地面移动机器人系统的研究现状与关键技术

在总结了地面移动机器人系统的概念、特点、组成结构的基础上,分析了该领域国内外的最新动态及其关键技术,为多运动方式地面移动机器人的进一步设计与开发提供了翔实的信息与参考依据。最后,提出了一种新颖的足轮混合式地面移动机器人系统,根据不同的环境变换轮式运动和足式运动两种运动方式,达到良好的运动灵活性和较高的移动速度的统一。     

AS-R移动机器人基于双目视觉的动态避障

针对移动机器人的动态避障问题,以AS-R移动机器人为平台,设计了一种基于双目视觉的动态避障方法。从双目视觉系统出发,介绍了AS-R机器人的视觉系统,研究了AS-R机器人的运动原理,探讨了图像信息处理的过程;最后设计了两种简单环境下机器人的动态避障,并验证了所提方法的有效性。     

基于多帧差分与自适应技术的运动目标检测方法研究

提出了动态环境下适用于移动机器人视觉导航的运动目标检测方法,将多帧差分与自适应背景剪除技术相结合,考虑了背景的运动变化和对移动机器人的运动进行了补偿,优化了图像采集频率和摄像机运动速度之间的参数,并选取相应的最优帧数进行差分。该方法获得了准确、完整的检测结果,提高了检测的实时性,可用于视觉导航的智能移动机器人系统。     

一种基于DSP的移动机器人控制器的开发

针对动态环境下需要对移动机器人进行实时控制的要求,提出了一种以DSP为核心处理器的两轮式移动机器人运动控制器的设计方法,克服了传统单片机控制器存在电路复杂、稳定性低的缺点,该系统充分利用了TMS320LF2407A运动控制器外设接口丰富及运算速度快的特点,设计并实现了运动控制器及驱动器电路,通过PWM控制策略来对直流电机速度控制,实现了对非完整性两轮移动机器人的快速、实时、准确控制,并通过实验验证了该设计方法的可行性和有效性.     

基于终端滑模控制的两轮移动机器人自平衡实现方法研究

针对两轮移动机器人动力学模型的多变量、强耦合、非线性等特点以及控制性能不稳定等问题,论述了两轮移动机器人的简化模型并基于拉格朗日运动方程建立了两轮移动机器人的动力学模型。在此基础上,基于滑模控制理论提出了一种两轮移动机器人自平衡实现方法,同时设计了终端滑模控制器并进行了稳定性分析。最后,根据所述自平衡实现方法进行了仿真实验,仿真结果表明:终端滑模控制器具有较好的控制效果,而且具有较快的收敛速度,可以实现两轮移动机器人的自平衡控制。     

基于ARM移动机器人视觉系统的设计

在嵌入式ARM+Linux平台开发了基于ARM移动机器人的视觉系统,使移动机器人脱离PC机平台,因此,机器人运动相对方便,运动范围也相对扩大.视觉系统的软件实现基于OpenCV,使系统开发方便,开发周期短.视觉系统可通过实时获取和处理图像来不断调整机器人运动位置,使跟踪定位精确.     

基于双DSPs架构的移动机器人运动控制系统的设计

为了提高移动机器人的运动性能,以全向轮移动平台作为机器人的运动机构,设计了一种基于双DSPs架构的移动机器人运动控制系统,系统硬件电路简洁、集成度高;轮速控制使用模糊自适应PID算法,实现了PID参数的自动整定,较好地实现了全向轮的速度控制.实验证明移动机器人运动快速灵活、可控性强.     

新型轮腿式地面移动机器人的结构设计与运动特性分析

依据平行四边形悬挂机构的运动特性设计的轮式移动机器人具有良好的越障性能,通过结合摆臂和行星轮机构而设计的新型轮腿式地面移动机器人克服了原有机器人不能跨沟的缺陷,对外界非结构化的路面环境具有良好的适应能力。详述了该新型轮腿式机器人的结构特点,对该机器人的运动特性进行了分析,并利用拉格朗日方程建立了该机器人越障状态下的动力学模型。采用一组可行的结构尺寸,利用ADAMS软件建立虚拟样机模型验证了该地面移动机器人的可行性。     

单排连续切换全向轮移动机器人的布局方式与运动的稳定性分析

研讨了一种单排的连续切换全向轮及其全方位移动机器人的全向轮布局选择与运动时的稳定性问题,提出了一种单排的连续切换全向轮的结构,并基于此结构建立了移动机器人系统的运动学模型,通过解析系统速度逆雅可比矩阵的秩,得到了系统实现全方位运动的条件,分析给出了实现全方位运动所需的三轮、四轮的布局方式。同时,根据非完整系统的劳斯方程,建立了全向轮机器人的动力学方程,计算了车体在斜坡上运动时不发生后翻和侧翻的条件,并用ADAMS软件进行仿真验证了模型。研究为此类系统的设计和控制提供了理论基础。     

具有越障功能的小型地面移动机器人

针对在危险环境下对移动机器人的运动要求,研究了机器人系统的总体设计,提出了一种具有越障功能的小型地面轮履复合式移动机器人,阐述了其机构设计度其实现。分析了谊机器人通过台阶、斜面、楼梯等障碍时的越障特性。研制完成的第一台样机具有结构简单、体积小、质量轻的特点,通过实验验证了该机器人运动灵活,具有很好的环境适应性和越障能力。     

移动机器人导航算法研究

利用非线性微分动力系统稳定性理论,用点吸引子构造移动机器人奔向目标行为和用点排斥子构造避障行为两种行为模式,将奔向目标行为和避障行为相结合,使机器人能够根据所处环境的变化,通过行为模式权值的调整实现行为间的竞争;分析了系统稳定的基本条件,建立了基于动态方法的智能机器人行为动态导航模型;运用立体双目视觉构建局部地图,通过计算机实例仿真,验证了该机器人导航模型的可行性。仿真结果表明:该模型可以满足较为复杂未知动态环境中的导航和目标跟踪要求。     

Corridor navigation of the mobile robot using image based control

In this paper, the wall following navigation algorithm of the mobile robot using a mono vision system is described. The key points of the mobile robot navigation system are effective acquisition of the environmental information and fast recognition of the robot position. Also, from this information, the mobile robot should be appropriately controlled to follow a desired path. For the recognition of the relative position and orientation of the robot to the wall, the features of the corridor structure are extracted using the mono vision system, then the relative position, the offset distance and steering angle of the robot from the wall, is derived for a simple corridor geometry. For the alleviation of the computation burden of the image processing, the Kalman filter is used to reduce search region in the image space for line detection. Next, the robot is controlled by this information to follow the desired path. The wall following control scheme by the PD control scheme is composed of two control parts, the approaching control and the orientation control, and each control is performed by steering and forward-driving motion of the robot. To verify the effectiveness of the proposed algorithm, the real time navigation experiments are performed. Through the result of the experiments, the effectiveness and flexibility of the suggested algorithm are verified in comparison with a pure encoder-guided mobile robot navigation system.     

曲率可控双轮自平衡机器人路径规划

为了提高双轮自平衡机器人运动的侧向稳定性,提出了一种适用于狭小空间内避障的,以最大曲率为约束条件的路径规划方法。首先,建立双轮自平衡机器人运动学模型,然后分析其在临界倾斜情况下曲率与速度的关系,确定出双轮自平衡机器人运动轨迹的最大曲率值。利用A~*路径搜索算法生成无碰撞的线段性轨迹,并以考虑最大曲率约束的三次B样条曲线优化运动轨迹。通过案例验证,优化后的运动轨迹曲线满足最大曲率约束,可以使双轮自平衡机器人在无倾斜的情况下,以一较快的速度匀速沿既定轨迹曲线运动且无碰撞。证实了该方法对提高双轮自平衡机器人运动的侧向稳定性具有良好的效果。     

基于相关系数的光流计算方法研究

在对移动机器人的运动与光流进行定性分析时,引入全局光流相关系数的概念,将相关系数作为光流全局分布特牲的测度因子来判别移动机器人运动情况。这对以后的移动机器人视觉导航等具有一定的现实意义。     

一种基于ARM嵌入式系统的低成本小型移动机器人平台

文章介绍一种应用常见经济型器件构建的移动机器人平台,它满足低成本、装配简单、可扩展性好等要求。我们选择了高速低功耗的ARM芯片作为处理器,为机器人设计了丰富的功能,并与上位机视觉定位和控制系统结合,使其适用于导航与定位、运动控制策略、多机器人系统体系结构与协作机制等领域研究。     

Self-learning navigation algorithm for vision-based mobile robots using machine learning algorithms

Many mobile robot navigation methods use, among others, laser scanners, ultrasonic sensors, vision cameras for detecting obstacles and following paths. However, humans use only visual (e.g. eye) information for navigation. In this paper, we propose a mobile robot control method based on machine learning algorithms which use only camera vision. To efficiently define the state of the robot from raw images, our algorithm uses image-processing and feature selection steps to choose the feature subset for a neural network and uses the output of the neural network learned through supervised learning. The output of the neural network uses the state of a reinforcement learning algorithm to learn obstacle-avoiding and path-following strategies using camera vision image. The algorithm is verified by two experiments, which are line tracking and obstacle avoidance.     

Balance control of a 12-DOF mobile manipulator based on two-wheel inverted pendulum robot

Humanoid mobile manipulator which is based on two-wheel inverted pendulum robot has been studied.Balance control is a key problem for this kind of centroid-variable robot.Due to the principle of two wheel inverted pendulum,a timely angle compensation is necessary to make the system keep balance when the centroid changes.In this paper,a method based on coordinate transformation is introduced to get the compensatory angle and a 12-DOF mobile manipulator is also used to check the method.Simulation and experimental results show the effectiveness of the method.     

Hand–eye calibration and positioning for a robot drilling system

In the aircraft manufacturing, drilling large amount of assembly holes in aircraft board is one of the key bottlenecks of production efficiency. To enhance the efficiency and quality of assembly holes' manufacturing, robot drilling system replacing manual operation becomes more and more urgent. Normally, a robot system needs accurate mathematical models of the manufactured object and the environment when it's working; as a matter of fact, because of the manufacturing error, the homogeneity between aircraft board and its mathematical model is dissatisfied. So a hand–eye vision system is introduced to realize the positioning of the end effector in order to improve the flexibility and robustness of a robot drilling system. The paper discusses the calibration and positioning of a hand–eye vision system for a robotic aircraft board drilling system. Because the drill must be vertical and keep a fixed distance to the aircraft board surface before drilling, the depth information of hand–eye relationship is neglected and by defining an intermediate scene coordinate system the hand–eye relationship between the robot coordinate system and the vision coordinate system is established. Then the position of target point can be described in the robot coordinate system by using the calibrated hand–eye relationship, and thus the navigation information for the robot drilling system can be provided. Experimental results of the calibration and positioning of the hand–eye vision of a robot drilling system is provided, and the main factors that affect the positioning error are analyzed.     

A two-layered subgoal based mobile robot navigation algorithm with vision system and IR sensors

The present work describes the real-life implementation of a mobile robot navigation scheme where vision sensing is employed as primary sensor for path planning and IR sensors are employed as secondary sensors for actual navigation of the mobile robot with obstacle avoidance capability in a static or dynamic indoor environment. This two-layer based, goal-driven architecture utilizes a wireless camera in the first layer to acquire image and perform image processing, online, to determine subgoal, employing a shortest path algorithm, online. The subgoal information is then utilized in the second layer to navigate the robot utilizing IR sensors. Once the subgoal is reached, vision based path planning and IR guided navigation is reactivated. This sequential process is continued in an iterative fashion until the robot reaches the goal. The algorithm has been effectively tested for several real-life environments created in our laboratory and the results are found to be satisfactory.