Efficient Dynamic Constraints for Animating Articulated Figures

This paper presents an efficient dynamics-based computer animation system for simulating and controlling the motion of articulated figures. A non-trivial extension of Featherstone's O(n) recursive forward dynamics algorithm is derived which allows enforcing one or more constraints on the animated figures. We demonstrate how the constraint force evaluation algorithm we have developed makes it possible to simulate collisions between articulated figures, to compute the results of impulsive forces, to enforce joint limits, to model closed kinematic loops, and to robustly control motion at interactive rates. Particular care has been taken to make the algorithm not only fast, but also easy to implement and use. To better illustrate how the constraint force evaluation algorithm works, we provide pseudocode for its major components. Additionally, we analyze its computational complexity and finally we present examples demonstrating how our system has been used to generate interactive, physically correct complex motion with small user effort.     

基于自主行为智能体的月球车运动规划与控制

研究基于自主行为智能体的月球车运动规划与控制方法．在基于自主行为智能体的月球车系统结构基础上,首先设计了月球车运动规划与控制的一组基本行为,对其原理进行证明．通过行为状态机对行为进行选择,如果不能保障月球车安全性能,则由运动规划智能体学习其行为参数,并由神经网络记忆．将月球车运动规划与控制分解为行为设计与学习两个过程,使月球车控制系统易于加入先验知识．同时,月球车运动规划能够满足其机动性与地形传送性约束,保证工程开发的结构化与可实现性．该方法不仅具有实时性,而且对未知环境具有较强的适应能力．仿真研究与实验证明了该方法的有效性．     

基于规则的移动机器人实时运动规划

研究移动机器人在动态环境中的导航与避障问题。为提高规划的实时性,提出了基于规则的规划方法,将多移动障碍环境机器人的运动规划分解为相对简单的单移动障碍运动规划,利用最优控制来实现单障碍的最优避障,并用智能搜索方法解决了移动机器人在多移动障碍环境中的实时运动规划问题。仿真实例表明了该方法的有效性。     

基于运动控制和频域分析的移动机器人能耗最优轨迹规划

本文针对两轮自平衡可移动机器人, 提出了一种新的能耗最优运动轨迹规划方法.本文将轨迹规划与由轨迹跟踪控制器和机器人动力学方程组成的运动控制模型相结合, 基于期望轨迹与实际电机输入电压间的传递函数和能量在时域和频域上的对应关系, 通过频域分析的方法得到了具有明确机理表达的线性能耗模型, 并采用最小二乘线性回归法对模型参数进行辨识.对于能耗最优轨迹, 由全局路径规划得到的路径点作为局部轨迹规划的局部目标点, 通过一定的数学转换和参数求导, 可直接得到相邻两个局部目标点间的能耗最优运行轨迹和对应的运行时间.通过仿真实验证明了本文所提能耗模型的准确性和所得轨迹的能耗最优性.     

基于最优控制的仿人机器人行走振动抑制

针对仿人机器人行走过程中由腿部非刚性特性引起的振动,提出了一种基于最优控制的行走振动抑制方法．首先对振动进行建模,并将这一模型加入到原有机器人动力学模型中去．然后基于拓展的动力学模型,使用预观控制方法求取一条符合质心加速度约束的控制轨迹,作为求解最优控制问题的初始解．进而由此初始解出发,迭代求解此带约束的最优控制问题,利用得到的最优控制轨迹即能以前馈方式抑制行走过程中的振动．最后,使用预观控制方法和本文方法在仿人机器人“空”上进行行走对比实验．实验结果表明提出的方法显著减小了机器人行走过程中零力矩点（ZMP）的振荡和躯干的晃动．该方法对行走振动抑制的有效性得到了验证．     

多个关节机器人分布式协作运动规划

由多个关节自治机器人构成的协作系统具有高度非线性和严重耦合性,为此提出了分布式协作运动规划方法(DCMP).应用大系统理论的分解协调方法,通过预估协调变量,解耦协作运动规划问题,使其分解为各个机器人个体的独立子规划,得到能够分布式迭代计算的子系统优化模型.利用协同进化算法在全局优化和约束条件处理方面的优势,DCMP解决高维组合C空间的空间—时间优化搜索问题、静态和动态约束问题,尤其是其它方法难以克服的速度约束问题.仿真实验结果证明了本文方法的有效性.     

Skiing Simulation Based on Skill‐Guided Motion Planning

Skiing is a popular recreational sport, and competitive skiing has been events at the Winter Olympic Games. Due to its wide moving range in the outdoor environment, motion capture of skiing is hard and usually not a good solution for generating skiing animations. Physical simulation offers a more viable alternative. However, skiing simulation is challenging as skiing involves many complicated motor skills and physics, such as balance keeping, movement coordination, articulated body dynamics and ski‐snow reaction. In particular, as no reference motions — usually from MOCAP data — are readily available for guiding the high‐level motor control, we need to synthesize plausible reference motions additionally. To solve this problem, sports techniques are exploited for reference motion planning. We propose a physics‐based framework that employs kinetic analyses of skiing techniques and the ski–snow contact model to generate realistic skiing motions. By simulating the inclination, angulation and weighting/unweighting techniques, stable and plausible carving turns and bump skiing animations can be generated. We evaluate our framework by demonstrating various skiing motions with different speeds, curvature radii and bump sizes. Our results show that employing the sports techniques used by athletes can provide considerable potential to generate agile sport motions without reference motions.     

人体运动捕捉及运动控制的研究

在论述了几种方式的人体运动捕捉的基础上,介绍了光学运动捕捉的关键技术,包括摄像机标定、标记点跟踪和三维重建技术。无标记点的运动捕捉是新兴的技术,它将捕获的图像进行分割并分析,然后用多种约束算法进行三维重建。基于运动捕捉的人体运动控制相关文献很少,文中列举了现有各种应用运动捕捉数据的方式,包括基于关键帧、运动学和物理模型等的应用。论文对运动捕捉及运动控制技术进行了总结,可为此领域的研究提供有用的信息。     

Optimal Motion Planning of Robotic Manipulators Removing Mobile Objects Grasped in Motion

The following study deals with motion optimization of robot arms having to transfer mobile objects grasped when moving. This approach is aimed at performing repetitive transfer tasks at a rapid rate without interrupting the dynamics of both the manipulator and the moving object. The junction location of the robot gripper with the object, together with grasp conditions, are partly defined by a set of local constraints. Thus, optimizing the robot motion in the approach phase of the transfer task leads to the statement of an optimal junction problem between the robot and the moving object. This optimal control problem is characterized by constrained final state and unknown traveling time. In such a case, Pontryagin"s maximum principle is a powerful mathematical tool for solving this optimization problem. Three simulated results of removing a mobile object on a conveyor belt are presented; the object is grasped in motion by a planar three-link manipulator.     

虚拟人实时运动控制的研究

为在虚拟环境中再现逼真的人体运动,对虚拟人的实时运动控制进行了研究。简单阐述了对虚拟人体的几何建模和运动建模。在传统人体动画控制技术的基础上,提出了虚拟人行为的高、低两层控制策略,实现了虚拟人行为的智能化－－在低层运动控制部分,通过关键帧,正向、逆向运动学和简单动力学适当结合的多重控制技术对人体的关节直接控制,实现了周期地步行、跑步等移动动作;在高层运动控制中,研究了避免碰撞障碍物的路径规划和接近     

多点焊接机器人最优运动规划策略与算法研究

利用遗传算法寻找多点焊接机器人的最优运动规划的方法。第一步应用遗传算法对多点焊接工业机器人进行全局路径最优规划,确定机器人操作手终端遍历所有目标点的最短路径;然后在相邻目标点之间应用遗传算法进行关节空间的轨迹规划,寻找时间最短的最优轨迹。实验结果表明缩短了运行时间,提高了机器人的工作效率,可见方法的可行性和先进性。     

基于运动控制的加减速阶段减振方法研究

运动控制系统起停时的振动抑制一直都是运动控制系统设计的难点.设计了一个二阶运动控制系统,研究实际工程中引起系统起停时振动的原因,发现在实际工程中,运动控制系统并不是关于电机的单输入系统,还有摩擦或者系统连接等引起的系统振动.由拉格朗日-欧拉方程建立系统运动微分模型,设置约束条件抑制系统起停时的振动,通过约束条件来规划S型加速度各段的参数值.仿真验证表明,算法保证了运动控制系统在起停时的振动为零,从而使所设计的控制算法提高了工作效率.所设计的算法计算量少,方便应用于各种简单的控制器设备中.     

采用跟踪边界修正和进化探索实现最佳的运动计划

研究在非结构化的环境中操作的移动机器人的最佳的运动计划系统, 提出一种新的障碍表示法———交叉线, 一种跟踪边界修正方法和一种混合进化运动计划算法. 实验结果表明了跟踪边界修正方法和交叉线表示法的有效性, 并且证明了结合跟踪边界修正和进化探索能够产生最佳的或接近最佳的路径.     

混杂系统优化控制的动态规划方法研究

研究了末态自由、时间一定的混杂系统的最优控制问题. 提出了基于可达网络的混杂系统优化控制的动态规划方法. 这种方法能够给出问题的全局最优解, 并有效地降低系统的计算量.     

基于神经网络的运动控制系统算法研究

文章研究了神经网络控制算法在运动控制系统中的应用,以典型的运动控制系统为例,基于神经网络内模自适应控制方法设计速度环控制器。采用递归神经网络的ANNI用于充分逼近被控对象的动态模型,前向反馈神经网络ANNC用作控制器,在网络拓扑结构的设计及训练算法的选择上,进行探讨,提供了解决方法并获得了一些有意义的结论。实时仿真结果表明了基于神经网络的控制系统有很强的自适应能力和抗负载扰动能力,在系统的性能上优于传统的控制算法,为类似运动控制系统的设计与研究提供了新的方法。     

Motion Planning for Drift-Free Nonholonomic Systems under a Discrete Levels Control Constraint

We solve the Motion Planning Problem for nonholonomic systems without drift when their inputs are restricted to take values in a prescribed discrete levels set of finite cardinality. In particular, we propose algorithms that produce the proper sequence of input levels as well as the corresponding switching times providing exact steering when the system is nilpotent or feedback nilpotentizable. For a general drift-free system, i.e. for a drift-free system that is neither nilpotent nor feedback nilpotentizable another algorithm provides approximate steering within a permissible error. Finally, we apply the proposed algorithms in motion planning for a car-like mobile robot.     

基于多规则加权电梯群控系统仿真

论文讨论了一种基于多目标控制的电梯群控算法,基于不同的交通模式下选择相应的权重向量,根据各个目标的加权求和,选择最小权重得电梯,以实现有效的派梯。另一方面,根据当前的交通流量和群控系统中电梯的数量,动态的改变电梯的调度算法以实现资源最优利用率。并且通过在电梯群控系统虚拟环境下仿真得到了实验数据,实验结果证明该算法的可行性,能够更大程度地满足乘客的需要。     

基于RDC-PSOA的复杂自动化控制最优任务规划方法研究

在复杂的自动化控制模型中进行最优任务规划数据推荐时,容易出现只将任务分配给最先执行的处理器的情况,导致个别处理器上任务分配过多,造成整体时间跨度增加,提出一种基于推荐数据特点粒子群优化(recommended data characteristics-particle swarm optimization algorithm:RDC-PSOA)的复杂自动化控制最优任务规划方法,以一群随机粒子为初始解,依据复杂自动化控制模型中任务规划数据推荐问题的特点,在粒子群算法的基础上,重新塑造粒子描述形式,对粒子的位置与速度进行编码,将粒子群算法映射到离散空间,通过迭代获取全部可能的自动化控制任务规划方案,实现数据的有效推荐;仿真实验结果表明,所提方法不仅具有很强的收敛能力,而且数据推荐完成时间短,性能优越.     

MPC-based Motion Planning and Control Enables Smarter and Safer Autonomous Marine Vehicles: Perspectives and a Tutorial Survey

Autonomous marine vehicles (AMVs) have received considerable attention in the past few decades, mainly because they play essential roles in broad marine applications such as environmental monitoring and resource exploration. Recent advances in the field of communication technologies, perception capability, computational power and advanced optimization algorithms have stimulated new interest in the development of AMVs. In order to deploy the constrained AMVs in the complex dynamic maritime environment, it is crucial to enhance the guidance and control capabilities through effective and practical planning, and control algorithms. Model predictive control (MPC) has been exceptionally successful in different fields due to its ability to systematically handle constraints while optimizing control performance. This paper aims to provide a review of recent progress in the context of motion planning and control for AMVs from the perceptive of MPC. Finally, future research trends and directions in this substantial research area of AMVs are highlighted.      

Dynamic Motion Planning for Mobile Robots Using Potential Field Method

The potential field method is widely used for autonomous mobile robot path planning due to its elegant mathematical analysis and simplicity. However, most researches have been focused on solving the motion planning problem in a stationary environment where both targets and obstacles are stationary. This paper proposes a new potential field method for motion planning of mobile robots in a dynamic environment where the target and the obstacles are moving. Firstly, the new potential function and the corresponding virtual force are defined. Then, the problem of local minima is discussed. Finally, extensive computer simulations and hardware experiments are carried out to demonstrate the effectiveness of the dynamic motion planning schemes based on the new potential field method.