视觉方向导航新方法

针对运行方向上导航目标本身不具有明显视觉特征的情况,提出了一种新的视觉导航方法。该方法利用环境中与目标位置关系已知的显著特征点作为参考点,根据参考点的成像位置确定系统运动的姿态调整量,实现机器人运行方向的导航。为了提高算法对环境的适应能力,该算法只利用一个参考点,具有结构简单和计算复杂度小等特点。实验结果表明,该文方法能够有效地消除导航过程的方向误差,能够配合位置导航装置实现机器人的方向导航,具有较高的实用性和稳定性。     

移动机器人视觉导航控制研究

该文研究了移动机器人视觉导航的控制问题。针对导航中的图像畸变以及视野有限易造成导航线丢失等问题,提出了一种简单的单目视觉目标定位算法和一种新的控制策略。在导航时,首先利用定位算法精确地获取地面目标的深度信息,然后控制机器人沿一系列切线方向平滑接近导航线(或目标),并根据实施控制的时间间隔控制速度,以保证机器人视野中导航线(或目标)不丢失。实际的应用证明了该定位算法和策略的有效性。     

一种寻线行走机器人设计与实现

在分析机器人比赛对机器人寻线行走功能的基础上，提出了一种寻线行走机器人的设计方法，采用高性能的DSP完成核心处理功能，为满足光电检测I／O端口数目和其它辅助数字电路的需要，在核心控制板上增加了CPLD器件；控制策略采用模糊控制规则，控制器的输出驱动步进电机，进而调整机器人的行走路线，最终实现机器人寻线行走。     

室内移动机器人新型视觉导航系统设计

视觉导航作为一种重要的机器人导航方式正日益受到人们的关注,为了解决目前采用视觉导航存在的算法设计复杂、导航性能差等问题,提出一种新的室内移动机器人视觉导航系统设计方案。首先阐述了该导航新方法的设计思想,然后介绍了采用该方法的导航系统的整体结构设计。利用放置在室内固定位置的摄像机采集室内环境的视频图像,设计了实时图像分析算法实现障碍物躲避、机器人位置跟踪等功能,为机器人的行走提供导航依据,同时通过无线通信的方式发送控制指令,实现对室内移动机器人的行走过程导航控制。     

基于模糊控制的除草机器人自主导航

研究了基于机器视觉导航的田间自主移动除草机器人．采用模糊控制方法引导除草机器人沿着农作物 行自动行走．根据导航角和导航距的参数特性选择了隶属函数,建立了两种控制规则库,并探讨了两种控制效果． 试验表明,模糊控制方法能够使机器人平稳运动．在直行阶段,控制规则1 有较高的控制精度．控制规则2 能使机 器人更好地通过弯道,对42.2± 的弯道,机器人的行走准确率达到74.58%．     

基于模糊推理的移动机器人导航研究

本文采用模糊控制的方法控制移动机器人的前进方向，在模糊控制中根据障碍物的实际位置及机器人运动方向与目标点夹角的不同情况，给出了机器人的反应规则。移动机器人的运动方向的控制利用自启发式规则进行模糊推理而实现。     

农业自主行走机器人视觉导航技术研究

对农业自主行走机器人的单目视觉导航技术展开了研究,包括行走路径的提取方法和机器人的自定位方法.对复杂农田场景和道路场景进行了描述和合理的假设,通过对图像信息的处理和理解,采用改进的Hough变换的方法提取出导航路径,并根据导航路径信息对农业自主行走机器人的自定位技术进行了研究,最终求得机器人相对于导航路径的横向偏离和角度偏差.实验结果表明,该方法能够满足农业机器人自主行走的要求.     

基于DSP和模糊控制的寻线行走机器人设计与实现

针对机器人比赛和电子设计竞赛中机器人寻线行走的普遍要求,提出了一种通用的寻线行走机器人的设计方法。机器人的核心控制器包括实现控制算法的DSP和用于扩展功能实现的CPLD;对来自光电检测传感器的信号采用模糊控制规则进行综合,核心控制器根据模糊控制器输出调整机器人的行走路线,最终实现机器人寻线行走。     

基于模糊推理的移动机器人导航研究

本文采用模糊控制的方法控制移动机器人的前进方向,在模糊控制中根据障碍物的实际位置及机器人运动方向与目标点夹角的不同情况,给出了机器人的反应规则。移动机器人的运动方向的控制利用自启发式规则进行模糊推理而实现。     

移动机器人自主导航矩阵二维码辅助定位研究

移动机器人自主导航过程中,为机器人提供精确地位置信息十分重要,只有机器人有了精确地位置信息才能准确地导航到目标点。由于单纯的惯性和里程测量系统的相对定位方式都不能消除长时间的累计定位误差,因此需要一种绝对位置信息加以辅助修正累计误差。二维码是一种很好的可以存储绝对位置信息的方式,且信息获取简单易用,本文提出了一种将矩阵二维码作为绝对位置标签辅助修正里程和惯性测量系统导航过程中产生的累计误差的导航定位方法。通过实验对比验证了该方法的有效性。     

A virtual target approach for resolving the limit cycle problem in navigation of a fuzzy behaviour-based mobile robot

A virtual target approach is proposed for resolving the limit cycle problem in navigation of a behaviour-based mobile robot. Starting from the onset point of a possible limit cycle path, the real target is switched to a virtual location and the robot is navigated according to the virtual target set up temporarily and the real environment information sensed, until a switching-back condition is reached. The cause of the limit cycle is analysed and the abrupt change in target orientations at two consecutive reaction instants is then identified to be the condition for shifting the target from the real location to the virtual one. The condition for switching back to the real target is established using a specific change in the obstacle information sensed. The algorithm is described together with some particular considerations in implementation. Efficiency and effectiveness of the proposed approach are verified through simulation and experiments conducted with a Nomad 200 robot incorporating a fuzzy behaviour-based controller.     

架空输电线路巡线机器人的视觉导航

巡线机器人沿相线行走时必须探测识别和定位各种障碍物,并根据障碍类型规划越障行为。针对220 kV架空输电线路的结构特点,利用视觉传感器,设计了基于结构约束的障碍识别算法,完成了障碍识别和分类。根据障碍物的结构特点,设计了一种自适应多窗口区域立体匹配算法,实现了障碍物的双目视觉定位。模拟线路实验结果表明,算法能可靠地从复杂背景中识别并定位出防振锤、悬垂线夹和耐张线夹等障碍物,满足了巡线机器人导航要求。     

Stable navigation in formation for a multi-robot system based on a constrained virtual structure

This paper deals with the navigation in formation of a group of mobile robots. A set of virtual targets (points) forms a virtual structure of the same shape as the desired formation. Hence, to join and to remain in this formation, each robot has only to track one of these targets. In order to track the chosen target, it has to be attainable by the robot despite its kinematic constraints. This paper studies then the maximum allowed dynamic of the virtual structure according to the kinematic constraints of the robots. Both linear and angular velocities of the targets are constrained. Moreover, depending on these velocities, some relative positions (targets) in the formation become unattainable. These positions are also defined. A stable control law allows us to attain the generated set-points. Simulation and experimental results validate the proposed contributions.     

Evolutionary Design of Fuzzy Logic Based Position Controller for Mobile Robot

This paper develops a fuzzy logic based position controller whose membership functions are tuned by genetic algorithm. The main goal is to ensure successful velocity and position trajectories tracking between the mobile robot and the virtual reference cart. The proposed fuzzy controller has two inputs and two outputs. The first input represents the distance between the mobile robot and the reference cart. The second input is the angle formed by the straight line defined with the orientation of the robot, and the straight line that connects the robot with the reference cart. The outputs represent linear and angular velocity commands, respectively. The performance of the fuzzy controller is validated through comparison with previously developed mobile robot position controller based on control Lyapunov functions (CLF). Simulation results indicate good performance of position tracking while at the same time a substantial reduction of the control torques is achieved.     

基于比例导引的足球机器人截球算法研究

常用的足球机器人截球运动控制算法较少,通过类比机器人截球运动与导弹或鱼雷追踪目标的相似性,引入军事目标追踪导引算法中常用的比例导引算法,并给出了导引算法模型及其在足球机器人系统中的应用实现。仿真结果表明该算法效果明显,行之有效,并能够根据不同的应用要求灵活的调整导引系数,取得比较理想的截球控制效果。     

基于行为的轮式移动机器人导航控制

介绍了一种轮式移动机器人CASIA-I及其运动机构,针对该运动机构给出了机器人的运动方程和基于行为的导航控制算法,并根据该算法进行了软件仿真和实物实验．实验结果表明,该导航控制算法是一种有效的导航算法．     

A Smooth Path Tracking Algorithm for Wheeled Mobile Robots with Dynamic Constraints

In order to avoid wheel slippage or mechanical damage during the mobile robot navigation, it is necessary tosmoothly change driving velocity or direction of the mobile robot. This means that dynamic constraints of the mobile robotshould be considered in the design of path tracking algorithm. In the study, a path tracking problem is formulated asfollowing a virtual target vehicle which is assumed to move exactly along the path with specified velocity. The drivingvelocity control law is designed basing on bang-bang control considering the acceleration bounds of driving wheels. Thesteering control law is designed by combining the bang-bang control with an intermediate path called the landing curve whichguides the robot to smoothly land on the virtual target's tangential line. The curvature and convergence analyses providesufficient stability conditions for the proposed path tracking controller. A series of path tracking simulations and experimentsconducted for a two-wheel driven mobile robot show the validity of the proposed algorithm.     

Supervised fuzzy reinforcement learning for robot navigation

This paper addresses a new method for combination of supervised learning and reinforcement learning (RL). Applying supervised learning in robot navigation encounters serious challenges such as inconsistent and noisy data, difficulty for gathering training data, and high error in training data. RL capabilities such as training only by one evaluation scalar signal, and high degree of exploration have encouraged researchers to use RL in robot navigation problem. However, RL algorithms are time consuming as well as suffer from high failure rate in the training phase. Here, we propose Supervised Fuzzy Sarsa Learning (SFSL) as a novel idea for utilizing advantages of both supervised and reinforcement learning algorithms. A zero order Takagi–Sugeno fuzzy controller with some candidate actions for each rule is considered as the main module of robot's controller. The aim of training is to find the best action for each fuzzy rule. In the first step, a human supervisor drives an E-puck robot within the environment and the training data are gathered. In the second step as a hard tuning, the training data are used for initializing the value (worth) of each candidate action in the fuzzy rules. Afterwards, the fuzzy Sarsa learning module, as a critic-only based fuzzy reinforcement learner, fine tunes the parameters of conclusion parts of the fuzzy controller online. The proposed algorithm is used for driving E-puck robot in the environment with obstacles. The experiment results show that the proposed approach decreases the learning time and the number of failures; also it improves the quality of the robot's motion in the testing environments.