T), and high coefficient of determination, R², were obtained by the fuzzy logic model proposed in this study.     

Proximal object and hazard detection for autonomous underwater vehicle with optical fibre sensors

This paper describes short range and tactile optical fibre sensors for marine applications. The sensors are designed for obstacle avoidance on unmanned underwater vehicles (UUVs) operating in confined spaces, but have other possible applications. The fibre sensors augment the sensory abilities derived from ultrasonic and other sensors employed for marine proximity measurement. Of particular interest is proximity detection in the “near” (less than 1 m) and tactile areas. The paper describes the basic principle of operation and alternative sensor configurations. Results are given based on laboratory tests and deployment on a mini autonomous submersible in a test pool.     

A fibre-optic oxygen sensor based on contact charge-transfer absorption

A fibre-optic oxygen (O₂) sensor monitoring at a wavelength of 400 nm has been successfully developed for the determination of gaseous O₂. Its working principle is based on the contact charge-transfer absorption of N,N-dimethyl-p-toluidine and O₂. The response to changes in O₂ concentrations is reversible and in good agreement with the Beer-Lambert law. The response and recovery times are 12 and 26 min, respectively. The sensor can detect a wide range of O₂ concentrations, ranging from 4.3 to 100% O₂. The precision is 1.45% (n=5) in a gas mixture of 95% O₂ in N₂ and the limit of detection is 4.3% O₂ (3σ_b). The sensor is stable with a 0.53% change in sensitivity per hour. There is a 0.25% °C⁻¹ decrease of the sensitivity of the sensor to O₂ in the range 20–34°C. Water vapour and nitrogen dioxide interfere slightly, whereas hydrogen sulphide and hydrogen chloride have moderate interference on the sensor. However, chlorine and sulphur dioxide seriously interfere with the sensor.     

Guided Autowave Pulse Coupled Neural Network (GAPCNN) based real time path planning and an obstacle avoidance scheme for mobile robots

Real time path planning for mobile robots requires fast convergence to optimal paths. Most rapid collision free path planning algorithms do not guarantee the optimality of the path. In this paper we present a Guided Autowave Pulse Coupled Neural Network (GAPCNN) approach for mobile robot path planning. The proposed model is a novel approach that improves upon the recently presented Modified PCNN (MPCNN) by introducing directional autowave control and accelerated firing of neurons based on a dynamic thresholding technique. Simulation studies and experimental results in both static as well as dynamic environments confirm GAPCNN to be a robust and time efficient path planning scheme for finding optimal paths.     

基于滚动速度障碍法的无人机山地航测避障路径规划研究

无人机在进行山地航测时,经常遭遇鸟类等动态障碍,若不能及时规避掉障碍,极容易发生坠机事故。为此,研究一种基于滚动速度障碍法的无人机山地航测避障路径规划方法。基于山地环境模型,结合飞行路径长度、路径平滑度建立一个综合目标函数并利用改进布谷鸟搜索算法求解,得到无人机山地航测的初始路径。对图像进行预处理后,识别无人机初始路径飞行过程中遇到的障碍物,并通过超声波测量无人机与障碍物之间的距离,以此建立速度障碍模型,实现速度障碍碰撞分析,通过滚动窗口的方式确定无人机与障碍物是否存在飞行冲突。基于滚动速度障碍避障方法实现滚动角度避障和速度避障,获取最终的优化路径,完成基于滚动速度障碍法的无人机山地航测避障路径规划。测试结果表明:航测避障路径长度为571.45m,平滑度为165.52,规划的方案更具合理性。     

基于Flex＋Spring小区信息系统的设计与实现

Flex是一个高效并且免费的开源框架,可用于构建具有表现力的Web应用程序,这些应用程序能通过AdobeFlash Player执行,运行时跨浏览器、桌面和操作系统,实现一致的部署.Spring是一个轻量级IoC（控制反转）和AOP（面向切面编程）的容器框架,它使J2EE开发变得更加容易,能提供一种统一并且高效的方式来构造整个应用.基于Flex＋Spring设计并实现了小区信息系统,该系统能方便地从大量数据中提取出小区信息,前台利用Flex来实现,后台基于Spring框架用Java编写.利用庖丁分词器对收集来的地址信息进行分词并存入关键词库,最后利用关键词创建小区信息.     

基于Sagnac传感器的周界安防入侵信号识别

针对光纤安防周界系统采集信号的非线性、非平稳性、间歇性等特点,给出了一种入侵振动信号分帧方法.采用嵌入维数计算方法,在保留时间序列信号的内在动力学特性条件下以确定最小分帧长度.利用短时能量和短时平均过零率双条件判断识别振动信号,再进行小波分解,比较各层小波系数能量分布特征,通过二级判定识别降低入侵振动信号误识率.实验结果表明:该方法可行有效,能准确判定识别入侵振动信号.     

基于遗传算法优化的足球机器人模糊避障研究

在Robocup小型组(F-180)足球机器人比赛中,随着比赛对机器人运动能力和系统实时性要求的越来越高,基于本地传感器和处理器的主动避障策略日益受到人们的重视.模糊控制方法不依赖于精确的环境信息,并且运算量小,应用于自主避障很有前途,但传统的模糊控制方法难于手工创建控制规则,并且缺乏学习能力.研究了采用遗传算法对模糊路径规划器的模糊规则进行在线自动提取和优化的方法,并提出了一种用于评价规则有效性的适应度函数.仿真结果表明了算法有较好的适用性.     

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。     

基于模糊Elman网络算法的移动机器人路径规划分析

为了调正移动机器人避障线路,建立了基于模糊Elman网络算法的移动机器人路径规划模型,并应用进行Matlab仿真分析。利用现有障碍物的距离信息来实现机器人步长的实施可控制与调节,防止移动机器人在做出准确避障行为之后因为没有设定合适的步长而导致撞上障碍物,以0.5作为机器人的最初运动步长。仿真结果表明,采用模糊Elman网络可以获得比其它两种方法更优的路径规划效果,同时对障碍物进行高效避让,由此实现最优的路径规划。采用模糊Elman网络来构建得到的路径规划算法能够满足规划任务的要求,同时还能够根据机器人处于不同工作空间中的情况进行灵活调整。