978 resultados para Robot motion
Resumo:
Sampling based planners have been successful in path planning of robots with many degrees of freedom, but still remains ineffective when the configuration space has a narrow passage. We present a new technique based on a random walk strategy to generate samples in narrow regions quickly, thus improving efficiency of Probabilistic Roadmap Planners. The algorithm substantially reduces instances of collision checking and thereby decreases computational time. The method is powerful even for cases where the structure of the narrow passage is not known, thus giving significant improvement over other known methods.
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Robots must plan and execute tasks in the presence of uncertainty. Uncertainty arises from sensing errors, control errors, and uncertainty in the geometry of the environment. The last, which is called model error, has received little previous attention. We present a framework for computing motion strategies that are guaranteed to succeed in the presence of all three kinds of uncertainty. The motion strategies comprise sensor-based gross motions, compliant motions, and simple pushing motions.
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In many motion-vision scenarios, a camera (mounted on a moving vehicle) takes images of an environment to find the "motion'' and shape. We introduce a direct-method called fixation for solving this motion-vision problem in its general case. Fixation uses neither feature-correspondence nor optical-flow. Instead, spatio-temporal brightness gradients are used directly. In contrast to previous direct methods, fixation does not restrict the motion or the environment. Moreover, fixation method neither requires tracked images as its input nor uses mechanical tracking for obtaining fixated images. The experimental results on real images are presented and the implementation issues and techniques are discussed.
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Manipulator motion planning is a task which relies heavily on the construction of a configuration space prior to path planning. However when fast real-time motion is needed, the full construction of the manipulator's high-dimensional configu-ration space can be too slow and expensive. Alternative planning methods, which avoid this full construction of the manipulator's configuration space are needed to solve this problem. Here, one such existing local planning method for manipulators based on configuration-sampling and subgoal-selection has been extended. Using a modified Artificial Potential Fields (APF) function, goal-configuration sampling and a novel subgoal selection method, it provides faster, more optimal paths than the previously proposed work. Simulation results show a decrease in both runtime and path lengths, along with a decrease in unexpected local minimum and crashing issues.
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This robot has low natural frequencies of vibration. Insights into the problems of designing joint and link flexibility are discussed. The robot has three flexible rotary actuators and two flexible, interchangeable links, and is controlled by three independent processors on a VMEbus. Results from experiments on the control of residual vibration for different types of robot motion are presented. Impulse prefiltering and slowly accelerating moves are compared and shown to be effective at reducing residual vibration.
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This paper describes a new system, dubbed Continuous Appearance-based Trajectory Simultaneous Localisation and Mapping (CAT-SLAM), which augments sequential appearance-based place recognition with local metric pose filtering to improve the frequency and reliability of appearance-based loop closure. As in other approaches to appearance-based mapping, loop closure is performed without calculating global feature geometry or performing 3D map construction. Loop-closure filtering uses a probabilistic distribution of possible loop closures along the robot’s previous trajectory, which is represented by a linked list of previously visited locations linked by odometric information. Sequential appearance-based place recognition and local metric pose filtering are evaluated simultaneously using a Rao–Blackwellised particle filter, which weights particles based on appearance matching over sequential frames and the similarity of robot motion along the trajectory. The particle filter explicitly models both the likelihood of revisiting previous locations and exploring new locations. A modified resampling scheme counters particle deprivation and allows loop-closure updates to be performed in constant time for a given environment. We compare the performance of CAT-SLAM with FAB-MAP (a state-of-the-art appearance-only SLAM algorithm) using multiple real-world datasets, demonstrating an increase in the number of correct loop closures detected by CAT-SLAM.
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采用基于Visual C++和OpenGL的建模和运动仿真方法,对可重构星球探测机器人系统的三维运动仿真实验平台进行了研究,建立了一个多机器人系统的仿真实验平台。开发的实验平台可用于探索和验证机器人系统的工作原理、工作空间、多机器人协调算法、重构方法、系统集成技术等。在该平台上进行了机器人的运动学仿真和协调运动研究,验证了该仿真平台的有效性和机器人系统体系结构的合理性。
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面对传统遗传算法在解决一些复杂问题时所存在的收敛慢或早熟等困难 ,基于仿人理性决策原则 ,提出一种具有更丰富进化含义的进化算法——理性遗传算法 .其通过遗传信息的反馈或理性规则的建立来指导遗传操作的进行 ,从而将种群内部知识与经验的继承和学习更有效地结合在遗传算法之中 .相对于传统遗传算法 ,较好地解决了多机器人确知环境下协调运动规划问题 .理论分析和仿真实验结果都是令人鼓舞的 .
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本文以水下机器人的遥操作作业为应用背景 ,提出并实现了虚拟现实技术和视觉感知信息辅助机器人遥操作实验系统 .该系统使用了 CAD模型和立体视觉信息完成遥操作机器人及其作业环境的几何建模和运动学建模 ,实现了虚拟作业环境的生成和实时动态图形显示 .采用了基于立体视觉的虚拟环境与真实环境的一致性校正、图形图像叠加、作业体与环境位姿关系建立、基于网络的监控通讯等关键技术 .在这个实验系统中 ,操作人员可利用所生成的虚拟环境 ,在多视点、多窗口作业状态图形和图像显示帮助下 ,实时动态地进行作业观测与机器人遥操作与运动规划 ,为先进遥操作机器人系统的实现提供了经验和关键技术 .
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月球探测对于我国有着长远的战略意义,移动机器人将在我国“二”期月球探测计划中发挥举足轻重的作用。因为月面环境恶劣、机器人自主性有限,所以基于虚拟现实的遥操作技术将在月球探测任务中发挥重要作用。它给操作者提供一种三维的、逼真的和可交互的机器人仿真平台。在此平台上,操作者可以借助科学家的智能来解决月球探测机器人自主和遥操作的结合问题,可以验证路径规划、机械臂运动规划及控制指令等。 本文分析了月球探测机器人和真实三维地形几何拓扑信息的交互过程,借助基于虚拟现实的遥操作技术,开发了基于真实地形场景的移动机器人运动仿真平台。在此平台上,运动仿真反应了机器人真实的运动状态。 首先通过对真实地形三维点云的三角剖分和纹理影射,我们得到了真实三维地形场景。然后借助OpenGL软件库和Solidworks软件我们对月球探测机器人进行了精确的几何建模。 本文在分析国内外星球探测机器人仿真系统基础上,提出了一种轮式移动机器人轮子与地形几何拓扑信息交互的方法,此方法解释了地形变化如何影响到机器人姿态的变化。通过在虚拟地形上实验和分析机器人状态数据,证明了此方法的合理性。 本文还推导了六轮移动机器人的运动学模型,确定了机器人车体位姿及其变化与轮子接地点位姿及其变化之间的关系,为机器人如何调整姿态以适应变化的三维崎岖地形提供了理论基础。并利用速度投影法,得到了轮式移动机器人运动学模型新的形式。 最后结合运动学模型和几何模型,我们在Windows平台上利用VC++OpenGL 开发了基于真实地形场景的星球探测机器仿真系统,实现了月球探测机器人的实时仿真。该系统具有较强的交互性和实时性,为星球探测机器人虚拟导航、路径验证、遥操作等提供了验证平台。
Resumo:
In this paper, a disturbance controller is designed for making robotic system behave as a decoupled linear system according to the concept of internal model. Based on the linear system, the paper presents an iterative learning control algorithm to robotic manipulators. A sufficient condition for convergence is provided. The selection of parameter values of the algorithm is simple and easy to meet the convergence condition. The simulation results demonstrate the effectiveness of the algorithm..
Resumo:
为了使机器人跟踪给定的期望轨线,提出了一种新的基于机器人运动重复性的学习控制法.在这种方法中机器人通过重复试验得到期望运动,这种控制法的优点:一是对于在期望运动附近非线性机器人动力学的近似表达式的线性时变机械系统产生期望运动的输入力矩可不由估计机器人动力学的物理参数形成;二是可以适当的选择位置、速度和加速度反馈增益矩阵,从而加快误差收敛速度;三是加入了加速度反馈,减少了速度反馈,减少了重复试验的次数.这是因为在每次试验的初始时刻不存在位置和速度误差,但存在加速度误差.另外,这种控制法的有效性通过PUMA562机器人的前三个关节的计算机仿真结果得到验证。