基于模糊神经网络算法的智能轮椅沿墙走行为研究

为了解决移动机器人在户外自主导航移动过程中的局部路径规划问题,提出了一种更为实用的模糊神经网络算法来进行局部路径规划。利用多个声纳和一个摄像头来采集外部环境信息,使智能轮椅在移动过程中可以得到较全面的外部环境信息,使用模糊神经网络算法来对得到的环境信息进行融合,应用的神经网络模型为Takagi-Sugeno(T-S)型,通过融合的结果来控制轮椅的沿墙走行为。通过计算机仿真和实验,验证了该方法的可行性和有效性,轮椅沿墙行走的路径得到了优化。     

模糊神经网络信息融合方法在机器人避障中的应用

基于Takagi—Sugeno(T—S)模型的模糊神经网络不但具有模糊逻辑和神经网络两者的优点，又具有很好的学习能力。将基于T—S模型的模糊神经网络的信息融合算法应用在移动机器人的避障运动中，采用了多个超声测距传感器探测障碍物的距离和方向，经过模糊神经网络信息融合后，实现了机器人对障碍物和环境类型的识别以及无冲突的运动。实验表明：此方法能够使机器人安全避障。     

基于声纳的移动机器人沿墙导航控制

针对移动机器人提出了一种沿墙导航控制算法．算法首先对室内环境中墙的形状进行分类，并针对每一类型的墙设计相应的控制策略．然后在移动机器人运动过程中，基于有限状态机实现移动机器人沿墙状态的转移，从而使移动机器人采用不同的控制策略控制其运动．文中利用Pioneer 2DX移动机器人对此算法进行了实验研究，取得了理想的效果．     

结合历史运动状态的机器人高效沿墙算法研究

针对目前室内移动机器人沿墙走算法过于复杂、路径易重复、不能完全遍历、效率低等问题, 采用室内未知环境下结合历史状态的机器人沿墙高效遍历研究来解决这些问题. 该算法由移动机器人的上一个周期历史环境运动状态(分8类)、当前环境运动状态(分8类)和旋向信息(分2类)建立运动规则库, 沿墙行走时移动机器人时时采集这三类信息(上一个周期历史环境运动状态、当前环境运动状态和旋向信息)决定移动机器人当前的运动方向, 如此循环直到完成指定的沿墙任务. 最后对该算法进行了仿真与实际实验, 实验结果证明该算法可以在不同的、复杂的环境中高效、快速地完成沿墙走的任务, 并且对室内未知环境有很好的适应性.     

室内机器人及其路径跟踪网络设计

近年来,室内移动机器人的研究和设计成为关注的焦点。我们采用单片机作为机器人的核心控制器,利用超声波传感器、碰撞传感器、步进电机及其控制芯片Ta8435联合制作开发了机器人实验平台。最后介绍了模糊控制、模糊神经网络,并利用模糊控制和模糊神经网络技术对室内机器人导航中的模糊控制避障和模糊神经网络路径跟踪作了MATLAB仿真研究,达到了预期的目的。     

基于行为模糊控制的机器人绕墙走研究

本文建立了一种基于行为的模糊逻辑控制系统,将模糊逻辑推理运用到移动机器人基于行为控制的结构中,利用超声波的信息并结合移动机器人的外部环境,使机器人能够完成绕墙走运动,并把机器人在运动过程中遇到的一般障碍物当作墙.可同时实现简单地绕障碍物运动.将本文提出的控制策略通过Mobitsim软件仿真证明其可行性.     

移动机器人的智能导航研究

研究移动机器人优化导航问题,由于系统在动态未知、复杂环境下,研究自主移动机器人导航问题,首先将行为优先级控制与模糊逻辑控制相结合提出了四种基本的行为控制方案:目标查找、避障碍物、目标跟踪与解锁,并采用模糊控制器来实现.然后针对’U’型和’V’型障碍物运行解锁问题,提出了行走路径记忆方法,通过构建虚拟墙来进免机器人再次走...     

两级传感器信息融合的移动机器人避障研究

针对移动机器人的避障问题,以AS-R移动机器人为研究平台,提出了一种将神经网络和模糊神经网络相结合的两级融合方法。采用BP神经网络对多超声波传感器信息进行融合,以减少传感器信息的不确定,提高对障碍物识别的准确率;采用模糊神经网络实现移动机器人的避障决策控制,使之更适合系统的避障要求。该方法使移动机器人在避障中具有较好的灵活性和鲁棒性。机器人避障实验验证了所提方法的有效性。     

基于复合控制算法的移动机器人沿墙导航研究

室内复杂环境下,由于超声波传感器测量精度不高、数量有限,导致移动机器人沿墙导航效果不佳,现有的控制算法实现较为复杂。为此,提出一种基于复合控制算法的沿墙导航策略,通过PID控制算法和Bang-Bang控制算法切换,控制移动机器人进行沿墙导航并最终实现室内环境的边缘检测。实际运行实验验证了该方法的可行性和鲁棒性。     

基于BP神经网络的移动机器人避障设计及仿真

为了解决移动机器人在复杂环境中如何高效精确地躲避障碍物的问题,提出了一种基于BP神经网络的避障方法。建立了机器人的避障运动模型并设计了神经网络避障控制系统;分析了机器人在运动过程中与障碍物的位置关系,使用超声波传感器采集距离信息,进行BP神经网络输入、输出训练并采用Matlab工具进行仿真试验。结果表明,该方法可以高效精确地实现移动机器人的自主避障,运行相对稳定、轨迹连续平滑,达到了较为理想的避障效果。验证了方法的可行性和有效性,为移动机器人自主避障提供了一种新的控制方法。     

垂直壁面行走机器人系统研制

本文对能在高层全封闭玻璃结构外墙上行走的垂直壁面行走机器人系统的总体组成，基本功能，实施方案和技术指标等作一介绍，并对系统中的关键技术进行了分析。     

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.     

An autonomous fuzzy mobile robot

Autonomous mobile robots should have the capability of recognizing their environments and manoeuvring through those environments on the basis of their own judgement. Fuzzy control is suitable for autonomous mobile robot control where the amount of information to be handled is limited as much as possible and the processing is simple. Autonomous mobile control of a robot is derived from two kinds of controls: for obstacle avoidance and for guidance following an appropriate path to a destination point. Fuzzy control of a robot for obstacle avoidance based on finding permissible passageways using the edges between the floor and the wall or obstacles obtained by processing the image from a CCD camera in front of the robot is developed. Furthermore, guidance control of the robot over paths that are specified in terms of maps may be developed by a process that treats a wrong path as a virtual obstacle on the screen, and the robot advances in the designated direction when it reaches intersections. An autonomous fuzzy robot based on the above method is fabricated as a trial and its usefulness is demonstrated.     

仿盲人摸巷机理的机器人导航方法分析

根据“盲人摸巷”及昆虫“用须探物”的启发,结合物体接触后能产生力的特性,提出基于接触交互信息的机器人导航方法,移动机器人与环境的接触力感觉来自移动机器人的触须,该触须可以是2个多自由度机械臂,其末端装有多维力传感器,或由弹性材料特制而成,达到完成探测、自我定位及局部路径规划任务的目标,是应用图像、光、电磁、声等原理的现有导航方法的很好补充。     

Mechanism Design and Mechanical Analysis of Multi-Suction Sliding Cleaning Robot Used in Glass Curtain Wall

In order to meet the needs of high-altitude glass curtain wall cleaning, a multi-suction sliding cleaning robot was designed. The sliding robot sucker, cleaning system, obstacle avoidance and rotation ability, walking circuit and mobile working principle of the cleaning robot were designed. This involved the analysis of the robot’s anti-rollover mechanics during adsorption, of robotic winds when working at height, and of anti-sliding mechanics during robot movement, in order to explore feasible ways to improve the robot’s adsorption performance. The relationship between the effective diameter D of the suction cup, the vacuum degree △ P, and the gravity G should be determined by the anti-slipping analysis. In order to ensure the safe and reliable adsorption force and the flexibility of this robot when moving on a wall, the aforementioned analyses were conducted to improve the motion performance of wall-climbing robots, which provides a good theoretical basis for design optimization and motion control of cleaning robots. The curtain wall cleaning robot has stable walking ability and can clean the wall surface effectively; therefore, it has a certain practical value.     

轮式机器人滑模轨迹跟踪控制器设计

轮式机器人是一个典型的非完整性系统。由于非线性和非完整特性,很难为移动机器人系统的轨迹跟踪建立一个合适的模型。介绍了一种轮式机器人滑模轨迹跟踪控制方法。滑模控制是一个鲁棒的控制方法,能渐近的按一条所期望的轨迹稳定移动机器人。以之为基础,描述了轮式机器人的动力学模型并在二维坐标下建立了运动学方程,根据运动学方程设计滑模控制器,该控制器使得机器人的位置误差收敛到零。     

Acquiring Mobile Robot Behaviors by Learning Trajectory Velocities

The development of robots that learn from experience is a relentless challenge confronting artificial intelligence today. This paper describes a robot learning method which enables a mobile robot to simultaneously acquire the ability to avoid objects, follow walls, seek goals and control its velocity as a result of interacting with the environment without human assistance. The robot acquires these behaviors by learning how fast it should move along predefined trajectories with respect to the current state of the input vector. This enables the robot to perform object avoidance, wall following and goal seeking behaviors by choosing to follow fast trajectories near: the forward direction, the closest object or the goal location respectively. Learning trajectory velocities can be done relatively quickly because the required knowledge can be obtained from the robot's interactions with the environment without incurring the credit assignment problem. We provide experimental results to verify our robot learning method by using a mobile robot to simultaneously acquire all three behaviors.     

Adaptive behavior navigation of a mobile robot

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