一个虚拟人手臂操控的运动规划框架

基于双向扩展的启发式快速扩展随机树(RRT)算法,提出了一种虚拟人手臂操控的运动规划框架。该框架根据是否抓握操控对象,将虚拟人的手臂操控划分为接触和搬运两个阶段,分别采用手臂的前向和逆向运动学两种策略进行规划,保证规划结果快速、可靠。通过实验验证了该方法的有效性。     

虚拟人手臂避障抓取运动规划

针对强干涉环境下虚拟人手臂无碰撞抓取物体动作规划问题,提出一种逆向运动学与运动规划相结合的求解方法.首先在现有FABRIK逆向运动学算法的基础上提出避障FABRIK算法,用于实现手臂无碰撞抓取姿态设计,作为最终抓取姿态;然后采用Bi-RRT算法,综合考虑7维角度域约束和对非光滑路径的简化问题对手臂进行无碰撞运动规划,产生从给定初始姿态到最终抓取状态之间的中间姿态.仿真实验结果表明,该方法速度快,产生的路径能有效地避开障碍;可用于强干涉环境下的虚拟人动作设计.     

基于启发式Bi-RRT算法的虚拟手臂运动规划

为提高虚拟人手臂在复杂多障碍物空间进行维修时的运动路径规划效率,提出一种基于启发式Bi-RRT的路径规划算法。在运用Bi-RRT算法的基础上,结合启发式搜索思想对腕关节进行路径规划,通过比较多个随机搜索点与目标点的距离,保留距离目标点最近的随机搜索点,使随机树有方向地进行扩展,减少无效的搜索。仿真实验结果表明,启发式Bi-RRT算法较传统的RRT算法和Bi-RRT算法有更好的搜索效果,搜索效率更高,对于7自由度虚拟手臂的运动路径规划也有较好的效果。     

具有避障能力的虚拟人姿态优化算法

为了解决复杂环境下的虚拟人运动控制问题,提出一种基于人工势场的具有避障能力的虚拟人姿态优化算法.首先在虚拟环境中根据目标和障碍物建立人工势场,并对虚拟人运动链末端进行无碰撞路径规划;其次将规划后末端每个中间位姿的逆运动学计算问题表示为带位姿和无碰撞约束的舒适度函数优化问题,并进行求解;最后得到虚拟人运动过程中一系列连续的无碰撞姿态,实现自行避障的虚拟人运动控制.通过实例测试验证了文中算法的有效性,该算法可应用于复杂环境下的虚拟人动画和人机工程仿真.     

一种虚拟人导航运动的路径规划算法

提出了一种虚拟人在复杂的3D障碍环境中根据导航目标快速进行最优路径规划的算法,该算法以使用栅格法表示虚拟环境为基础。首先,将虚拟环境中虚拟人高度范围内所有障碍物的几何形状映射到一个离散的2D位图,并对障碍物进行“膨胀”;然后,使用提出的算法规划出一条从初始位置到目标位置的最优路径,引导虚拟人在虚拟环境中进行导航运动。该方法可以在较少的内存代价和计算代价的情况下,快速规划出从虚拟人目前位置到目标位置的最优路径,算法的可行性和有效性经过实验验证。     

船舶狭小空间虚拟人维修姿态建模技术

针对现有虚拟人仿真技术在船舶狭小空间维修作业中存在的效率低下、需要较多人工干预、仿真成本高等问题,提出一种虚拟人姿态混合建模仿真技术。根据狭小空间中人体维修作业的特点,将虚拟人姿态建模分为虚拟人躯干及下肢姿态建模与虚拟人手臂姿态建模两部分。首先,提出一种基于姿态库的狭小空间姿态自动匹配算法,以确定虚拟人在狭小空间中的操作位置与姿态;在此基础上,建立多目标优化模型对手臂姿态进行求解,并实现维修仿真姿态的生成。以某型船舶机舱罐体阀门维修为例的实验结果表明,所提方法可以实现虚拟人姿态的自动定位与生成,且可有效提高维修仿真效率。     

情感决策的智能家居虚拟人路径规划

智能家居环境中的虚拟人,目前在智能性和行为规划的研究中还存在许多不足,针对这2方面问题,提出了认知过滤层的概念,并建立了虚拟人行为模型.在此基础上,对模型中的认知行为部分进行了深入研究,并利用现有情感模型将情感智能与虚拟人行为规划相结合.由于路径规划是一种基本且重要的行为,针对智能家居环境中多环形障碍物的特点,对路径规划算法进行了改进.通过三维仿真对上述模型和算法加以实现.仿真结果表明,虚拟人的行为具有一定的智能性,情感的加入也使得虚拟人更加人性化.     

一种采用迭代优化的移动抓取规划方法

提出了一种无需对目标物体进行预建模的迭代优化移动抓取规划方法.该方法通过点云相机在线对目标物体进行立体模型测量和建模,通过深度卷积神经网络对目标点云生成的多个候选抓取位置的抓取成功率进行评价.然后,对机器人底盘和手爪的位置和姿态进行迭代优化,直到抓取目标物体时机器人达到一个最优的位形.再用A*算法规划一条从机器人当前位置到目标位置的运动路径.最后,在路径的基础上,用一种启发式随机路径逼近算法规划手臂的运动,实现边走边抓的效果.本文的深度学习抓取成功率评估算法在康奈尔数据集上取得了83.3%的精确度.所提运动规划算法能得到更平滑、更短且更有利于后续运动的路径.     

基于骨骼的三维虚拟人运动合成方法研究

针对虚拟人运动合成中建立的人体模型存在复杂化、合成的虚拟人运动序列逼真度差的问题,提出了一种基于骨骼的虚拟人运动合成方法。在分析人体结构的基础上,通过三维图形软件获取人体骨骼数据,构建虚拟人体的骨骼模型。另外,将关键帧四元数球面插值算法与时间和空间变形方法相结合,生成多样化的虚拟人运动序列。实验结果验证了该方法的有效性。     

虚拟人运动中受扰的平衡保持算法

为了更加真实且实时地模拟运动中虚拟人恢复平衡的反应动作,提出一种针对受到外界作用力扰动的虚拟人平衡保持算法.首先通过虚拟人质心位移和速度来判断平衡性;然后借助生物力学的研究成果设计了具有人体特性的虚拟人运动受扰后的平衡保持方法,并用动力学进行模拟,驱动虚拟人完成平衡恢复.实验结果表明:该算法计算效率高,符合人体的生物力学特性,并且具有良好的交互性与较好的视觉效果,适用于虚拟人动画合成.     

面向人臂三角形动作基元的拟人臂运动学

对拟人臂的运动规划问题进行了研究.通过引入动作基元的概念,将动作基元空间作为连接任务空间与关节空间的桥梁,构建了一个"关节空间-动作基元空间-任务空间"的三级运动规划框架.这种规划方式既保证了对拟人臂运动过程的控制,又简化了复杂操作任务的运动规划.在抽象的动作基元基础之上提出了一个具体的人臂三角形模型,用以描述拟人臂的运动状态.通过引入拟人臂工作平面的概念,利用坐标变换与几何分析相结合的方法详细推导并建立了人臂三角形空间与任务空间和关节空间之间的正逆运动学,为基于人臂三角形的动作基元设计奠定了基础.最后通过仿真算例验证了运动学算法的有效性与可行性.     

Human Interaction with Motion Planning Algorithm

This paper presents an interactive motion planning system to compute free collision motion in a numerical model. The system is based on interaction between a user and a motion planning algorithm. On one hand the user moves the object with an interactive device and on the other hand a motion planning algorithm searches a solution in the configuration space. The interaction aims at improving the guidance of an operator during a robot motion task in a virtual environment with the help of an automatic path planning algorithm. Existing works use a two-step decomposition which limits the interaction between the user and the ongoing process. We propose a modification of a classic motion planning method, the Rapidly-exploring Random Tree to build an Interactive-RRT. This method is based on exchanging pseudo-forces between the algorithm and the user, and on data gathering (labels) from the virtual scene. Examples are shown to illustrate the Interactive motion planning system with different interactive devices (space mouse and haptic arm). We analyze the influence of the user’s dexterity to find a solution depending on various parameters of the algorithm and we show how we can adapt these parameters to a user.     

基于三维环境模型的启发式路径规划算法

针对多自由度机器人手臂在未知环境中实时避障的问题,提出了一种基于环境信息的连杆机器人实时路径规划方法。采用笛卡尔空间内的障碍物检测信息建立了障碍物的空间模型,并依据该模型设计一种基于启发式规则的机器人路径规划算法。该算法不断猜测和修正路径,通过模糊推理得到下一位姿点,通过曲线拟合得到到达该位姿点的路径。在Matlab下利用机器人工具箱建立了PUMA560型机器人的运动学模型,并在运动空间设置障碍物,对该算法进行仿真分析,分析结果说明所提出的路径规划算法可以在较短时间内完成避障运动,具有较好的实时性,同时运动关节的角度变化曲线比较平滑,运动中冲击力较小,这些特点使其便于在实际工程中使用。     

Position control of two-arm manipulators for coordinated point-to-point motion

A trajectory planning and motion control algorithm is; presented for the point-to-point (PTP) motion of two-arm manipulators cooperating on a task. The proposed method considers the multi-arm manipulator as a system when formulating its kinematic model and obtains a global solution to the system, as opposed to individual arm solutions. For PTP motion control between two arm configurations, a simple trajectory is first assumed by defining joint velocity profiles and maximum allowable task space errors between the two end effectors of the manipulator. The task space errors during the motion are then continuously monitored to take corrective action when necessary to prevent those errors from exceeding the given tolerance limits. The main objective of this method is to reduce the number of inverse kinematics solutions during the real-time control of the two-arm system. The algorithm is illustrated by a numerical example for an eight degree-of-freedom kinematically redundant planar two-arm system.     

Coverage trajectory planning for a bush trimming robot arm

A novel motion planning algorithm for robotic bush trimming is presented. The algorithm is based on an optimal route search over a graph. Differently from other works in robotic surface coverage, it entails both accuracy in the surface sweeping task and smoothness in the motion of the robot arm. The proposed method requires the selection of a custom objective function in the joint space for optimal node traversal scheduling, as well as a kinematically constrained time interpolation. The algorithm was tested in simulation using a model of the Jaco arm and three target bush shapes. Analysis of the simulated motions showed how, differently from classical coverage techniques, the proposed algorithm is able to ensure high tool positioning accuracy while avoiding excessive arm motion jerkiness. It was reported that forbidding manipulation posture changes during the cutting phase of the motion is a key element for task accuracy, leading to a decrease of the tool positioning error up to 90%. Furthermore, the algorithm was validated in a real‐world trimming scenario with boxwood bushes. A target of 20 mm accuracy was proposed for a trimming result to be considered successful. Results showed that on average 82% of the bush surface was affected by trimming, and 51% of the trimmed surface was cut within the desired level of accuracy. Despite the fact that the trimming accuracy turned out to be lower than the stated requirements, it was found out this was mainly a consequence of the inaccurate, early stage vision system employed to compute the target trimming surface. By contrast, the trimming motion planning algorithm generated trajectories that smoothly followed their input target and allowed effective branch cutting.     

Arm path planning of a space robot with angular momentum

Arm path planning of a space robot with angular momentum is considered in this paper. A space robot changes its attitude by the arm motion and angular momentum of the space robot has the possibility to reduce the attitude change. A path planning method of the arm where the final satellite attitude becomes the same as the initial one is proposed. The method derives an approximate path first based on the attitude change when the arm moves along an infinitely small closed curve and then modifies the path by the Newton method. The amplitude of the arm motion decreases with the magnitude of the angular momentum, which shows that the proposed method utilizes the angular momentum effectively.     

Collision-avoidance trajectory planning using tube concept: Analysis and simulation

The concept of a tube is introduced and is applied to the solving of the collision-avoidance, minimum-time trajectory planning problem. A collision-free space is represented by an articulated tube with parameters of reference points and path tolerances. For obstacle avoidance, the end effector is constrained to move inside of the tube. An algorithm which will find suboptimal solutions for optimizing both path and velocity history of the trajectory by the use of piecewise joint-space polynomials is presented. This algorithm exploits the robot arm dynamics in realistic environments where obstacles are present and the minimization of task time is desired with smooth motion. Experimental results show that as the path tolerance increases the new algorithm takes advantage of the spatial freedom to provide solutions superior to conventional approaches and to methods based on predefined paths.     

State Space Constrained Iterative Learning Control for Robotic Manipulators

Real‐life work operations of industrial robotic manipulators are performed within a constrained state space. Such operations most often require accurate planning and tracking a desired trajectory, where all the characteristics of the dynamic model are taken into consideration. This paper presents a general method and an efficient computational procedure for path planning with respect to state space constraints. Given a dynamic model of a robotic manipulator, the proposed solution takes into consideration the influence of all imprecisely measured model parameters, making use of iterative learning control (ILC). A major advantage of this solution is that it resolves the well‐known problem of interrupting the learning procedure due to a high transient tracking error or when the desired trajectory is planned closely to the state space boundaries. The numerical procedure elaborated here computes the robot arm motion to accurately track a desired trajectory in a constrained state space taking into consideration all the dynamic characteristics that influence the motion. Simulation results with a typical industrial robot arm demonstrate the robustness of the numerical procedure. In particular, the results extend the applicability of ILC in robot motion control and provide a means for improving the overall trajectory tracking performance of most robotic systems.