类人足球机器人决策系统的设计

类人机器人足球比赛是机器人足球比赛的最高赛事.类人足球机器人的决策系统是基于独立视觉的自主决策系统,很大程度上决定着比赛的胜败.介绍了自主研发的类人足球机器人决策系统的架构及实现方法,并在此基础上运用有限状态机理论,对单个机器人的自主进攻策略进行了详细分析和研究,真实环境中的实验及比赛结果证明了其有效性.该决策系统的设计及研究工作对基于自主决策的多智能体协作以及服务性机器人决策系统的研究都具有重要的价值.     

A Real-Time Hybrid Architecture for Biped Humanoids with Active Vision Mechanisms

A real-time hybrid control architecture for biped humanoid robots is proposed. The architecture is modular and hierarchical. The main robot’s functionalities are organized in four parallel modules: perception, actuation, world-modeling, and hybrid control. Hybrid control is divided in three behavior-based hierarchical layers: the planning layer, the deliberative layer, and the reactive layer, which work in parallel and have very different response speeds and planning capabilities. The architecture allows: (1) the coordination of multiple robots and the execution of group behaviors without disturbing the robot’s reactivity and responsivity, which is very relevant for biped humanoid robots whose gait control requires real-time processing. (2) The straightforward management of the robot’s resources using resource multiplexers. (3) The integration of active vision mechanisms in the reactive layer under control of behavior-dependant value functions from the deliberative layer. This adds flexibility in the implementation of complex functionalities, such as the ones required for playing soccer in robot teams. The architecture is validated using simulated and real Nao humanoid robots. Passive and active behaviors are tested in simulated and real robot soccer setups. In addition, the ability to execute group behaviors in real- time is tested in international robot soccer competitions.     

不同个性的情感机器人表情研究

在情感机器人研究中,不同个性的面部表情是情感机器人增强真实感的重要基础。为实现情感机器人更加丰富细腻的表情,将人类的个性特征引入情感机器人,分析个性理论和情感模型理论,得知不同个性机器人的情感强度。结合面部动作编码系统中面部表情与机器人控制点之间的映射关系,得到情感机器人不同个性的基本表情实现方法。利用Solidworks建立情感机器人脸部模型,在ANSYS工程软件中将SHFR-Ⅲ情感机器人脸部模型设置为弹性体,通过有限元仿真计算方法,对表情的有限元仿真方法进行了探究,得到实现SHFR-Ⅲ不同个性基本表情的控制区域载荷大小和仿真结果。最后,根据仿真结果,进行SHFR-Ⅲ情感机器人不同个性的表情动作实验。实验结果表明,有限元表情仿真可以指导SHFR-Ⅲ情感机器人实现近似人类的不同个性的基本面部表情。     

Policy gradient learning for a humanoid soccer robot

In humanoid robotic soccer, many factors, both at low-level (e.g., vision and motion control) and at high-level (e.g., behaviors and game strategies), determine the quality of the robot performance. In particular, the speed of individual robots, the precision of the trajectory, and the stability of the walking gaits, have a high impact on the success of a team. Consequently, humanoid soccer robots require fine tuning, especially for the basic behaviors. In recent years, machine learning techniques have been used to find optimal parameter sets for various humanoid robot behaviors. However, a drawback of learning techniques is time consumption: a practical learning method for robotic applications must be effective with a small amount of data. In this article, we compare two learning methods for humanoid walking gaits based on the Policy Gradient algorithm. We demonstrate that an extension of the classic Policy Gradient algorithm that takes into account parameter relevance allows for better solutions when only a few experiments are available. The results of our experimental work show the effectiveness of the policy gradient learning method, as well as its higher convergence rate, when the relevance of parameters is taken into account during learning.     

霍夫空间中多足球机器人协作目标定位算法

针对嵌入式仿人足球机器人提出一种霍夫空间中的多机器人协作目标定位算法。机器人利用实验场地中的标志物采用基于三角几何定位方法进行自定位,把机器人多连杆模型进行简化,通过坐标系位姿变换把图像坐标系转换到世界坐标系中,实现机器人目标定位;在多机器人之间建立ZigBee无线传感器网络进行通信,把多个机器人定位的坐标点进行霍夫变换,在霍夫空间中进行最小二乘法线性拟合,获取最优参数,然后融合改进后的粒子滤波实现对目标小球的跟踪;最后在21自由度的仿人足球机器人上进行仿真和实验。数据结果表明,这种多机器人协作的定位算法的精度提高了约48%,在满足实时性的前提下,对目标的跟踪效果也得到了改善。     

Soccer playing humanoid robots: Processing architecture,gait generation and vision system

Research on humanoid robotics in Mechatronics and Automation (MA) Laboratory, Electrical and Computer Engineering (ECE), National University of Singapore (NUS) was started at the beginning of this decade. Various research prototypes for humanoid robots have been designed and are going through evolution over these years. These humanoids have been successfully participating in various robotic soccer competitions. In this paper, three major research and development aspects of the above humanoid research are discussed. The paper focuses on various practical and theoretical considerations involved in processing architecture, gait generation and vision systems.     

Highly Precise Dynamic Simulation Environment for Humanoid Robots

In this paper we present a simulation environment for humanoid robots with a precise and efficient method of handling ground contact, and experiments empirically validating the simulator. Highly accurate dynamic simulation is an essential tool for research and development in humanoid robotics, and a simulator should ideally provide a transparent interface with pathways for control and sensing information identical to those of the actual robot(s) it models. We identified ground contact as the chief source of divergence from reality in work to date and have tackled this problem by developing an algorithm for resolving ground contact for humanoid robots. Our objective was to produce an algorithm that is accurate, efficient and easy to implement. The algorithm is general with respect to the complexity of the foot model; is based on empirically measurable characteristics of the foot–ground interaction, i.e., friction, which we have obtained using experiments described; provides an exact implementation of the Coulomb friction model (avoiding polyhedral approximation of the friction cone); runs in real-time; is also amenable to a straightforward accuracy–speed trade-off; and is relatively easy to implement as a constraint selection method. The simulation environment embodies generality, and we have applied it to two different humanoid robots, Hoap-2 and CB. We present experiments comparing the results of simulation with identical motions performed by real robots, and comparing the full contact resolution algorithm, the modification trading accuracy for computational speed and a penalty-based method.     

Adequate motion simulation and collision detection for soccer playing humanoid robots

In this paper a humanoid robot simulator based on the multi-robot simulation framework (MuRoSimF) is presented. Among the unique features of this simulator is the scalability in the level of physical detail in both the robot’s motion and sensing systems. It facilitates the development of control software for humanoid robots which is demonstrated for several scenarios from the RoboCup Humanoid Robot League.Different requirements exist for a humanoid robot simulator. E.g., testing of algorithms for motion control and postural stability require high fidelity of physical motion properties whereas testing of behavior control and role distribution for a robot team requires only a moderate level of detail for real-time simulation of multiple robots. To meet such very different requirements often different simulators are used which makes it necessary to model a robot multiple times and to integrate different simulations with high-level robot control software.MuRoSimF provides the capability of exchanging the simulation algorithms used for each robot transparently, thus allowing a trade-off between computational performance and fidelity of the simulation. It is therefore possible to choose different simulation algorithms which are adequate for the needs of a given simulation experiment, for example, motion simulation of humanoid robots based on kinematical, simplified dynamics or full multi-body system dynamics algorithms. In this paper also the sensor simulation capabilities of MuRoSimF are revised. The methods for motion simulation and collision detection and handling are presented in detail including an algorithm which allows the real-time simulation of the full dynamics of a 21 DOF humanoid robot. Merits and drawbacks of the different algorithms are discussed in the light of different simulation purposes. The simulator performance is measured and illustrated in various examples, including comparison with experiments of a physical humanoid robot.     

A new paradigm of humanoid robot motion programming based on touch interpretation

Most humanoid soccer robot teams design the basic movements of their robots, like walking and kicking, off-line and manually. Once these motions are considered satisfactory, they are stored in the robot’s memory and played according to a high level behavioral strategy. Much time is spent in the development of the movements, and despite the significant progress made in humanoid soccer robots, the interfaces employed for the development of motions are still quite primitive. In order to accelerate development, an intuitive instruction method is desired. We propose the development of robot motions through physical interaction. In this paper we propose a ”teaching by touching” approach; the human operator teaches a motion by directly touching the robot’s body parts like a dance instructor. Teaching by directly touching is intuitive for instructors. However, the robot needs to interpret the instructor’s intention since tactile communication can be ambiguous. This paper presents a method to learn the interpretation of the touch meaning and investigates, through experiments, a general (shared among different users) and intuitive touch manner.     

Kick it with elasticity: Safety and performance in human–robot soccer

The RoboCup community has one definite goal [H. Kitano, M. Asada, RoboCup humanoid challenge: That’s one small step for a robot, one giant leap for mankind, in: IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, IROS1998, Victoria, pp. 419–424, 1998]: winning against the human world soccer champion team by the year 2050. This implies real tackles and fouls between humans and robots, rising safety concerns for the robots and even more important for the human players. Nowadays, similar questions are discussed in the field of physical human–robot interaction (pHRI), but mainly in the context of industrial and service robotics applications.The first part of our paper is an attempt for a pHRI view on human–robot soccer. We take scenes from real soccer matches and discuss what could have happened if one of the teams consisted of robots instead of humans. The most important result is that elastic joints are needed to reduce the impact during collisions. The second and third part consider conversely, how the robot can handle the impact of kicking the ball and how it can reach the velocity of human-level soccer. Again joint elasticity is the key point.Overall, the paper analyzes a vision far ahead. However, all our conclusions are based on concrete simulations, experiments, derivations, or findings from sports science, forensics, and pHRI.     

A comparison of learning strategies for biologically constrained development of gaze control on an iCub robot

Gaze control requires the coordination of movements of both eyes and head to fixate on a target. We present a biologically constrained architecture for gaze control and show how the relationships between the coupled sensorimotor systems can be learnt autonomously from scratch, allowing for adaptation as the system grows or changes. Infant studies suggest developmental learning strategies, which can be applied to sensorimotor learning in humanoid robots. We examine two strategies (sequential and synchronous) for the learning of eye and head coupled mappings, and give results from implementations on an iCub robot. The results show that the developmental approach can give fast, cumulative, on-line learning of coupled sensorimotor systems.     

Statistical Learning for Humanoid Robots

The complexity of the kinematic and dynamic structure of humanoid robots make conventional analytical approaches to control increasingly unsuitable for such systems. Learning techniques offer a possible way to aid controller design if insufficient analytical knowledge is available, and learning approaches seem mandatory when humanoid systems are supposed to become completely autonomous. While recent research in neural networks and statistical learning has focused mostly on learning from finite data sets without stringent constraints on computational efficiency, learning for humanoid robots requires a different setting, characterized by the need for real-time learning performance from an essentially infinite stream of incrementally arriving data. This paper demonstrates how even high-dimensional learning problems of this kind can successfully be dealt with by techniques from nonparametric regression and locally weighted learning. As an example, we describe the application of one of the most advanced of such algorithms, Locally Weighted Projection Regression (LWPR), to the on-line learning of three problems in humanoid motor control: the learning of inverse dynamics models for model-based control, the learning of inverse kinematics of redundant manipulators, and the learning of oculomotor reflexes. All these examples demonstrate fast, i.e., within seconds or minutes, learning convergence with highly accurate final peformance. We conclude that real-time learning for complex motor system like humanoid robots is possible with appropriately tailored algorithms, such that increasingly autonomous robots with massive learning abilities should be achievable in the near future.     

足球机器人系统仿真中的碰撞研究

总结了近年来足球机器人碰撞的研究成果,指出当前一些碰撞模型的不足,进而指出了数字仿真系统的离散特性对足球机器人的碰撞性质的影响. 详细地分析了足球机器人的运动碰撞情况. 最后提出了足球机器人的碰撞模型并给出了具体的算法. 实践证明该算法是有效的．     

Towards cooperation of heterogeneous,autonomous robots: A case study of humanoid and wheeled robots

In this paper a case study of the cooperation of a strongly heterogeneous autonomous robot team, composed of a highly articulated humanoid robot and a wheeled robot with largely complementing and some redundant abilities is presented. By combining strongly heterogeneous robots the diversity of achievable tasks increases as the variety of sensing and motion abilities of the robot system is extended, compared to a usually considered team of homogeneous robots. A number of methodologies and technologies required in order to achieve the long-term goal of cooperation of heterogeneous autonomous robots are discussed, including modeling tasks and robot abilities, task assignment and redistribution, robot behavior modeling and programming, robot middleware and robot simulation. Example solutions and their application to the cooperation of autonomous wheeled and humanoid robots are presented in this case study. The scenario describes a tightly coupled cooperative task, where the humanoid robot and the wheeled robot track a moving ball, which is to be approached and kicked by the humanoid robot into a goal. The task can be fulfilled successfully by combining the abilities of both robots.     

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.     

Constrained Analytical Trajectory Filter for stabilizing humanoid robot motions

Mimicking human motion with a humanoid robot is essential for allowing humanoid robots to be used in service applications. Simply creating motions without considerations for balance and stability or directly copying motion from a human using motion capture and implementing it on a humanoid robot may not be successful because of the difference in physical properties between the human and the humanoid robot, which may cause instability and make it fall. Using the Zero Moment Point as the stability criteria, this work proposes a Constrained Analytical Trajectory Filter as part of an Analytical Motion Filter, which stabilizes a reference motion that can come from human motion capture data, kinematic synthesis, or animation software. The resulting solutions used in the Constrained Analytical Trajectory Filter provide insight into the complex interactions of motion and stability. The solutions were verified in simulation and with hardware, showing that the analytical filter can be successfully applied for stabilizing reference motions for humanoid robots which may be unstable otherwise.     

Human and Humanoid Dynamics

The questions when and why one needs to use mathematical models, and especially the models of dynamics, represent a still unresolved issue. A general answer would be that dynamic modeling is needed as a tool when designing structure of the system and its control unit. In this case we talk about simulation. The other application is in on-line control of the system – the so-called dynamic control. While for simulation one should generally use the best available model, the control can be based on a reduced dynamics, depending on a particular task. This article considers humans and humanoid robots and addresses a rather important question: what are dynamic effects that one should take care of when modeling and simulating a human or a humanoid? The article suggests the key topics for work and tries to justify them. As a final result a General Human/Humanoid-Dynamics Simulator is seen.     

The Effect of a Humanoid Robot’s Emotional Behaviors on Users’ Emotional Responses: Evidence from Pupillometry and Electroencephalography Measures

ABSTRACT

The design of humanoid robots’ emotional behaviors has attracted many scholars’ attention. However, users’ emotional responses to humanoid robots’ emotional behaviors which differ from robots’ traditional behaviors remain well understood. This study aims to investigate the effect of a humanoid robot’s emotional behaviors on users’ emotional responses using subjective reporting, pupillometry, and electroencephalography. Five categories of the humanoid robot’s emotional behaviors expressing joy, fear, neutral, sadness, or anger were designed, selected, and presented to users. Results show that users have a significant positive emotional response to the humanoid robot’s joy behavior and a significant negative emotional response to the humanoid robot’s sadness behavior, indicated by the metrics of reported valence and arousal, pupil diameter, frontal middle relative theta power, and frontal alpha asymmetry score. The results suggest that humanoid robot’s emotional behaviors can evocate users’ significant emotional response. The evocation might relate to the recognition of these emotional behaviors. In addition, the study provides a multimodal physiological method of evaluating users’ emotional responses to the humanoid robot’s emotional behaviors.     

Learning to fall: Designing low damage fall sequences for humanoid soccer robots

A methodology for the analysis and design of fall sequences of robots that minimize joint/articulation injuries, and the damage of valuable body parts is proposed. These fall sequences can be activated/triggered by the robot in case of a detected unintentional fall or an intentional fall, which are common events in humanoid soccer environments. The methodology is human-based and requires the use of a realistic simulator as development tool. The obtained results show that fall sequences designed using the proposed method produce less damage than standard, uncontrolled falls.     

仿人跑步机器人机构与步态规划综述

阐述了仿人跑步机器人研究的必要性,介绍了几款国外典型的仿人机器人机构设计和驱动系统设计,对仿人跑步机器人的步态规划问题进行了综述,并指出仿人跑步机器人领域的研究难点和未来研究方向。