Human Interaction Dynamics for Its Use in Mobile Robotics:Impedance Control for Leader-follower Formation

A complete characterization of the behavior in human-robot interactions(HRI) includes both: the behavioral dynamics and the control laws that characterize how the behavior is regulated with the perception data. In this way, this work proposes a leader-follower coordinate control based on an impedance control that allows to establish a dynamic relation between social forces and motion error. For this, a scheme is presented to identify the impedance based on fictitious social forces, which are described by distance-based potential fields.As part of the validation procedure, we present an experimental comparison to select the better of two different fictitious force structures. The criteria are determined by two qualities: least impedance errors during the validation procedure and least parameter variance during the recursive estimation procedure.Finally, with the best fictitious force and its identified impedance,an impedance control is designed for a mobile robot Pioneer 3AT,which is programmed to follow a human in a structured scenario.According to results, and under the hypothesis that moving like humans will be acceptable by humans, it is believed that the proposed control improves the social acceptance of the robot for this kind of interaction.

A Facial Expression Emotion Recognition Based Human-robot Interaction System

A facial expression emotion recognition based human-robot interaction(FEER-HRI) system is proposed, for which a four-layer system framework is designed. The FEERHRI system enables the robots not only to recognize human emotions, but also to generate facial expression for adapting to human emotions. A facial emotion recognition method based on2D-Gabor, uniform local binary pattern(LBP) operator, and multiclass extreme learning machine(ELM) classifier is presented,which is applied to real-time facial expression recognition for robots. Facial expressions of robots are represented by simple cartoon symbols and displayed by a LED screen equipped in the robots, which can be easily understood by human. Four scenarios,i.e., guiding, entertainment, home service and scene simulation are performed in the human-robot interaction experiment, in which smooth communication is realized by facial expression recognition of humans and facial expression generation of robots within 2 seconds. As a few prospective applications, the FEERHRI system can be applied in home service, smart home, safe driving, and so on.

Interpreting and Extracting Open Knowledge for Human-Robot Interaction

A more natural way for non-expert users to express their tasks in an open-ended set is to use natural language. In this case,a human-centered intelligent agent/robot is required to be able to understand and generate plans for these naturally expressed tasks. For this purpose, it is a good way to enhance intelligent robot's abilities by utilizing open knowledge extracted from the web, instead of hand-coded knowledge. A key challenge of utilizing open knowledge lies in the semantic interpretation of the open knowledge organized in multiple modes, which can be unstructured or semi-structured, before one can use it.Previous approaches used a limited lexicon to employ combinatory categorial grammar(CCG) as the underlying formalism for semantic parsing over sentences. Here, we propose a more effective learning method to interpret semi-structured user instructions. Moreover, we present a new heuristic method to recover missing semantic information from the context of an instruction. Experiments showed that the proposed approach renders significant performance improvement compared to the baseline methods and the recovering method is promising.

Information perception and feedback mechanism and key techniques of multi-modality human-robot interaction for service robots

With the increasing of the elderly population and the growing hearth care cost, the role of service robots in aiding the disabled and the elderly is becoming important. Many researchers in the world have paid much attention to heaRthcare robots and rehabilitation robots. To get natural and harmonious communication between the user and a service robot, the information perception/feedback ability, and interaction ability for service robots become more important in many key issues.

Augmented Virtual Stiffness Rendering of a Cable-driven SEA for Human-Robot Interaction

Human-robot interaction(HRI) is fundamental for human-centered robotics, and has been attracting intensive research for more than a decade. The series elastic actuator(SEA) provides inherent compliance, safety and further benefits for HRI, but the introduced elastic element also brings control difficulties. In this paper, we address the stiffness rendering problem for a cable-driven SEA system, to achieve either low stiffness for good transparency or high stiffness bigger than the physical spring constant, and to assess the rendering accuracy with quantified metrics. By taking a velocity-sourced model of the motor, a cascaded velocity-torque-impedance control structure is established. To achieve high fidelity torque control, the 2-DOF(degree of freedom) stabilizing control method together with a compensator has been used to handle the competing requirements on tracking performance, noise and disturbance rejection,and energy optimization in the cable-driven SEA system. The conventional passivity requirement for HRI usually leads to a conservative design of the impedance controller, and the rendered stiffness cannot go higher than the physical spring constant. By adding a phase-lead compensator into the impedance controller,the stiffness rendering capability was augmented with guaranteed relaxed passivity. Extensive simulations and experiments have been performed, and the virtual stiffness has been rendered in the extended range of 0.1 to 2.0 times of the physical spring constant with guaranteed relaxed passivity for physical humanrobot interaction below 5 Hz. Quantified metrics also verified good rendering accuracy.

Teaching the User By Learning From the User:Personalizing Movement Control in Physical Human-robot Interaction

This paper proposes a novel approach for physical human-robot interactions(pHRI), where a robot provides guidance forces to a user based on the user performance. This framework tunes the forces in regards to behavior of each user in coping with different tasks, where lower performance results in higher intervention from the robot. This personalized physical human-robot interaction(p2HRI) method incorporates adaptive modeling of the interaction between the human and the robot as well as learning from demonstration(LfD) techniques to adapt to the users' performance. This approach is based on model predictive control where the system optimizes the rendered forces by predicting the performance of the user. Moreover, continuous learning of the user behavior is added so that the models and personalized considerations are updated based on the change of user performance over time. Applying this framework to a field such as haptic guidance for skill improvement, allows a more personalized learning experience where the interaction between the robot as the intelligent tutor and the student as the user,is better adjusted based on the skill level of the individual and their gradual improvement. The results suggest that the precision of the model of the interaction is improved using this proposed method,and the addition of the considered personalized factors to a more adaptive strategy for rendering of guidance forces.

Connecting Language with Perception and Action for Human-robot Interaction

To build robots that engage in intuitive communication with people by natural language, we are developing a new knowledge representation called conceptual network model. The conceptual network connects natural language concepts with visual perception including color perception, shape perception, size perception, and spatial perception. In the implementation of spatial perception, we present a computational model based on spatial template theory to interpret qualitative spatial expressions. Based on the conceptual network model, our mobile robot can understand user's instructions and recognize the object referred to by the user and perform appropriate action. Experimental results show our approach promising.

Robotic Etiquette:Socially Acceptable Navigation of Service Robots with Human Motion Pattern Learning and Prediction

Nonverbal and noncontact behaviors play a significant role in allowing service robots to structure their interactions withhumans.In this paper, a novel human-mimic mechanism of robot’s navigational skills was proposed for developing sociallyacceptable robotic etiquette.Based on the sociological and physiological concerns of interpersonal interactions in movement,several criteria in navigation were represented by constraints and incorporated into a unified probabilistic cost grid for safemotion planning and control, followed by an emphasis on the prediction of the human’s movement for adjusting the robot’spre-collision navigational strategy.The human motion prediction utilizes a clustering-based algorithm for modeling humans’indoor motion patterns as well as the combination of the long-term and short-term tendency prediction that takes into accountthe uncertainties of both velocity and heading direction.Both simulation and real-world experiments verified the effectivenessand reliability of the method to ensure human’s safety and comfort in navigation.A statistical user trials study was also given tovalidate the users’favorable views of the human-friendly navigational behavior.

Cyber-Physical-Social System Between a Humanoid Robot and a Virtual Human Through a Shared Platform for Adaptive Agent Ecology

Two artificial agents(a humanoid robot and a virtual human) are enriched with various similar intelligence,autonomy, functionalities and interaction modalities. The agents are integrated in the form of a cyber-physical-social system(CPSS) through a shared communication platform to create a social ecology. In the ecology, the agents collaborate(assist each other) to perform a real-world task(search for a hidden object)for the benefits of humans. A robot-virtual human bilateral trust model is derived and a real-time trust measurement method is developed. The role of taking initiative in the collaboration is switched between the agents following a finite state machine model triggered by bilateral trust, which results in a mixedinitiative collaboration. A scheme is developed to evaluate the performance of the agents in the ecology through the CPSS.The results show that the robot and the virtual human perform satisfactorily in the collaboration through the CPSS. The results thus prove the effectiveness of the real-world ecology between artificial agents of heterogeneous realities through a shared platform based on trust-triggered mixed-initiatives. The results can help develop adaptive social ecology comprising intelligent agents of heterogeneous realities to assist humans in various tasks through collaboration between the agents in the form of a CPSS.

A Survey of Human-centered Intelligent Robots:Issues and Challenges

Intelligent techniques foster the dissemination of new discoveries and novel technologies that advance the ability of robots to assist and support humans. The human-centered intelligent robot has become an important research field that spans all of the robot capabilities including navigation, intelligent control, pattern recognition and human-robot interaction. This paper focuses on the recent achievements and presents a survey of existing works on human-centered robots. Furthermore, we provide a comprehensive survey of the recent development of the human-centered intelligent robot and discuss the issues and challenges in the field.

Development of a Musical-based Interaction System for the Waseda Flutist Robot-Implementation of a Real-time Vision Interface Based on the Particle Filter Algorithm

The aim of this paper is to create an interface for human-robot interaction.Specifically,musical performance parameters(i.e.vibrato expression)of the Waseda Flutist Robot No.4 Refined IV(WF-4RIV)are to be manipulated.This research focused on enabling the WF-4RIV to interact with human players(musicians)in a natural way.In this paper,as the first approach,a vision processing algorithm,which is able to track the 3D-orientation and position of a musical instrument,was developed.In particular,the robot acquires image data through two cameras attached to its head.Using color histogram matching and a particle filter,the position of the musician's hands on the instrument are tracked.Analysis of this data determines orientation and location of the instrument.These parameters are mapped to manipulate the musical expression of the WF-4RIV,more specifically sound vibrato and volume values.The authors present preliminary experiments to determine if the robot may dynamically change musical parameters while interacting with a human player(i.e.vibrato etc.).From the experimental results,they may confirm the feasibility of the interaction during the performance,although further research must be carried out to consider the physical constraints of the flutist robot.

Human Detection by Robotic Urban Search and Rescue Using Image Processing and Neural Networks

This paper proposes a new approach for detecting human survivors in destructed environments using an autonomous robot. The proposed system uses a passive infrared sensor to detect the existence of living humans and a low-cost camera to acquire snapshots of the scene. The images are fed into a feed-forward neural network, trained to detect the existence of a human body or part of it within an obstructed environment. This approach requires a relatively small number of images to be acquired and processed during the rescue operation, which considerably reduces the cost of image processing, data transmission, and power consumption. The results of the conducted experiments demonstrated that this system has the potential to achieve high performance in detecting living humans in obstructed environments relatively quickly and cost-effectively. The detection accuracy ranged between 79% and 91% depending on a number of factors such as the body position, the light intensity, and the relative color matching between the body and the surrounding environment.

基于模糊控制的上肢康复机器人变导纳控制

传统固定参数的导纳模型无法对上肢康复机器人的柔顺性实时调节,而现有变参数导纳模型需要结合实际康复需求进行改善。为此,以自主研发的串并混联的末端牵引式上肢康复机器人为研究对象,结合模糊控制与导纳控制,提出了一种面向实际康复需求的新型变导纳控制策略,制定了4条有利于康复效率与安全的模糊规则。该策略利用交互力误差及其变化率作为模糊控制输入,实时改变导纳模型的参数,实现柔顺性自主调节。仿真和实验结果验证了所提出的变导纳模型控制策略可行性和所制定的4条模糊规则的有效性。在患者可适应训练强度的场景中,相较于固定导纳模型,变导纳模型追踪给定路径时产生的冗余路径最多可降低56.13%,提高了康复训练效率;当康复运动超出患者可承受范围时,变导纳模型可提前0.5 s改变追踪路径,提高了康复的安全性。

智能人机交互在有人太空探索中的应用与展望

近年来,空间机器人技术发展迅速,国内外已有诸多空间站机器人、深空探测机器人的应用实例。未来有人太空探索过程当中,人与众多智能机器人协同开展任务将成为必然趋势。对国内外空间协作机器人应用实例进行了总结,在分析人机协作机制的基础上,提出了空间人机交互的关键技术,包含人体检测与跟踪、手势识别与分类、动作识别与意图预测、低重力环境下的人机协同控制等,对上述关键技术的难点与研究进展进行了分析综述,并对后续的发展方向进行了展望。

基于视觉人体姿态识别的人机交互机器人课程教学研究

为了解决机器人学教材和课程内容更新滞后、人工智能等前沿领域知识匮乏、缺乏学科交叉知识等问题,本文提出了一种基于视觉人体姿态识别的人机交互教学方法,旨在帮助学生深入接触机器人学领域的前沿知识,拓宽跨学科背景,深刻理解运动控制原理。在激发学生学习兴趣的同时,协助学生更加有效地掌握机器人学领域的知识。

服务机器人人机交互社会力模型研究

服务机器人在人机交互中应遵守人类社会规则,应用于人与人之间行为动态关系的传统社会力模型不能完全适用于机器人交互设计,机器人和人之间的动态交互塑造了全新的交互行为范式。通过实验测量室内交互场景下人与服务机器人的交互距离,提出人机交互过程中情感“亲密力”的存在,实验结果发现人与机器人静态交互受亲密力和交互力影响、动态交互受亲密力和社会文化力影响、从动到静交互过程受亲密力和速度力影响,最终构建服务机器人人机交互社会力模型,为提升服务机器人人机交互体验提供理论依据。

人机接触运动交互中人体舒适感知的测量方法

目的完善人机接触运动交互中用户的舒适度测量方法,提高机器人作业的智能感知能力。方法基于机器人按摩作业平台,收集测试者人机接触中皮肤所受的力与变形等作业数据,以及主观触觉感知评价。建立以疼痛感、牵扯感和灼热感为基本维度的三维舒适度感知空间,通过反向传播神经网络建立客观测量数据与人体触觉感知的映射关系;采用支持向量机算法对人体触觉感知进行舒适、不舒适可作业和不舒适需停机分类。结果实验结果显示,触觉感知预测模型和状态分类模型,能有效实现从作业特征信息到触觉感知再到人机作业状态的整体辨识。结论该方法对人机交互过程中的作业特征和人体主观触觉感知进行联系和模式识别,为机器人安全、舒适的作业规划和在线控制提供了有效依据。

基于MR混合现实的足球机器人目标跟踪仿真

机器人跟踪足球时,由于目标具有较强的运动随机性,且较小的目标尺度增大了跟踪框的自适应跟踪难度,目标跟踪时会将很多背景信息包含在内,导致目标模型的更新错误。为此,提出基于MR混合现实的足球机器人目标跟踪方法。构建MR混合现实平台,令足球机器人在虚拟现实场景下完成人机交互,在保证目标跟踪安全性的同时提升目标跟踪性能;采用帧差法获取目标模型,利用投影法对二值图像投影,并对差分图像滤波,以此获取足球机器人在目标模板区域及候选目标区域中的直方图。基于此,更新足球机器人目标模型,实现基于MR混合现实的足球机器人目标跟踪。实验结果表明,当跟踪误差阈值为0.8mm时,研究方法的目标跟踪准确率和成功率均可达95%以上,平均耗时为55.17ms。当足球目标出现遮挡问题时,该方法仍能完成高精度跟踪。

基于虚拟现实的立管横焊机器人辅助焊接工艺设计与实现

焊接工艺参数的选择对焊接质量有重要影响。针对立管J型铺设窄坡口横焊熔池下淌特性,提出基于横焊焊道厚度及热输入恒定策略的焊接工艺参数匹配算法。基于Unity3D开发平台和工艺参数匹配算法,通过C#开发了立管横焊CAPP模块,扩展了虚拟现实人机交互系统的功能模块;开展立管横焊计算机辅助焊接工艺实验,焊后焊缝成形良好,无焊接缺陷。实验结果表明:CAPP模块可有效获取匹配的焊接工艺参数,能缩短焊接工艺开发周期,降低工艺开发成本,有效提升系统的智能化水平。

基于主动交互的服务机器人用户体验设计研究

机器人主动交互是未来人机智能协同的重要形式之一,服务机器人的主动交互行为模式与用户体验密切相关。聚焦在用户首次接触公共空间服务机器人时,机器人展示不同的行为模式对用户体验的影响,总结该情景下的用户体验设计策略。用眼动仪收集用户对机器人主动行为的关注度,通过热力图分布结合用户体验量表评估用户体验意愿,从吸引力、同理心和信任感几个维度进行研究,发现服务机器人的主动交互过程中,运动速度、情感表达程度、互动距离,以及对于人类行为模式的抽象模仿,对用户体验有较大影响。经过实验验证,优化上述参数的交互行为设计可提高服务机器人的用户使用体验,并吸引用户进一步互动。