共查询到19条相似文献,搜索用时 62 毫秒
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蔡自兴、贺汉根、陈虹等教授的新著<未知环境中移动机器人导航控制理论与方法>已在科学出版社问世,成为<21世纪先进制造技术丛书>的一枝新秀,作为该丛书的主编,我深感荣幸.专著是由三位教授和研究组成员,在智能机器人领域多年来辛苦耕耘,取得丰硕成果的基础上,精心凝练写成的,高屋建瓴,叶茂根深.它的出版值得大家庆贺. 相似文献
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未知环境中移动机器人实时导航与避障的分层模糊控制 总被引:11,自引:0,他引:11
为了解决单模糊控制器的“规则库爆炸”问题,设计了一种分层的模糊控制器,用于指导移动机器人通过未知环境到达指定的目标点.控制器根据8个超声传感器的信息和目标相对于机器人的方位确定机器人的运动.首先,每个超声传感器的信息被输入到危险度模糊控制器(DFC)中,产生关于周围环境中障碍物危险度的模糊向量.这些模糊向量经过融合与归一化处理后分别输入到上层的速度模糊控制器(VFC)和角速度模糊控制器(RFC)的推理机中.VFC根据目标的距离和障碍物的危险度控制机器人的前进速度.RFC根据目标的方向和障碍物的危险度控制机器人的转向,并采用最大隶属度法的反模糊化策略解决“对称不确定”问题.仿真与实验结果证明了所设计的模糊控制器简单而有效. 相似文献
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移动机器人视觉导航控制研究 总被引:5,自引:1,他引:5
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。 相似文献
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本文分析了未知远程环境下移动机器人导航过程中进化学习的效率和知识更新
问题,提出了并行进化模型来解决此问题,并设计和论证了高效的并行进化计算机.最后通
过实验和仿真证实基于并行进化模型移动机器人在未知环境中导航是可行的和有效的. 相似文献
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基于人工神经元网络的移动机器人导航研究 总被引:4,自引:0,他引:4
本文采用人工神经元网络模型,提出了一个基于行为的移动机人导航方法,并在仿真实验系统上进行了实验研究,取得了令人满意的结果,这是一种前有前途的导航方法。 相似文献
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基于情感与环境认知的移动机器人自主导航控制 总被引:2,自引:0,他引:2
将基于情感和认知的学习与决策模型引入到基于行为的移动机器人控制体系中, 设计了一种新的自主导航控制系统. 将动力学系统方法用于基本行为设计, 并利用ART2神经网络实现对连续的环境感知状态的分类, 将分类结果作为学习与决策算法中的环境认知状态. 通过在线情感和环境认知学习, 形成合理的行为协调机制. 仿真表明, 情感和环境认知能明显地改善学习和决策过程效率, 提高基于行为的移动机器人在未知环境中的自主导航能力 相似文献
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《Advanced Robotics》2013,27(3-4):395-420
We present a method for wheeled mobile robot navigation based on the proportional navigation law. This method integrates the robot's kinematics equations and geometric rules. According to the control strategy, the robot's angular velocity is proportional to the rate of turn of the angle of the line of sight that joins the robot and the goal. We derive a relative kinematics system which models the navigation problem of the robot in polar coordinates. The kinematics model captures the robot path as a function of the control law parameters. It turns out that different paths are obtained for different control parameters. Since the control parameters are real, the number of possible paths is infinite. Results concerning the navigation using our control law are rigorously proven. An extensive simulation confirms our theoretical results. 相似文献
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研究了室内环境下移动机器人的视觉导航问题。由单目传感器获取场景图像,利用颜色信息提取路径,采用最小二乘法拟合路径参数,简化图像处理过程,提高了算法的实时性。通过消除相对参考路径的距离偏差和角度偏差来修正机器人的位姿状态,实现机器人对路径的跟踪。为消除机器视觉识别和传输的耗时,达到实时控制,采用改进的多变量广义预测控制方法预测下一时刻控制信号的变化量来修正系统滞后。仿真和实验结果证明了控制算法的可靠性。 相似文献
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《Advanced Robotics》2013,27(4):317-333
The purpose of this study is to improve the locomotion performance for autonomous mobile robots in outdoor environments. In this paper improvement of an environment model is called empirical locomotion performance leaming. A system avoids wasting time of observations and actions by analyzing data from the last run. We propose a method of empirical learning. The method is expressed by rewriting the rules on the trajectory data. Brief route information for navigating a robot is represented with motion directions at intersections and metric distances between intersections. The behavior of our robot is based on a locomotion strategy 'sign pattern-based stereotyped motion'. The behaviors are implemented on our mobile robot HARUNOBU-4 and tested at our university campus. Experimental results show a robustness of our proposed behaviors under dynamic environments with existing obstacles. Furthermore, they showed that our proposed rewriting rules improved the locomotion performance. In particular, searching time was shortened by 87% (from 453 to 61 s) and the travel distance was shortened by 10% (from 173.8 to 157.5 m). 相似文献
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Jose A. Fernandez-Leon Gerardo G. Acosta Miguel A. Mayosky 《Robotics and Autonomous Systems》2009,57(4):411-419
This paper deals with the study of scaling up behaviors in evolutive robotics (ER). Complex behaviors were obtained from simple ones. Each behavior is supported by an artificial neural network (ANN)-based controller or neurocontroller. Hence, a method for the generation of a hierarchy of neurocontrollers, resorting to the paradigm of Layered Evolution (LE), is developed and verified experimentally through computer simulations and tests in a Khepera® micro-robot. Several behavioral modules are initially evolved using specialized neurocontrollers based on different ANN paradigms. The results show that simple behaviors coordination through LE is a feasible strategy that gives rise to emergent complex behaviors. These complex behaviors can then solve real-world problems efficiently. From a pure evolutionary perspective, however, the methodology presented is too much dependent on user’s prior knowledge about the problem to solve and also that evolution take place in a rigid, prescribed framework. Mobile robot’s navigation in an unknown environment is used as a test bed for the proposed scaling strategies. 相似文献
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N. Mokhtar M. Sugisaka L. T. Lung A. Hamzah M. Mubin N. Md Shah 《Artificial Life and Robotics》2008,13(1):255-258
Positioning tracking is not a new idea as we have been seen from the ability of the GPS (Global Positioning System) to track
the position of the object, in general with acceptable accuracy but the cost of GPS installation is expensive. However, in
the case of detecting the exact position in signal-blocked closed environment (e.g. inside building, forest, mines and others);
the accuracy factor provide by GPS is low. Thus, this project presents a positioning tracking system that is able to track
the movement of an object within a small area or inside buildings. A complete set of the positioning tracking system consists
of a pair of computer mechanical mouse and a microcontroller. From the position displayed on the computer screen, the position
of the object can be located. The pair of mouse detects each movement of the object and sends the movement data to microcontroller.
Linear, angular displacement and positioning calculation are also being discussed. From the results, it’s shown that the positioning
system is applicable. However, some small errors are also occurred but in acceptable range.
This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January
31–February 2, 2008 相似文献
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Research focused on the development and experimental validation of intelligent control techniques for autonomous mobile robots able to plan and perform a variety of assigned tasks in unstructured environments is presented. In particular, an autonomous mobile robot, HERMIES-IIB intelligence experiment series, is described. It is a self-powered, wheel-driven platform containing an onboard 16-node Ncube hypercube parallel processor interfaced to effectors and sensors through a VME-based system containing a Motorola 68020 processor, a phased sonar array, dual manipulator arms, and multiple cameras. Research on navigation and learning is examined 相似文献
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Realizing steady and reliable navigation is a prerequisite for a mobile robot, but this facility is often weakened by an unavoidable slip or some irreparable drift errors of sensors in long-distance navigation. Although perceptual landmarks were solutions to such problems, it is impossible not to miss landmarks occasionally at some specific spots when the robot moves at different speeds, especially at higher speeds. If the landmarks are put at random intervals, or if the illumination conditions are not good, the landmarks will be easier to miss. In order to detect and extract artificial landmarks robustly under multiple illumination conditions, some low-level but robust image processing techniques were implemented. The moving speed and self-location were controlled by the visual servo control method. In cases where a robot suddenly misses some specific landmarks when it is moving, it will find them again in a short time based on its intelligence and the inertia of the previous search motion. These methods were verified by the reliable vision-based indoor navigation of an A-life mobile robot.This work was presented in part at the 8th International Symposium on Artificial Life and Robotics, Oita, Japan, January 24–26, 2003 相似文献
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Adaptive behavior navigation of a mobile robot 总被引:3,自引:0,他引:3
Zalama E. Gomez J. Paul M. Peran J.R. 《IEEE transactions on systems, man, and cybernetics. Part A, Systems and humans : a publication of the IEEE Systems, Man, and Cybernetics Society》2002,32(1):160-169
Describes a neural network model for the reactive behavioral navigation of a mobile robot. From the information received through the sensors the robot can elicit one of several behaviors (e.g., stop, avoid, stroll, wall following), through a competitive neural network. The robot is able to develop a control strategy depending on sensor information and learning operation. Reinforcement learning improves the navigation of the robot by adapting the eligibility of the behaviors and determining the linear and angular robot velocities 相似文献