Human foot modeling towards footwear design

Comfort test of footwear is mainly based on subjective perception of the wearer and a large number of subjects are required to obtain a reliable result. Therefore, the subjective comfort test is expensive and time consuming. Although the foot size and shape of a subject can be obtained by using a three-dimensional (3D) foot scanner, it is still difficult to create foot motion animations of each subject suitable for computer simulation.In this paper, we propose a fast approach to model foot deformation and present its application in simulating interaction with footwear towards footwear design. The simulation determines deformation of foot and footwear models. It can also determine stress distribution in the footwear. Given an initial foot model and a captured foot motion, human foot animation is created first. Then, the footwear model is fitted to the foot to compute the deformation and stress in the footwear. In this article, the boundary element method (BEM) is adopted. We demonstrate the results by conducting simulation of a captured gait motion. Experimental results showed that the method can be used to simulate human gait motion, and can determine deformation of footwear.     

Motion Sickness Simulation Based on Sensorimotor Control

Sensorimotor control is an essential mechanism for human motions, from involuntary reflex actions to intentional motor skill learning, such as walking, jumping, and swimming. Humans perform various motions according to different task goals and physiological sensory perception; however, most existing computational approaches for motion simulation and generation rarely consider the effects of human perception. The assumption of perfect perception (i.e., no sensory errors) of existing approaches restricts the generated motion types and makes dynamical reactions less realistic. We propose a general framework for sensorimotor control, integrating a balance controller and a vestibular model, to generate perception‐aware motions. By exploiting simulated perception, more natural responses that are closer to human reactions can be generated. For example, motion sickness caused by the impairments in the function of the vestibular system induces postural instability and body sway. Our approach generates physically correct motions and reasonable reactions to external stimuli since the spatial orientation estimation by the vestibular system is essential to preserve balance. We evaluate our framework by demonstrating standing balance on a rotational platform with different angular speeds and duration. The generated motions show that either faster angular speeds or longer rotational duration cause more severe motion sickness. Our results demonstrate that sensorimotor control, integrating human perception and physically‐based control, offers considerable potential for providing more human‐like behaviors, especially for perceptual illusions of human beings, including visual, proprioceptive, and tactile sensations.     

Robust on-line adaptive footplant detection and enforcement for locomotion

A common problem in virtual character computer animation concerns the preservation of the basic foot-floor constraint (or footplant), consisting in detecting it before enforcing it. This paper describes a system capable of generating motion while continuously preserving the footplants for a real-time, dynamically evolving context. This system introduces a constraint detection method that improves classical techniques by adaptively selecting threshold values according to motion type and quality. The footplants are then enforced using a numerical inverse kinematics solver. As opposed to previous approaches, we define the footplant by attaching to it two effectors whose position at the beginning of the constraint can be modified, in order to place the foot on the ground, for example. However, the corrected posture at the constraint beginning is needed before it starts to ensure smoothness between the unconstrained and constrained states. We, therefore, present a new approach based on motion anticipation, which computes animation postures in advance, according to time-evolving motion parameters, such as locomotion speed and type. We illustrate our on-line approach with continuously modified locomotion patterns, and demonstrate its ability to correct motion artifacts, such as foot sliding, to change the constraint position and to modify from a straight to a curved walk motion.     

面向反馈运动控制器的多目标求解

目的 基于物理模拟的人体运动生成方法由于能够合成符合自然规律的运动片段,可实时响应环境的变化,且生成的物理运动不是机械性的重复,因此是近年来计算机动画和虚拟现实领域中最活跃的研究方向之一。然而人体物理模型具有高维、非线性及关节间强耦合性等特点,求解人体物理运动十分困难。反馈控制器常用于人体物理运动控制,求解时通常需要对多个目标函数加权求和,然而权重的设置需多次试验,烦杂耗时。针对运动控制器求解困难的问题,本文提出了一种面向反馈运动控制器的多目标求解方法。方法 首先,对运动数据进行预处理并提取关键帧求解初始控制器,并设计一种改进的反馈控制机制;在此基础上,种群父代个体变异产生子代,采用禁选区域预筛选策略去除不满足约束的个体,并通过重采样获取新解;然后,通过物理仿真获得多目标适应度值,采用区域密度多层取优选取分布均匀的优秀个体作为下一代父代,并通过基于剪枝的多阶段物理求解算法决定是否进入下一阶段优化;经过多次迭代后获得物理控制器,从而生成具有反馈的人体物理运动。结果 针对提出的方法,本文针对多个测试函数和物理运动分别进行实验：在测试函数实验中,本文分别采用经典的测试函数进行实验对比,在相同的迭代次数下,相比之前算法,本文算法中满足约束的优秀个体命中率更高,反转世距离更小,且最优解集的分布更加均匀;物理运动生成实验中,分别针对走路、跑步和翻滚等运动进行物理运动生成,与之前算法进行对比,本文算法可以更早地完成收敛,同时目标函数值更小,表明生成的运动效果更好。结论 本文提出的进化求解方法可以生成不同运动的控制器,该控制器不仅可以生成物理运动,而且还具备外力干扰下保持平衡的能力,解决了运动控制器求解中多目标权重设置困难、优化时间长的问题;除此之外,本文算法还对具有约束的多目标问题具有较好的求解效果。     

Tunable Robustness: An Artificial Contact Strategy with Virtual Actuator Control for Balance

Physically based characters have not yet received wide adoption in the entertainment industry because control remains both difficult and unreliable. Even with the incorporation of motion capture for reference, which adds believability, characters fail to be convincing in their appearance when the control is not robust. To address these issues, we propose a simple Jacobian transpose torque controller that employs virtual actuators to create a fast and reasonable tracking system for motion capture. We combine this controller with a novel approach we call the topple‐free foot strategy which conservatively applies artificial torques to the standing foot to produce a character that is capable of performing with arbitrary robustness. The system is both easy to implement and straightforward for the animator to adjust to the desired robustness, by considering the trade‐off between physical realism and stability. We showcase the benefit of our system with a wide variety of example simulations, including energetic motions with multiple support contact changes, such as capoeira, as well as an extension that highlights the approach coupled with a Simbicon controlled walker. With this work, we aim to advance the state‐of‐the‐art in the practical design for physically based characters that can employ unaltered reference motion (e.g. motion capture data) and directly adapt it to a simulated environment without the need for optimization or inverse dynamics.     

Iterative learning control of a drop foot neuroprosthesis — Generating physiological foot motion in paretic gait by automatic feedback control

Many stroke patients suffer from the drop foot syndrome, which is characterized by a limited ability to lift the foot and leads to a pathological gait. We consider treatment of this syndrome via Functional Electrical Stimulation (FES) of the peroneal nerve during the swing phase of the paretic foot. We highlight the role of feedback control for addressing the challenges that result from the large individuality and time-variance of muscle response dynamics. Unlike many previous approaches, we do not reduce the control problem to the scalar case. Instead, the entire pitch angle trajectory of the paretic foot is measured by means of a 6D Inertial Measurement Unit (IMU) and controlled by an Iterative Learning Control (ILC) scheme for variable-pass-length systems. While previously suggested controllers were often validated for the strongly simplified case of sitting or lying subjects, we demonstrate the effectiveness of the proposed approach in experimental trials with walking drop foot patients. Our results reveal that conventional trapezoidal stimulation intensity profiles may produce a safe foot lift, but often at the cost of too high intensities and an unphysiological foot pitch motion. Starting from such conservative intensity profiles, the proposed learning controller automatically achieves a desired foot motion within one or two strides and keeps adjusting the stimulation to compensate time-variant muscle dynamics and disturbances.     

Real‐time Locomotion Controller using an Inverted‐Pendulum‐based Abstract Model

In this paper, we propose a novel motion controller for the online generation of natural character locomotion that adapts to new situations such as changing user control or applying external forces. This controller continuously estimates the next footstep while walking and running, and automatically switches the stepping strategy based on situational changes. To develop the controller, we devise a new physical model called an inverted‐pendulum‐based abstract model (IPAM). The proposed abstract model represents high‐dimensional character motions, inheriting the naturalness of captured motions by estimating the appropriate footstep location, speed and switching time at every frame. The estimation is achieved by a deep learning based regressor that extracts important features in captured motions. To validate the proposed controller, we train the model using captured motions of a human stopping, walking, and running in a limited space. Then, the motion controller generates human‐like locomotion with continuously varying speeds, transitions between walking and running, and collision response strategies in a cluttered space in real time.     

A new ship's auto pilot design through a stochastic model

A new type of ship's autopilot system is designed by a statistical approach. A ship's motion at sea is described by a multi-variable autoregressive model using minimum AIC (Akaike's Information Criterion) procedure. Through the fitted model, the ship's behavior is analyzed and an optimal control law for a ship under a newly introduced criterion function is derived. The feasibility of our control system is checked by both digital and hybrid simulations. The results of the simulation show that with our controller the yaw motion is depressed through smoother and less rudder motion than that of the conventional autopilot systems and the ill effect of rolling is avoided. It is expected that the controller has another merit: it is quite robust for possible changes of external environments. Finally, a successful result of an actual sea test is briefly discussed. Thus, the possibility of realizing an entirely new autopilot system by a stochastic model is demonstrated.     

Modelling of Human Jaw Motion in Sliding Contact

Dental CAD/CAM requires appropriate modelling of human jaw motion in contact with the opposite jaw. Such modelling is necessary for planning orthodontic treatment and robust design of dental restorations, especially their occlusal surfaces which must fit the existing articulation patterns. We propose a fast and purely geometrical approach to model sliding of the lower jaw over the surface of the upper, fixed jaw. For every discrete step of the motion, a new position and orientation of the sliding object is found by maximizing its displacement toward the fixed object, to obtain stable contact without interpenetrations. We impose constraints on the range of rotation angles to justify simplifications of our linear model handling the distance from the fixed object to chosen points on the surface of the sliding object as a function of its configuration. In such formulation, we reduce the complexity of the problem of object placement in contact to that of a linear optimization task. Expensive multi-point collision detection and distance computation are handled by rasterizing graphics hardware, which supports generation of valid configurations for complex objects at stable and interactive speeds. Our model of human jaw sliding compares favourably with experimental motion data. © 1997 John Wiley & Sons, Ltd.     

Editing and Synthesizing Two‐Character Motions using a Coupled Inverted Pendulum Model

This study aims to develop a controller for use in the online simulation of two interacting characters. This controller is capable of generalizing two sets of interaction motions of the two characters based on the relationships between the characters. The controller can exhibit similar motions to a captured human motion while reacting in a natural way to the opponent character in real time. To achieve this, we propose a new type of physical model called a coupled inverted pendulum on carts that comprises two inverted pendulum on a cart models, one for each individual, which are coupled by a relationship model. The proposed framework is divided into two steps: motion analysis and motion synthesis. Motion analysis is an offline preprocessing step, which optimizes the control parameters to move the proposed model along a motion capture trajectory of two interacting humans. The optimization procedure generates a coupled pendulum trajectory which represents the relationship between two characters for each frame, and is used as a reference in the synthesis step. In the motion synthesis step, a new coupled pendulum trajectory is planned reflecting the effects of the physical interaction, and the captured reference motions are edited based on the planned trajectory produced by the coupled pendulum trajectory generator. To validate the proposed framework, we used a motion capture data set showing two people performing kickboxing. The proposed controller is able to generalize the behaviors of two humans to different situations such as different speeds and turning speeds in a realistic way in real time.     

Fuzzy sliding mode control of a finger of a humanoid robot hand

Abstract: The motion control problem for the finger of a humanoid robot hand is investigated. First, the index finger of the human hand is dynamically modelled as a kinematic chain of cylindrical links. During construction of the model, special attention is given to determining bone dimensions and masses that are similar to the real human hand. After the kinematic and dynamic analysis of the model, in order to ensure that the finger model tracks its desired trajectory during a closing motion, a fuzzy sliding mode controller is applied to the finger model. In this controller, a fuzzy logic algorithm is used in order to tune the control gain of the sliding mode controller; thus, an adaptive controller is obtained. Finally, numerical results, which include a performance comparison of the proposed fuzzy sliding mode controller and a conventional sliding mode controller, are presented. The results demonstrate that the proposed control method can be used to perform the desired motion task for humanoid robot hands efficiently.     

无人机实时飞行仿真系统的时滞补偿方法

针对无人机半物理仿真系统的模拟运动滞后问题,在分析无人机模型和飞行仿真转台模型的基础上,提出一种新的飞行仿真系统构建方案.基于自适应逆控制和李亚普洛夫函数,设计了能够补偿运动滞后的数字控制器.将原始无人机模型与数字控制器进行联合计算,获取无人机修正模型的输出,完成了对仿真运动滞后的补偿.仿真结果说明了时滞补偿方案的有效性.     

Human motion generation with multifactor models

To generate human motions with various specific attributes is a difficult task because of high dimensionality and complexity of human motions. This paper presents a novel human motion model for generating and editing motions with multiple factors. A set of motions performed by several actors with various styles was captured for constructing a well‐structured motion database. Subsequently, MICA (multilinear independent component analysis) model that combines ICA and conventional multilinear framework was adopted for the construction of a multifactor model. With this model, new motions can be synthesized by interpolation and through solving optimization problems for the specific factors. Our method offers a practical solution to edit stylistic human motions in a parametric space learnt with MICA model. We demonstrated the power of our method by generating and editing sideways stepping, reaching, and striding over obstructions using different actors with various styles. The experimental results show that our method can be used for interactive stylistic motion synthesis and editing. Copyright © 2011 John Wiley & Sons, Ltd.     

A new controller architecture for high performance, robust, andfault-tolerant control

We propose a new feedback controller architecture. The distinguished feature of our new controller architecture is that it shows structurally how the controller design for performance and robustness may be done separately which has the potential to overcome the conflict between performance and robustness in the traditional feedback framework. The controller architecture includes two parts: one part for performance and the other part for robustness. The controller architecture works in such a way that the feedback control system can be solely controlled by the performance controller when there is no model uncertainties and external disturbances and the robustification controller can only be active when there are model uncertainties or external disturbances     

深度纯追随的拟人化无人驾驶转向控制模型

目的 在无人驾驶系统技术中,控制车辆转向以跟踪特定路径是实现驾驶的关键技术之一,大量基于传统控制的方法可以准确跟踪路径,然而如何在跟踪过程中实现类人的转向行为仍是当前跟踪技术面临的挑战性问题之一。现有传统转向模型并没有参考人类驾驶行为,难以实现过程模拟。此外,现有大多数基于神经网络的转向控制模型仅仅以视频帧作为输入,鲁棒性和可解释性不足。基于此,本文提出了一个融合神经网络与传统控制器的转向模型：深度纯追随模型（deep pure pursuit,deep PP）。方法 在deep PP中,首先利用卷积神经网络（convolutional neural network,CNN）提取驾驶环境的视觉特征,同时使用传统的纯追随（pure pursuit,PP）控制器融合车辆运动模型以及自身位置计算跟踪给定的全局规划路径所需的转向控制量。然后,通过拼接PP的转向结果向量和视觉特征向量得到融合特征向量,并构建融合特征向量与人类转向行为之间的映射模型,最终实现预测无人驾驶汽车转向角度。结果 实验将在CARLA（Center for Advanced Research on Language Acquisition）仿真数据集和真实场景数据集上进行,并与Udacity挑战赛的CNN模型和传统控制器进行对比。实验结果显示,在仿真数据集的14个复杂天气条件下,deep PP比CNN模型和传统转向控制器更贴近无人驾驶仪的转向指令。在使用均方根误差（root mean square error,RMSE）作为衡量指标时,deep PP相比于CNN模型提升了50.28%,相比于传统控制器提升了35.39%。最后,真实场景实验验证了提出的模型在真实场景上的实用性。结论 本文提出的拟人化转向模型,综合了摄像头视觉信息、位置信息和车辆运动模型信息,使得无人驾驶汽车的转向行为更贴近人类驾驶行为,并在各种复杂驾驶条件下保持了高鲁棒性。     

Generalized Model‐Based Human Motion Recognition with Body Partition Index Maps

Content‐based human motion analysis has captured extensive concerns of researchers from the domains of computer animation, human‐machine interaction, entertainment, etc. However, it is a non‐trivial task due to the spatial and temporal variations in the motion data. In this paper, we propose a generalized model (GM)‐based approach to model the variations and accurately recognize motion patterns. We partition the human character model into five parts, and extract the features of the submotions of each specific body part using clustering techniques. These features from the training trials in each class are combined to build the GM. We propose a new penalty based similarity measure for DTW to be used with the GMs for isolated motion recognition. On the other hand, from the GMs five body partition index maps are constructed and used for matching together with a flexible end point detection scheme during continuous motion recognition. In the experiments, we examine the effectiveness and efficiency of the approach in both isolated motion and continuous motion recognition. The results show that our proposed method has good performance compared with other state‐of‐the‐art methods in recognition accuracy and processing speed.     

直流调速系统广义通用模型控制器的设计及应用

在直流调速系统中,转速、电流双闭环调速系统是应用最广的直流调速系统,设想应用Lie导数概念结合通用模型控制器对于直流调速系统的电流调节器和转速调节器进行设计,可以将被控对象的模型嵌入到控制器中,适当地选择主导极点可以获得比较好的调节品质,仿真结果验证了所设计的控制器的有效性.     

Coupled dynamical system based arm–hand grasping model for learning fast adaptation strategies

Performing manipulation tasks interactively in real environments requires a high degree of accuracy and stability. At the same time, when one cannot assume a fully deterministic and static environment, one must endow the robot with the ability to react rapidly to sudden changes in the environment. These considerations make the task of reach and grasp difficult to deal with. We follow a Programming by Demonstration (PbD) approach to the problem and take inspiration from the way humans adapt their reach and grasp motions when perturbed. This is in sharp contrast to previous work in PbD that uses unperturbed motions for training the system and then applies perturbation solely during the testing phase. In this work, we record the kinematics of arm and fingers of human subjects during unperturbed and perturbed reach and grasp motions. In the perturbed demonstrations, the target’s location is changed suddenly after the onset of the motion. Data show a strong coupling between the hand transport and finger motions. We hypothesize that this coupling enables the subject to seamlessly and rapidly adapt the finger motion in coordination with the hand posture. To endow our robot with this competence, we develop a coupled dynamical system based controller, whereby two dynamical systems driving the hand and finger motions are coupled. This offers a compact encoding for reach-to-grasp motions that ensures fast adaptation with zero latency for re-planning. We show in simulation and on the real iCub robot that this coupling ensures smooth and “human-like” motions. We demonstrate the performance of our model under spatial, temporal and grasp type perturbations which show that reaching the target with coordinated hand–arm motion is necessary for the success of the task.     

Perceptual validity in animation of human motion

The crucial concept of modeling and synthesis/control of human motion (including face and body) for animation has been widely studied and explored in the literature. In this regard, the audience's perception of generated or recorded animation scenes is of critical importance. In this paper, we explore and conceptualize the general notions that need to be taken into account for human motion to maintain perceptual accuracy. We propose a paradigm called Perceptual Validity composed of four major components, which are discussed in detail. The model is concerned with different aspects of the scene such as correct illustration of the stimuli, context, and local/global relations of various visual cues present in human motion. Satisfying all the proposed principles, based on the literature, seems compulsory and vital for synthesis of perceptually valid animation scenes of human motion. We investigate the relative significance of the different components of the paradigm using feedback from expert animators and conduct a case study on one of the components of the paradigm. For further evaluation and exploration, Disney's principles of animation are discussed and compared against our proposed paradigm. We argue that while there are significant parallels and overlaps, our model is only focused on and more inclusive towards human motion and can therefore provide a valuable set of guidelines for animators in the field of character animation. Copyright © 2015 John Wiley & Sons, Ltd.