Reusing Primitive and Acquired Motion Knowledge for Gait Generation of a Six-legged Robot Using Genetic Programming

There has been growing interest in motion planning problems for mobile robots. In this field, the main research is to generate a motion for a specific robot and task without previously acquired motions. However it is too wasteful not to use hard-earned acquired motions for other tasks. Here, we focus on a mechanism of reusing acquired motion knowledge and study a motion planning system able to generate and reuse motion knowledge. In this paper, we adopt a tree-based representation for expressing knowledge of motion, and propose a hierarchical knowledge for realizing a reuse mechanism. We construct a motion planning system using hierarchical knowledge as motion knowledge and using genetic programming as a learning method. We apply a proposed method for the gait generation task of a six-legged locomotion robot and show its availability with computer simulation.     

Real-time Unsupervised Segmentation of human whole-body motion and its application to humanoid robot acquisition of motion symbols

An interactive loop between motion recognition and motion generation is a fundamental mechanism for humans and humanoid robots. We have been developing an intelligent framework for motion recognition and generation based on symbolizing motion primitives. The motion primitives are encoded into Hidden Markov Models (HMMs), which we call “motion symbols”. However, to determine the motion primitives to use as training data for the HMMs, this framework requires a manual segmentation of human motions. Essentially, a humanoid robot is expected to participate in daily life and must learn many motion symbols to adapt to various situations. For this use, manual segmentation is cumbersome and impractical for humanoid robots. In this study, we propose a novel approach to segmentation, the Real-time Unsupervised Segmentation (RUS) method, which comprises three phases. In the first phase, short human movements are encoded into feature HMMs. Seamless human motion can be converted to a sequence of these feature HMMs. In the second phase, the causality between the feature HMMs is extracted. The causality data make it possible to predict movement from observation. In the third phase, movements having a large prediction uncertainty are designated as the boundaries of motion primitives. In this way, human whole-body motion can be segmented into a sequence of motion primitives. This paper also describes an application of RUS to AUtonomous Symbolization of motion primitives (AUS). Each derived motion primitive is classified into an HMM for a motion symbol, and parameters of the HMMs are optimized by using the motion primitives as training data in competitive learning. The HMMs are gradually optimized in such a way that the HMMs can abstract similar motion primitives. We tested the RUS and AUS frameworks on captured human whole-body motions and demonstrated the validity of the proposed framework.     

Robot motion design using bunraku emotional expressions – focusing on Jo-Ha-Kyū in sounds and movements*

One of the UNESCO intangible cultural heritages Bunraku puppets can play one of the most beautiful puppet motions in the world. The Bunraku puppet motions can express emotions without the so-called ‘Uncanny Valley.’ We try to convert these emotional motions into robot affective motions so that robots can interact with human beings more comfortable. In so doing, in the present paper, we present a robot motion design framework using Bunraku affective motions that are based on the so-called ‘Jo-Ha-Kyū,’ and convert a few simple Bunraku motions into a robot motions using one of deep learning methods. Our primitive experiments show that Jo-Ha-Kyū can be incorporated into robot motion design smoothly, and some simple affective robot motions can be designed using our proposed framework.     

A Fast Approach for Robot Motion Planning

This paper describes a new approach to robot motion planning that combines the end-point motion planning with joint trajectory planning for collision avoidance of the links. Local and global methods are proposed for end-point motion planning. The joint trajectory planning is achieved through a pseudoinverse kinematic formulation of the problem. This approach enables collision avoidance of the links by a fast null-space vector computation. The power of the proposed planner derives from: its speed; the good properties of the potential function for end-point motion planning; and from the simultaneous avoidance of the links collision, kinematic singularities, and local minima of the potential function. The planner is not defined over computationally expensive configuration space and can be applied for real-time applications. The planner shows to be faster than many previous planners and can be applied to robots with many degrees of freedom. The effectiveness of the proposed local and global planning methods as well as the general robot motion planning approach have been experimented using the computer-simulated robots. Some of the simulation results are included in this paper.     

广义Voronoi图求解多机器人运动规划

在拥挤环境中,由于障碍物的边界形状比较复杂,需要使用广义Voronoi图表示空间环境。且在多移动机器人的运动规划过程中,需要协调多个机器人的运动,必须得到Voronoi图通道的宽度。为此提出了一种计算拥挤障碍物环境中生成的广义Voronoi图及其通道宽度的算法。并在生成的Voronoi图上利用A*算法对多个机器人进行路径规划,并利用分布式方法协调多个机器人运动。对协调两个机器人运动的过程进行了仿真,仿真结果表明利用提出的算法生成的具有通道宽度信息的Voronoi图能够满足多移动机器人运动规划的需要。     

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.     

Experimental realization of dynamic walking of the biped humanoid robot KHR-2 using zero moment point feedback and inertial measurement

This paper describes a novel control algorithm for dynamic walking of biped humanoid robots. For the test platform, we developed KHR-2 (KAIST Humanoid Robot-2) according to our design philosophy. KHR-2 has many sensory devices analogous to human sensory organs which are particularly useful for biped walking control. First, for the biped walking motion, the motion control architecture is built and then an appropriate standard walking pattern is designed for the humanoid robots by observing the human walking process. Second, we define walking stages by dividing the walking cycle according to the characteristics of motions. Third, as a walking control strategy, three kinds of control schemes are established. The first scheme is a walking pattern control that modifies the walking pattern periodically based on the sensory information during each walking cycle. The second scheme is a real-time balance control using the sensory feedback. The third scheme is a predicted motion control based on a fast decision from the previous experimental data. In each control scheme, we design online controllers that are capable of maintaining the walking stability with the control objective by using force/torque sensors and an inertial sensor. Finally, we plan the application schedule of online controllers during a walking cycle according to the walking stages, accomplish the walking control algorithm and prove its effectiveness through experiments with KHR-2.     

人体步行规律与仿人机器人步态规划

从仿生学角度分析了人体的步行运动规律,提出了一种基于人体运动规律的仿人机器人步态参数设定方法.首先对人体步行运动数据进行捕捉并分析,得出人体各步态参数间的函数关系,以人体步行相似性作为评价指标,提出仿人机器人步态参数的设定方法.其次,通过分析人体在步行过程中的补偿支撑脚偏航力矩的基本原理,提出了基于双臂及腰关节协调运动的仿人机器人偏航力矩补偿算法,以提高仿人机器人行走的稳定性.最后通过仿真及实验验证了所提出的步态规划方法的正确性及有效性.     

提高柔性冗余度机器人动态特性的最小变形能法

冗余度柔性机器人的运动规划是机器人领域的重要前沿课题之一 .利用此机器人的冗余特性 ,可以改善其运动学和动力学性能 .柔性机器人的变形能能够很好地反映出其整体弹性变形程度 .本文提出了在最小变形能意义下的柔性冗余度机器人运动学规划的新方法 .以平面三柔性臂机器人为例进行了仿真 ,通过与最小末端误差意义下的规划策略进行比较 ,充分显示了最小变形能法在提高柔性机器人动态性能的有效性和优越性     

A Study on Error Recovery Search Strategies of Electronic Connector Mating for Robotic Fault-Tolerant Assembly

In this paper, a CAD-based trajectory planning scheme for parallel machining robots is introduced using the parametric Non-uniform rational basis spline (NURBS) curves. First, a trajectory is designed via a NURBS curve then, a motion scheduling architecture consisting of time-dependent and constant feedrate profiles is advised to generate the position commands on the represented NURBS curve as the tool path. Using the generated commands, the inverse kinematics is elaborated to obtain the joints motions of the parallel machining robot. This paper investigates the NURBS trajectory generation for a parallel robot with 4(UPS)-PU mechanism as the case study. In order to evaluate the effectiveness of the proposed method, the inverse kinematic results for the parallel machining robot of 4(UPS)-PU is compared with the simulation results obtained from the CATIA software. The results confirmed that the proposed trajectory planning scheme along with the advised motion planning architecture is not only feasible for the parallel machining robots but also yields a smooth trajectory with a satisfactory performance for all the joints.     

Spatio-temporal structure of human motion primitives and its application to motion prediction

This paper proposes a novel approach to structuring behavioral knowledge based on symbolization of human whole body motions, hierarchical classification of the motions, and extraction of the causality among the motions. The motion patterns are encoded into parameters of corresponding Hidden Markov Models (HMMs), where each HMM abstracts the dynamics of motion pattern, and hereafter is referred to as “motion symbol”. The motion symbols allow motion recognition and synthesis. The motion symbols are organized into a hierarchical tree structure representing the property of spatial similarity among the motion patterns, and this tree is referred to as “motion symbol tree”. Seamless motion is segmented into a sequence of motion primitives, each of which is classified as a motion symbol based on the motion symbol tree. The seamless motion results in a sequence of the motion symbols, which is stochastically represented as transitions between the motion symbols by an N-gram model. The motion symbol N-gram model is referred to as “motion symbol graph”. The motion symbol graph extracts the temporal causality among the human behaviors. The integration of the motion symbol tree and the motion symbol graph makes it possible to recognize motion patterns fast and predict human behavior during observation. The experiments on a motion dataset of radio calisthenics and on a large motion dataset provided by CMU motion database validate the proposed framework.     

Fast Motion Planning by Parallel Processing – a Review

One of the many features needed to support the activities of autonomoussystems is the ability to plan motion. This enables robots to move in theirenvironment securely and to accomplish given tasks. Unfortunately, thecontrol loop comprising sensing, planning, and acting has not yet beenclosed for robots in dynamic environments. One reason involves the longexecution times of the motion planning component. A solution for thisproblem is offered by the use of highly parallel computation. Thus, animportant task is the parallelization of existing motion planning algorithmsfor robots so that they are suitable for highly parallel computation. Inseveral cases, completely new algorithms have to be designed, so that aparallelization is feasible. In this survey, we review recent approaches tomotion planning using parallel computation. As a classification scheme, weuse the structure given by the different approaches to the robotsmotion planning. For each approach, the available parallel processingmethods are discussed. Each approach is assigned a unique class. Finally,for each research work referenced, a list of keywords is given.     

Human motion prediction for human-robot collaboration

In human-robot collaborative manufacturing, industrial robots would work alongside human workers who jointly perform the assigned tasks seamlessly. A human-robot collaborative manufacturing system is more customised and flexible than conventional manufacturing systems. In the area of assembly, a practical human-robot collaborative assembly system should be able to predict a human worker’s intention and assist human during assembly operations. In response to the requirement, this research proposes a new human-robot collaborative system design. The primary focus of the paper is to model product assembly tasks as a sequence of human motions. Existing human motion recognition techniques are applied to recognise the human motions. Hidden Markov model is used in the motion sequence to generate a motion transition probability matrix. Based on the result, human motion prediction becomes possible. The predicted human motions are evaluated and applied in task-level human-robot collaborative assembly.     

An obstacle avoidance algorithm for robot manipulators based on decision-making force

Obstacle avoidance is a significant skill not only for mobile robots but also for robot manipulators working in unstructured environments. Various algorithms have been proposed to solve off-line planning and on-line adaption problems. However, it is still not able to ensure safety and flexibility in complex scenarios. In this paper, a novel obstacle avoidance algorithm is proposed to improve the robustness and flexibility. The method contains three components: A closed-loop control system is used to filter the preplanned trajectory and ensure the smoothness and stability of the robot motion; the dynamic repulsion field is adopted to fulfill the robot with primitive obstacle avoidance capability; to mimic human’s complex obstacle avoidance behavior and instant decision-making mechanism, a parametrized decision-making force is introduced to optimize all the feasible motions. The algorithms were implemented in planar and spatial robot manipulators. The comparative results show the robot can not only track the task trajectory smoothly but also avoid obstacles in different configurations.     

Collision-free motion planning of dual-arm reconfigurable robots

Dual-arm reconfigurable robot is a new type of robot. It can adapt to different tasks by changing its different end-effector modules which have standard connectors. Especially, in fast and flexible assembly, it is very important to research the collision-free planning of dual-arm reconfigurable robots. It is to find a continuous, collision-free path in an environment containing obstacles. A new approach to the real-time collision-free motion planning of dual-arm reconfigurable robots is used in the paper. This method is based on configuration space (C-Space). The method of configuration space and the concepts reachable manifold and contact manifold are successfully applied to the collision-free motion planning of dual-arm robot. The complexity of dual-arm robots’ collision-free planning will reduce to a search in a dispersed C-Space. With this algorithm, a real-time optimum path is found. And when the start point and the end point of the dual-arm robot are specified, the algorithm will successfully get the collision-free path real time. A verification of this algorithm is made in the dual-arm horizontal articulated robot SCARATES, and the simulation and experiment ascertain that the algorithm is feasible and effective.     

多移动机器人实时最优运动规划

研究多移动机器人的实时运动规划问题，提出了运动规划问题的体系结构，并将最优控制与智能决策相结合，建立实时专家系统，在其支持下，使机器人在时间—能量最优情况下完成规划策略。仿真结果表明该方法具有很强的实时性。     

存在驱动饱和约束下的机器人时间最优实时运动规划研究

本文针对存在驱动饱和约束条件下的视觉伺服机器人,提出了一种时间最优实时运 动规划方法．该方法根据驱动电机的机械特性和运行状态,判断电机的驱动潜力,采用自适 应模糊逻辑规则实时地调节机器人运动速度,在不考虑奇异点等其它因素下实现时间最优运 动规划．文中采用该方法对二自由度平面机器人进行了仿真研究．     

面向全方位双足步行跟随的路径规划

双足步行机器人的足迹规划方法难以满足快速步行条件下的计算效率要求, 并存在步幅变化时运动失稳的风险, 2D环境下点机器人栅格规划则难于生成针对双足步行的高效路径.本文提出针对各向异性特征全方位步行机器人的一种路径规划策略, 将状态网格图方法拓展到全方位移动机器人领域, 基于三项基本假设及基元类型划分给出了系统的运动基元枚举及选择方法, 借助实时修正的增量式AD*搜索算法实现仿人机器人在动态环境下的快速路径规划, 通过合理选择启发函数及状态转移代价, 生成了平滑高效的路径, 为后续足迹生成的动力学优化提供了基础.计算机仿真证实了方法对各类环境的适应性, Robocup避障竞速挑战赛的成功表现证明了方法对于机器人样机部署的可行性及其提高步行效率的潜力.     

基于遗传算法的欠驱动机器人运动规划

研究了平面欠驱动机器人的非完整运动规划问题,建立欠驱动机器人系统动力学模型,提出了一种利用遗传算法(GA)解决欠驱动机器人运动规划的方法。引入部分稳定控制器的思想,提出基于能量最优的评价函数,利用遗传算法对评价函数进行离线优化,得到有关部分稳定控制器的切换规则。此方法可以推广到任意平面欠驱动机器人的规划问题上,以平面3R欠驱动机器人为研究对象进行了数值仿真,仿真结果验证了该方法的有效性。     

Inverse Kinematics Based Human Mimicking System using Skeletal Tracking Technology

Humanoid robots needs to have human-like motions and appearance in order to be well-accepted by humans. Mimicking is a fast and user-friendly way to teach them human-like motions. However, direct assignment of observed human motions to robot’s joints is not possible due to their physical differences. This paper presents a real-time inverse kinematics based human mimicking system to map human upper limbs motions to robot’s joints safely and smoothly. It considers both main definitions of motion similarity, between end-effector motions and between angular configurations. Microsoft Kinect sensor is used for natural perceiving of human motions. Additional constraints are proposed and solved in the projected null space of the Jacobian matrix. They consider not only the workspace and the valid motion ranges of the robot’s joints to avoid self-collisions, but also the similarity between the end-effector motions and the angular configurations to bring highly human-like motions to the robot. Performance of the proposed human mimicking system is quantitatively and qualitatively assessed and compared with the state-of-the-art methods in a human-robot interaction task using Nao humanoid robot. The results confirm applicability and ability of the proposed human mimicking system to properly mimic various human motions.