机器人运动规划中的姿态空间建模方法研究

提出机器人运动规划中的一种姿态空间建模方法,其基本思想是在工作空间中定义具有某种特征的点为基本碰撞体,可用于机械臂在复杂环境中的运动规划建模。实验表明该建模方法是有效可行的。     

基于遗传算法的机器人关节空间最优运动规划

本文整体上研究了最优轨迹规划问题，它包含两个密切相关的子问题，最优路径规划和最优轨迹规划，本文提出了一种基于遗传算法的更为一般，柔性和有效的新方法，该策略综合考虑了机器人的运动学约束，动力学约束和控制约束，最优轨迹用合适的多项式拟合，得到的控制序列适宜于高速高精度的动力学控制，本文最后以二自由度机器人为例进行了仿真实验。     

基于模糊优化的仿人机器人运动规划研究

文章主要是对仿人型两足机器人(简称仿人机器人)的运动稳定性进行分析、研究,建立了一套动力学模型,并运用模糊优化的方法分析其运动参数.实验表明:该方法在很大程度上提高了仿人两足机器人的运动稳定性.     

理性遗传算法及其在多机器人运动协调中的应用

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.     

空间机器人位姿空间分层量化建模的方法

本文首先分析了空间机器人在位姿空间的不会合可控性及其给运动规划带来的困难。在此基础上，本文提出一种适用于空间机器人的位姿空间分层量经建模方法。此方法首先对位姿空间进行了分层量化处理，然后通过定义位姿间的相邻关系而建立了位姿空间图（ＣＳＧ），并对ＣＳＧ的预点和边的碰撞情况做了规定。最后本文针对一个空间机器人模型给出一个位姿空间建模的实例和基于此建模方法的运动规划结果。     

空间机器人位姿空间分层量化建模方法

本文首先分析了空间机器人在位姿空间的不完全可控性及其给运动规划带来的困难．在此基础上，本文提出一种适用于空间机器人的位姿空间分层量化建模方法．此方法首先对位姿空间进行了分层量化处理，然后通过定义位姿间的相邻关系而建立了位姿空间图（ＣＳＧ），并对ＣＳＧ的顶点和边的碰撞情况做了规定．最后本文针对一个空间机器人模型给出一个位姿空间建模的实例和基于此建模方法的运动规划结果     

人工智能技术在机器人运动规划中的应用

机器人的运动规划问题是机器人学和人工智能中的一个重要课题。总结了国内外在机器人运动规划领域的一些典型算法,讨论了各种人工智能技术在运动规划中的应用,介绍了在空间机器人运动规划课题研究中的一些成果。     

自由飘浮空间机器人运动规划研究综述

本文介绍了自由飘浮空间机器人的运动学特性;从避奇异点规划、姿态调整规划和协同控制规划三个方面总结了自由飘浮空间机器人的运动规划研究现状,并对现有方法的局限性进行了分析;最后给出了自由飘浮空间机器人运动规划下一步需要研究的问题与解决思路。     

基于位姿空间栅格扩展及变维搜索的\=机器人运动规划新策略

本文首先对机器人运动规划算法中的栅格扩展策略及次序规划法进行了探讨,并提 出了相应的改进方法,在此基础上提出了一种新的机器人运动规划策略——基于位姿空间栅 格扩展及变维空间搜索算法,该算法可有效地在复杂环境中找出无碰撞路径．     

双机器人协调完成复杂边缘跟踪的运动规划

本文以空间复杂边缘跟踪任务为对象，系统地讨论了利用双机器人协调跟踪复杂边缘所遇到的问题，提出用双机器人协调解决此问题的基本策略与方法．结合汽车车门涂胶的实验，把一般性的理论推导深入到实际的应用中．     

Multirobot motion coordination in space and time

This paper describes a solution to the multirobot motion planning problem based on a decoupled analysis in the space domain and in the time domain. It investigates the practical use of the notion of motion plan quality and of the motion plan robustness measures for computing safe motions. The use of anytime algorithms allows one to evaluate the opportunity of looking for alternative solution paths by generating small variations of robot motions affecting both its geometrical path and its scheduled velocity. By using the concept of plan robustness, several alternative paths are generated and evaluated through various performance indices and impact factors, using heuristic rules. These indices allow one to know how much a variation affects a given plan. Finally, some recent experiments are outlined.     

基于传感器的非完整移动机器人运动规划

针对非完整移动机器人在未知室内环境中提出了一种路径规划方法, 通过利用传感器对周围环境的探测和实时处理传感器数据, 以及所设计的目标寻找函数, 可以有效地完成其运动规划. 该方法能够确保移动机器人在无障碍物区或障碍物对机器人不构成危险时加速前进, 在障碍物区能够慢速绕过, 从而使得移动机器人快速且安全地到达目标位置, 仿真的结果证明了该方法的有效性.     

霍夫空间中多足球机器人协作目标定位算法

针对嵌入式仿人足球机器人提出一种霍夫空间中的多机器人协作目标定位算法。机器人利用实验场地中的标志物采用基于三角几何定位方法进行自定位,把机器人多连杆模型进行简化,通过坐标系位姿变换把图像坐标系转换到世界坐标系中,实现机器人目标定位;在多机器人之间建立ZigBee无线传感器网络进行通信,把多个机器人定位的坐标点进行霍夫变换,在霍夫空间中进行最小二乘法线性拟合,获取最优参数,然后融合改进后的粒子滤波实现对目标小球的跟踪;最后在21自由度的仿人足球机器人上进行仿真和实验。数据结果表明,这种多机器人协作的定位算法的精度提高了约48%,在满足实时性的前提下,对目标的跟踪效果也得到了改善。     

非完整移动机器人鲁棒运动规划

非完整移动机器人利用传感器可以解决不确定性模型和未知环境中的许多问题. 利用移动机器人上配备的传感器的信息组合提出了一种在线视点寻求算法, 结合移动机器人的运动方程和传感器的量测方程采用扩展Kalman估计来对移动机器人的位置进行修正, 以降低运动的不确定性, 从而得到一种鲁棒的规划算法, 仿真的结果证明了上述方法是行之有效的.     

Optimum motion planning in joint space for robots using genetic algorithms

The optimum motion planning in joint space (OMPJS) for robots, which generally consists of two subproblems, optimum path planning and optimum trajectory planning, was considered as a whole in the paper. A new method for optimum motion planning problem based on an improved genetic algorithm is proposed, which is more general, flexible and effective. This approach incorporates kinematics constraints, dynamics constraints, and control constraints of robotic manipulator. The simulation results for a two and a three degrees of freedom robots are presented and discussed. The simulations are based on genetic algorithm class library WGAClass 1.0 developed by us with Borland C++ 3.1.     

Safe and efficient motion planning of multiple mobile robots based on artificial potential for human behavior and robot congestion

In order for robots to exist together with humans, safety for the humans has to be strictly ensured. On the other hand, safety might decrease working efficiency of robots. Namely, this is a trade-off problem between human safety and robot efficiency in a field of human–robot interaction. For this problem, we propose a novel motion planning technique of multiple mobile robots. Two artificial potentials are presented for generating repulsive force. The first potential is provided for humans. The von Mises distribution is used to consider the behavioral property of humans. The second potential is provided for the robots. The Kernel density estimation is used to consider the global robot congestion. Through simulation experiments, the effectiveness of the behavior and congestion potentials of the motion planning technique for human safety and robot efficiency is discussed. Moreover, a sensing system for humans in a real environment is developed. From experimental results, the significance of the behavior potential based on the actual humans is discussed. For the coexistence of humans and robots, it is important to evaluate a mutual influence between them. For this purpose, a virtual space is built using projection mapping. Finally, the effectiveness of the motion planning technique for the human–robot interaction is discussed from the point of view of not only robots but also humans.     

Rapidly exploring random graphs: motion planning of multiple mobile robots

Rapidly exploring random trees (RRT) and probabilistic roadmaps (PRM) are sampling-based techniques being extensively used for robot path planning. In this paper, the tree structure of the RRT is generalized to a graph structure which enables a greater exploration. Exploration takes place simultaneously from multiple points in the map, all explorations fusing at multiple points producing well-connected graph architecture. Initially, in a typical RRT manner, the search algorithm attempts to reach the goal by expansions, and thereafter furtherer areas are explored. With some additional computation cost, as compared to RRT with a single robot, the results can be significantly improved. The so-formed graph is similar to roadmap produced by PRM. However as compared to PRM, the proposed algorithm has a more judicious search strategy and is adaptable to the number of nodes as a parameter. Experimental results are shown with multiple robots planned using prioritization scheme. Results show the betterment of the proposed algorithm as compared to RRT and PRM techniques.     

空间机器人目标捕获的自适应规划

与地面固定基座机器人不同的是,空间机器人的运动学方程中含有动力学参数。在执行目标捕获任务时,目标动力学参数的不精确会给空间机器人的规划带来致命的影响。针对目标捕获后动力学参数不精确情况下的关节空间规划问题,在建立了自由飘浮空间机器人运动学模型的基础上,给出了雅可比矩阵及其动量守恒方程中的惯性参数以及惯性参数的组合参数线性化的具体形式,提出了一种关节空间的自适应规划方法。以平面二连杆空间机器人为研究对象进行仿真验证。结果表明,所提出的自适应规划方法可以有效降低惯性参数不精确给运动规划带来的影响,为空间机器人执行目标捕获等任务时提供了任务空间内精确轨迹跟踪的能力。     

A framework for multi-robot motion planning from temporal logic specifications

We propose a framework for the coordination of a network of robots with respect to formal requirement specifications expressed in temporal logics.A regular tessellation is used to partition the space of interest into a union of disjoint regular and equal cells with finite facets,and each cell can only be occupied by a robot or an obstacle.Each robot is assumed to be equipped with a finite collection of continuous-time nonlinear closed-loop dynamics to be operated in.The robot is then modeled as a hybrid automaton for capturing the finitely many modes of operation for either staying within the current cell or reaching an adjacent cell through the corresponding facet.By taking the motion capabilities into account,a bisimilar discrete abstraction of the hybrid automaton can be constructed.Having the two systems bisimilar,all properties that are expressible in temporal logics such as Linear-time Temporal Logic,Computation Tree Logic,and μ -calculus can be preserved.Motion planning can then be performed at a discrete level by considering the parallel composition of discrete abstractions of the robots with a requirement specification given in a suitable temporal logic.The bisimilarity ensures that the discrete planning solutions are executable by the robots.For demonstration purpose,a finite automaton is used as the abstraction and the requirement specification is expressed in Computation Tree Logic.The model checker Cadence SMV is used to generate coordinated verified motion planning solutions.Two autonomous aerial robots are used to demonstrate how the proposed framework may be applied to solve coordinated motion planning problems.