Biomechanics of manual material handling through simulation: Computational aspects

Computerized human motion simulation allows generation of dynamic human motions on computers. Biomechanical stresses can be estimated using the motions generated on a computer without actually collecting joint coordinate data. A two-dimensional whole-body lifting simulation model is presented in this paper. The model assumes that humans perform lifting activities based on minimization of physical work, subject to various constraints. The simulation method contains three major computation units: trajectory formation unit, dynamics of motion unit, and nonlinear optimization unit. The trajectory formation unit generates smooth polynomials representing motion characteristics of human lifting. Kinematics and kinetics are calculated in the dynamics unit. Objective and constraint functions are evaluated in the optimization unit. Optimal motions are generated by minimizing the objective function, subject to the constraints. Computation methods of the three units and simulation results are presented.     

Weighted hierarchical quadratic programming: assigning individual joint weights for each task priority

This paper proposes a novel method that computes the optimal solution of the weighted hierarchical optimization problem for both equality and inequality tasks. The method is developed to resolve the redundancy of robots with a large number of Degrees of Freedom (DoFs), such as a mobile manipulator or a humanoid, so that they can execute multiple tasks with differently weighted joint motion for each priority level. The proposed method incorporates the weighting matrix into the first-order optimality condition of the optimization problem and leverages an active-set method to handle equality and inequality constraints. In addition, it is computationally efficient because the solution is calculated in a weighted joint space with symmetric null-space projection matrices for propagating recursively to a low priority task. Consequently, robots that utilize the proposed method effectively show whole-body motions handling prioritized tasks with differently weighted joint spaces. The effectiveness of the proposed method was validated through experiments with a nonholonomic mobile manipulator as well as a humanoid.

     

基于逆运动学和重构式ICA的人体运动风格分析与合成

使用独立成分分析（Independent component analysis,ICA）来建模运动风格、合成风格化的人体运动,是一种有效且有前景的手段.为了避免现有方法在设定独立成分个数或子空间结构时的人为影响,并提高风格成分的质量,提出一种基于重构式独立成分分析的运动风格分析方法.由于放弃了混合矩阵的正交性约束,一方面,拥有了更多的自由度来表示各独立成分;另一方面,利用特征的过完备性以及自身在特征选择时的稀疏特性,能够自动地确立独立成分数目.此外,通过结合基于主测地线分析的逆运动学与运动过渡技术,该方法能够合成包含多种风格、任意长度的行走运动,同时还能通过编辑特定帧的人体姿势来约束合成的结果.实验结果表明,该方法能够有效地分析出行走、跳跃和踢腿等运动中代表风格的独立成分,并根据用户对风格的编辑,实时地生成自然、平滑的运动.     

Reconstructing whole-body motions with wrist trajectories

Reconstructing whole-body motions using only a low-dimensional input reduces the cost of and efforts for performance capture significantly, and yet remains a challenging problem. We introduce a novel technique that synthesizes whole-body motion using the two wrist trajectories. Given the wrist trajectories, we first determine the optimal ankle trajectories from a large number of candidate ankle paths obtained from example poses in the motion database. The optimal trajectory is efficiently achieved by solving for the shortest path problem in a directed acyclic graph. Next, we use both the wrist and ankle trajectories as the low-dimensional control signals to achieve the whole-body pose at each time step. We show that our method can reconstruct various whole-body motions that can be recognized by arm motions, such as walking, stepping, and in-place upper-body motions. Comparisons with ground truth motions and with other methods are provided.     

Constrained synthesis of textural motion for articulated characters

Obtaining high-quality, realistic motions of articulated characters is both time consuming and expensive, necessitating the development of easy-to-use and effective tools for motion editing and reuse. We propose a new simple technique for generating constrained variations of different lengths from an existing captured or otherwise animated motion. Our technique is applicable to textural motions, such as walking or dancing, where the motion sequence can be decomposed into shorter motion segments without an obvious temporal ordering among them. Inspired by previous work on texture synthesis and video textures, our method essentially produces a reordering of these shorter segments. Discontinuities are eliminated by carefully choosing the transition points and applying local adaptive smoothing in their vicinity, if necessary. The user is able to control the synthesis process by specifying a small number of simple constraints.     

Pose Controlled Physically Based Motion

In this paper we describe a new method for generating and controlling physically‐based motion of complex articulated characters. Our goal is to create motion from scratch, where the animator provides a small amount of input and gets in return a highly detailed and physically plausible motion. Our method relieves the animator from the burden of enforcing physical plausibility, but at the same time provides full control over the internal DOFs of the articulated character via a familiar interface. Control over the global DOFs is also provided by supporting kinematic constraints. Unconstrained portions of the motion are generated in real time, since the character is driven by joint torques generated by simple feedback controllers. Although kinematic constraints are satisfied using an iterative search (shooting), this process is typically inexpensive, since it only adjusts a few DOFs at a few time instances. The low expense of the optimization, combined with the ability to generate unconstrained motions in real time yields an efficient and practical tool, which is particularly attractive for high inertia motions with a relatively small number of kinematic constraints.     

基于hp自适应伪谱法的空间机器人路径规划

针对空间机器人运动过程中基座姿态产生较大扰动的问题,基于hp自适应高斯伪谱法提出了一种以基座所受反作用力矩最小为目标函数的空间机器人路径规划方法.首先,综合考虑空间机器人运动过程中存在的关节角度约束、关节角速度约束、控制力矩约束及初始状态和终端状态约束等约束条件,将空间机器人路径规划问题看成满足一系列约束条件和边界条件并实现特定性能指标最优的最优控制问题.其次,结合hp自适应高斯伪谱法（hp-AGPM）与非线性规划技术,求解带有边界约束和路径约束的优化控制问题,得到满足约束且性能指标最优的空间机器人运动轨迹.最后,以平面2自由度空间机械臂为例对所设计方法进行仿真验证,并与其他伪谱法进行对比分析.仿真结果表明：本文算法能在10.6 s的时间内规划出满足各约束条件且容许偏差低于10^-6的最优运动轨迹,并且在计算速度和配点数量上都优于其他伪谱法.     

A natural human hand model

We present a skeletal linked model of the human hand that has natural motion. We show how this can be achieved by introducing a new biology-based joint axis that simulates natural joint motion and a set of constraints that reduce an estimated 150 possible motions to twelve. The model is based on observation and literature. To facilitate testing and evaluation, we present a simple low polygon count skin that can stretch and bulge. To evaluate we first introduce a hand-motion taxonomy in a two-dimensional parameter space based on tasks that are evolutionary linked to the environment. Second, we discuss and test the model. The appendix shows motion sequences of the model and the real hand. Animations can be fetched from our website.     

Enriching a motion database by analogous combination of partial human motions

We have synthesized new human body motions from existing motion data, by dividing the body of an animated character into several parts, such as upper and lower body, and partitioning the motion of the character into corresponding partial motions. By combining different partial motions, we can generate new motion sequences. We select the most natural-looking combinations by analyzing the similarity of partial motions, using techniques such as motion segmentation, dimensionality reduction, and clustering. These new combinations can dramatically increase the size of a motion database, allowing more score in selecting motions to meet constraints, such as collision avoidance. We verify the naturalness and physical plausibility of the new motions using an SVM learning model and by analysis of static and dynamic balance.     

Fluid Simulation with Articulated Bodies

We present an algorithm for creating realistic animations of characters that are swimming through fluids. Our approach combines dynamic simulation with data-driven kinematic motions (motion capture data) to produce realistic animation in a fluid. The interaction of the articulated body with the fluid is performed by incorporating joint constraints with rigid animation and by extending a solid/fluid coupling method to handle articulated chains. Our solver takes as input the current state of the simulation and calculates the angular and linear accelerations of the connected bodies needed to match a particular motion sequence for the articulated body. These accelerations are used to estimate the forces and torques that are then applied to each joint. Based on this approach, we demonstrate simulated swimming results for a variety of different strokes, including crawl, backstroke, breaststroke, and butterfly. The ability to have articulated bodies interact with fluids also allows us to generate simulations of simple water creatures that are driven by simple controllers.     

Online Avatar Motion Adaptation to Morphologically-similar Spaces

In avatar-mediated telepresence systems, a similar environment is assumed for involved spaces, so that the avatar in a remote space can imitate the user's motion with proper semantic intention performed in a local space. For example, touching on the desk by the user should be reproduced by the avatar in the remote space to correctly convey the intended meaning. It is unlikely, however, that the two involved physical spaces are exactly the same in terms of the size of the room or the locations of the placed objects. Therefore, a naive mapping of the user's joint motion to the avatar will not create the semantically correct motion of the avatar in relation to the remote environment. Existing studies have addressed the problem of retargeting human motions to an avatar for telepresence applications. Few studies, however, have focused on retargeting continuous full-body motions such as locomotion and object interaction motions in a unified manner. In this paper, we propose a novel motion adaptation method that allows to generate the full-body motions of a human-like avatar on-the-fly in the remote space. The proposed method handles locomotion and object interaction motions as well as smooth transitions between them according to given user actions under the condition of a bijective environment mapping between morphologically-similar spaces. Our experiments show the effectiveness of the proposed method in generating plausible and semantically correct full-body motions of an avatar in room-scale space.     

A hybrid method for real-time animation of trees swaying in wind fields

Trees are one of the most important elements of natural landscapes. Therefore, in computer graphics, there is a great demand for methods to realize the natural representation of trees in virtual landscapes in various fields such as the entertainment industry or environmental assessment in construction. Many studies have been made on techniques in which the shapes of trees are modeled but only a few studies have been reported on methods to incorporate the shapes with motions in a wind field. Most of these studies use physical simulation techniques based on the equations of motion to generate the branch motions and cannot realize the motions of individual leaves. In this paper, we propose a method to create the natural motions of individual leaves and branches swaying in a wind field. The proposed method uses a hybrid approach combining a stochastic method and a simulation method. The stochastic method is based on 1/f noise, which is observed in various natural phenomena, and provides natural motion to leaves and branches. In addition, a simple simulation method based on the spring model is applied to branches to enhance the reality of their motions. This method enables the real-time creation of the leaf and branch motions. Diverse motions according to tree species and shapes and wind conditions can be easily realized by controlling the parameters.     

Hybrid predictive dynamics: a new approach to simulate human motion

A new methodology, called hybrid predictive dynamics (HPD), is introduced in this work to simulate human motion. HPD is defined as an optimization-based motion prediction approach in which the joint angle control points are unknowns in the equations of motion. Some of these control points are bounded by the experimental data. The joint torque and ground reaction forces are calculated by an inverse algorithm in the optimization procedure. Therefore, the proposed method is able to incorporate motion capture data into the formulation to predict natural and subject-specific human motions. Hybrid predictive dynamics includes three procedures, and each is a sub-optimization problem. First, the motion capture data are transferred from Cartesian space into joint space by using optimization-based inverse kinematics (IK) methodology. Secondly, joint profiles obtained from IK are interpolated by B-spline control points by using an error-minimization algorithm. Third, boundaries are built on the control points to represent specific joint profiles from experiments, and these boundaries are used to guide the predicted human motion. To predict more accurate motion, the boundaries can also be built on the kinetic variables if the experimental data are available. The efficiency of the method is demonstrated by simulating a box-lifting motion. The proposed method takes advantage of both prediction and tracking capabilities simultaneously, so that HPD has more applications in human motion prediction, especially towards clinical applications.     

Theoretical Investigation of a Time-Suboptimal Control Method for Rotational Motions of Industrial Manipulators End-Effectors

A time suboptimal control method is developed for rotational motions of industrial manipulators end-effectors. A set of nonlinear equations is obtained and linearized at each time step of the motion. A method which yields the time suboptimal joint angular velocities as functions of time is developed by considering constraints on joint velocities as well as joint and tool center point frame accelerations. The method is demonstrated on a six-degree-of-freedom elbow-type manipulator.     

Flexible Splicing of Upper‐Body Motion Spaces on Locomotion

This paper presents an efficient technique for synthesizing motions by stitching, or splicing, an upper‐body motion retrieved from a motion space on top of an existing lower‐body locomotion of another motion. Compared to the standard motion splicing problem, motion space splicing imposes new challenges as both the upper and lower body motions might not be known in advance. Our technique is the first motion (space) splicing technique that propagates temporal and spatial properties of the lower‐body locomotion to the newly generated upper‐body motion and vice versa. Whereas existing techniques only adapt the upper‐body motion to fit the lower‐body motion, our technique also adapts the lower‐body locomotion based on the upper body task for a more coherent full‐body motion. In this paper, we will show that our decoupled approach is able to generate high‐fidelity full‐body motion for interactive applications such as games.     

A Muscle-based Feed-forward Controller of the Human Body

There is an increasing demand for human body motion data. Motion capture and physical animation have been used to generate such data. It is, however, apparent that such methods cannot automatically generate arbitrary human body motions. A human body is a redundant multi-linked body controlled by a number of muscles. For this reason, the muscles must work appropriately and cooperatively for controlling the whole body. It is well-known that the human body control system is composed of two parts: The open-loop feed-forward control system and the closed-loop feedback control system. Many researchers have investigated the characteristics of the latter by analyzing the response of a human body to various external perturbations. However, for the former, very few studies have been done. This paper proposes an open-loop feed-forward model of the lower extremities which includes postural control for accurate animation of a human body. Assumptions are made here that the feed-forward controller minimizes a certain objective value while keeping the balance of the body stable. The actual human motion data obtained using a motion capturing technique is compared with the trajectory calculated using our method for verification. The best criteria which is based on muscle dynamics is proposed. Using our method, dynamically correct human animation can be created by merely specifying a few key postures.     

Memory-Based Human Motion Simulation for Computer-Aided Ergonomic Design

A novel memory-based motion simulation (MBMS) model was developed as a general framework for simulating natural human motions for computer-aided ergonomic design. The MBMS model utilizes real human motion samples recorded in motion capture experiments as templates for simulating novel motions. Such human motion samples are stored in a motion database. When a user submits an input simulation scenario to the model, a motion search engine termed the ldquoroot motion finderrdquo in the model searches the motion database and retrieves the motion samples that closely match the given scenario. The retrieved motions, referred to as root motions, may significantly differ from one another in the underlying movement technique. Such variability within the root motion set is analyzed and graphically summarized by a model component termed the motion variability analyzer. This analysis helps users rapidly identify alternative movement techniques for the given input simulation scenario and simulate human motions based on alternative movement techniques. Since root motions do not exactly satisfy but only closely match the input simulation scenario, a motion modification (MoM) algorithm adapts them to fit the scenario by systematically deforming them in the joint angle-time domain. The MoM algorithm retains the root motions' fundamental spatial-temporal structure and minimizes deviations from the root motions during such deformations. The MBMS model overcomes limitations of existing simulation models and achieves the following: 1) simulation of categorically different motions based on a single unified model; 2) simple and efficient learning of new motion behaviors; and 3) representation and simulation of human motion variability.     

Stepping over obstacles with humanoid robots

The wide potential applications of humanoid robots require that the robots can walk in complex environments and overcome various obstacles. To this end, we address the problem of humanoid robots stepping over obstacles in this paper. We focus on two aspects, which are feasibility analysis and motion planning. The former determines whether a robot can step over a given obstacle, and the latter discusses how to step over, if feasible, by planning appropriate motions for the robot. We systematically examine both of these aspects. In the feasibility analysis, using an optimization technique, we cast the problem into global optimization models with nonlinear constraints, including collision-free and balance constraints. The solutions to the optimization models yield answers to the possibility of stepping over obstacles under some assumptions. The presented approach for feasibility provides not only a priori knowledge and a database to implement stepping over obstacles, but also a tool to evaluate and compare the mobility of humanoid robots. In motion planning, we present an algorithm to generate suitable trajectories of the feet and the waist of the robot using heuristic methodology, based on the results of the feasibility analysis. We decompose the body motion of the robot into two parts, corresponding to the lower body and upper body of the robot, to meet the collision-free and balance constraints. This novel planning method is adaptive to obstacle sizes, and is, hence, oriented to autonomous stepping over by humanoid robots guided by vision or other range finders. Its effectiveness is verified by simulations and experiments on our humanoid platform HRP-2.