基于认知模型的室内移动服务机器人人机耦合协同作业机制

针对老年人和残疾人这类特殊用户群体与服务机器人构成的人机智能系统,提出了基于ACT-R(理性思维的适应性控制)认知架构模型的室内移动服务机器人人机耦合协同作业机制.基于ACT-R认知架构对人机一体化室内移动服务机器人人机协同作业系统进行了总体设计,利用简单自然的人机效应通道,设计了基于ACT-R认知架构的人机耦合界面;通过人-机-环境空间感知耦合,提出并建立了室内移动服务机器人人机一体化协同决策作业机制.最后在室内环境下进行移动服务机器人人机协同作业实验,系统安全高效地完成了作业任务,验证了该机制的有效性.     

穿戴式柔性下肢助力机器人发展现状及关键技术分析

穿戴式柔性下肢助力机器人技术在康复医疗、助老助残、生活起居等方面具有广阔的应用前景,具有质量轻、体积小、可穿戴性强、人机相容性好等优势.为促进我国柔性下肢助力机器人的研究和发展,总结国内外在该领域的研究进展,阐述了多种助力系统的组成、驱动原理和运动学信息等,分析了各助力系统的辅助力/矩传递规律及其助力效果.同时,对柔性下肢助力机器人所涉及的安全与可靠性、步态检测技术、驱动方式及控制策略、助力性能评估等关键技术进行了分析.在总结研究成果及分析关键技术的基础上,指出柔性下肢助力机器人今后的发展方向、研究思路和面临的挑战.对于柔性下肢助力机器人及相关的研究工作,具有一定的指导意义.     

满足不同交互任务的人机共融系统设计

人与机器人共同协作的灵活生产模式已经成为工业成产的迫切需求, 因此, 近年来人机共融系统方面的研究受到了越来越多关注. 设计并实现了一种满足不同交互任务的人机共融系统, 人体动作的估计和机器人的交互控制是其中的关键技术. 首先, 提出了一种基于多相机和惯性测量单元信息融合的人体姿态解算方法, 通过构造优化问题, 融合多相机下的2D关节检测信息和所佩戴的惯性测量单元测量信息, 对人体运动学姿态进行优化估计, 改善了单一传感器下, 姿态信息不全面以及对噪声敏感的问题, 提升了姿态估计的准确度. 其次, 结合机器人的运动学特性和人机交互的特点, 设计了基于目标点跟踪和模型预测控制的机器人控制策略, 使得机器人能够通过调整控制参数, 适应动态的环境和不同的交互需求, 同时保证机器人和操作人员的安全. 最后, 进行了动作跟随、物品传递、主动避障等人机交互实验, 实验结果表明了所设计的机器人交互系统在人机共融环境下的有效性和可靠性.     

基于人体手臂运动意图反馈的人机顺应协作

本文旨在通过人体手臂运动意图反馈来提高人与机器人协作的顺应性.首先通过自编码器和反向传播神经网络融合表面肌电信号和机器视觉信号,对人体手臂肘关节力矩和运动意图进行预测.然后,将人体手臂肘关节力矩反馈给机器人,以此预测手臂的运动意图并让机器人作出顺应响应,从而实现人机协作.最后,结合主观评价指标和客观评价指标,在人机协作锯木头实验中对比了3种不同协作模式的效果.与无运动意图反馈的人机协作相比,有反馈的人机协作下的协作交互力波动幅度减小了153.39 N,任务完成时间减少了19.25 s.实验结果表明,有人体手臂运动意图反馈的人机协作能够提高人与机器人协作的顺应程度,与人-人协作效果类似.     

人机智能技术及系统研究进展综述

人机智能系统是能够实现人机智能协作的机器人系统,近年来成为了机器人领域的研究热点,具有广泛的应用前景。针对人机智能系统技术和应用的国内外研究现状,从人机智能系统的关键技术和典型应用领域两方面进行了进展综述。重点综述了与传统机器人系统存在差异性的人机智能系统关键技术,从建模、交互、协同和优化4个方面的研究进展分别展开论述,对涉及的典型应用领域及典型人机智能系统进行总结,并对人机智能系统发展的挑战和未来研究方向进行了展望。     

面向静态稳定的外骨骼机器人阻抗自动化动态控制

针对外骨骼机器人阻抗不稳定、静态与动态的协调控制效果误差较大的问题，为了更好地模拟人的运动技能，该文提出一种面向静态稳定的外骨骼机器人阻抗自动化动态控制方法。分析外骨骼机器人在运动过程中的静态稳定性，计算不同姿态对应的稳定程度，采用sEMG信号组建新型人机技能传递系统，提取人的阻抗信息，将其映射到外骨骼机器人关节空间或者笛卡尔空间，通过力矩控制模式，进行力矩对应的刚度估计，自动化控制目标末端运动轨迹收敛过程。实验结果表明，阻抗控制器的运动轨迹和期望轨迹更加接近，阻抗控制系数均值误差为0.072，均方根误差为0.065，平均误差为0.044，能够更好地完成外骨骼机器人阻抗自动化动态控制。     

协作环境下的人机距离建模与最小距离迭代算法

为了使机器人适应更广泛、更复杂的任务需求,实现人与机器人的协作与共融,精确并实时地计算人与机器人之间的相对距离成为了不可避免的问题.针对该问题,提出了一种协作环境下的人机距离建模方法以及计算人机间最小距离的迭代算法.首先,利用机器人的3D模型构建机器人结构特征,并通过3D视觉传感器提取人体骨骼特征,将以上2组特征映射到同一坐标空间中建立协作环境下的人机距离模型.然后,在此模型的基础上迭代计算人与机器人间的最小距离并给出对应的空间位置点.最后以ABB公司的YuMi机器人为测试对象进行人机最小距离测量实验,实验结果表明该方法降低了建模难度、实现了计算的实时性,验证了该建模方法与迭代算法的有效性和实用性.     

基于差动机构的五连杆式人机合作机器人的动力学分析

介绍了一种基于差动机构的新型CVT连续变速传动机构, 通过CVT之间的不同耦合方法,构成了并联式和串联式人机合作机器人.分析了基于差动机构的人机合作机器人的工作原理,建立了差动机构的动力学模型.以五连杆式人机合作机器人为研究对象,分别建立了串联式和并联式人机合作机器人的动力学模型.􀁱 􀁽     

基于“人-机-环”信息流的机器人手术系统研究与展望

机器人手术系统凭借微创、精细、灵活、无震颤等优势,在多个外科领域不断得到普及应用.然而,现有的机器人手术系统尚未充分发挥人和机器各自的优势,在智能化交互方面的表现有待提高.因此,文中首先从系统科学的角度分析机器人手术系统中交互关系的发展,并从多方面提出当前人机交互的不足.然后,构建面向机器人手术系统的“人-机-环”信息流框架,以机器人辅助乳内动脉获取手术场景为例,梳理“人-机-环”各部分之间的复杂交互.最后,基于“人-机结合”理论,结合构建的“人-机-环”信息流框架,提出以“人机融合智能共进”为目标的新一代机器人手术系统的设计思路,为实现更安全高效的机器人微创外科手术目标提供借鉴.     

基于人机信息交互的助行外骨骼机器人技术进展

外骨骼机器人是集人体信息检测、机器人自动控制、神经工程等多学科知识于一身的高科技成果.本文简要介绍了外骨骼机器人研发技术现状和应用市场前景,分别从外骨骼动力驱动和运动测量技术角度剖析了支撑典型外骨骼机器人实现其运动辅助功能的主要技术基础,重点从神经信息交互角度出发,讨论了构建人机信息交互环路中的技术瓶颈,以及如何更为高效准确地获取人体运动意图.最后展望了其未来技术研发方向.     

Evolution of Plastic Control Networks

Evolutionary Robotics is a powerful method to generate efficient controllers with minimal human intervention, but its applicability to real-world problems remains a challenge because the method takes long time and it requires software simulations that do not necessarily transfer smoothly to physical robots. In this paper we describe a method that overcomes these limitations by evolving robots for the ability to adapt on-line in few seconds. Experiments show that this method require less generations and smaller populations to evolve, that evolved robots adapt in a few seconds to unpredictable change-including transfers from simulations to physical robots- and display non-trivial behaviors. Robots evolved with this method can be dispatched to other planets and to our homes where they will autonomously and quickly adapt to the specific properties of their environments if and when necessary.     

Advantages of flexible musculoskeletal robot structure in sensory acquisition

Morphological computation is the concept for which a well-designed hardware can bear part of the computational cost required for robot??s control and perception. So far, many musculoskeletal robots have been developed by taking inspiration from human??s one and shown superior motion performances. The use of pneumatic artificial muscles (PAMs) has been the key to realize these high performance. Additionally, PAMs have the possibility of being used as sensors for environmental information because they are flexible and backdrivable. In this research, we focus on clarifying how PAMs can contribute to morphological computation of robots driven by these actuators. In particular, we propose an analysis method based on transfer entropy and apply this method to the experimental data acquired by a musculoskeletal robot that opens a door.     

Evaluating the criteria for human resource for science and technology (HRST) based on an integrated fuzzy AHP and fuzzy DEMATEL approach

This study intends to use a combination of fuzzy Analytic Hierarchy Process (AHP) and fuzzy Decision-making Trial and Evaluation Laboratory (DEMATEL) method in human resource for science and technology (HRST). Specifically, this study first uses AHP to evaluate the weighting for each criterion and then use DEMATEL method to establish contextual relationships among those criteria. We find out Infrastructure might be more critical since it is a cause and will directly influence human resource for science and technology performance. For human resource for science and technology (HRST), improving Infrastructure might be a better choice for the long period of time. Moreover, Education, R&D Expenses and Immediate output are more important second-tier criteria than Value, Cooperation, Labor Market, Human Capital and Intermediate output. Therefore, the improvement should be started with Infrastructure, particularly on identification of the Education, R&D Expenses and Immediate output.     

一种簇结构下的多移动机器人通信方法

针对集中式通信方式的不足,提出一种基于簇的多移动机器人通信方法.通过周期性地发送识别消息,建立簇结构并形成机器人通信网络.采用层次结构管理机器人,可以有效地降低通信网络的负载.在多移动机器人团队上进行了簇结构重构和数据传输实验,结果证明该通信方法具有良好的实用性.     

Self-organized coordinated motion in groups of physically connected robots.

An important goal of collective robotics is the design of control systems that allow groups of robots to accomplish common tasks by coordinating without a centralized control. In this paper, we study how a group of physically assembled robots can display coherent behavior on the basis of a simple neural controller that has access only to local sensory information. This controller is synthesized through artificial evolution in a simulated environment in order to let the robots display coordinated-motion behaviors. The evolved controller proves to be robust enough to allow a smooth transfer from simulated to real robots. Additionally, it generalizes to new experimental conditions, such as different sizes/shapes of the group and/or different connection mechanisms. In all these conditions the performance of the neural controller in real robots is comparable to the one obtained in simulation.     

The leader-follower formation control of nonholonomic mobile robots

In this paper, the leader-waypoint-follower formation is constructed based on relative motion states of nonholonomic mobile robots. Since the robots’ velocities are constrained, we proposed a geometrical waypoint in cone method so that the follower robots move to their desired waypoints effectively. In order to form and maintain the formation of multi-robots, we combine stable tracking control method with receding horizon (RH) tracking control method. The stable tracking control method aims to make the robot’s state errors stable and the RH tracking control method guarantees that the convergence of the state errors tends toward zero efficiently. Based on the methods mentioned above, the mobile robots formation can be maintained in any trajectory such as a straight line, a circle or a sinusoid. The simulation results based on the proposed approaches show each follower robot can move to its waypoint efficiently. To validate the proposed methods, we do the experiments with nonholonomic robots using only limited on-board sensor information.     

一类有序化多移动机器人群集运动控制系统

群集运动控制(flocking control)是一种新型的多移动机器人运动协调控制, 目前的研究多集中于无leader模式下群集运动控制器的设计. 为此, 本文阐述了一类多移动机器人有序化群集运动系统控制方案及其性能评价方法. 首先, 在前人的研究基础上, 本文介绍了基于Agent的有序化编队控制机制; 然后, 运用非完整约束下移动机器人的动力学原理, 设计了由Agent到移动机器人的控制转化方法; 并进一步提出了基于“最小稳定时间”的群集运动分析法, 可对有序化群集运动系统进行分析; 最后, 运用仿真实例, 描述了多移动机器人有序化群集运动的控制及分析过程. 实验结果验证了此控制方案的有效性.     

Commentary on “Evaluating the criteria for human resource for science and technology (HRST) based on an integrated fuzzy AHP and fuzzy DEMATEL approach”

Chou et al. (Applied Soft Computing 12 (2012) 64–71) used combination of fuzzy AHP (Analytic Hierarchy Process) and fuzzy DEMATEL (Decision Making Trial and Evaluation Laboratory) in human resource for science and technology (HRST). For applying the fuzzy DEMATEL method there is a need to find the multiplicative inverse of a fuzzy matrix. In this paper, it is pointed out that the method, used by Chou et al. for evaluating the multiplicative inverse of a fuzzy matrix, is not valid. Hence, the method, proposed by Chou et al., is not valid. Furthermore, it is pointed out that this flaw can be resolved by using the existing method (International Journal of Computer Mathematics, 86 (8), (2009) 1433–1452) to find the multiplicative inverse of a fuzzy matrix.     

Inverse reinforcement learning-based time-dependent A* planner for human-aware robot navigation with local vision

In an environment where robots coexist with humans, mobile robots should be human-aware and comply with humans' behavioural norms so as to not disturb humans' personal space and activities. In this work, we propose an inverse reinforcement learning-based time-dependent A* planner for human-aware robot navigation with local vision. In this method, the planning process of time-dependent A* is regarded as a Markov decision process and the cost function of the time-dependent A* is learned using the inverse reinforcement learning via capturing humans' demonstration trajectories. With this method, a robot can plan a path that complies with humans' behaviour patterns and the robot's kinematics. When constructing feature vectors of the cost function, considering the local vision characteristics, we propose a visual coverage feature for enabling robots to learn from how humans move in a limited visual field. The effectiveness of the proposed method has been validated by experiments in real-world scenarios: using this approach robots can effectively mimic human motion patterns when avoiding pedestrians; furthermore, in a limited visual field, robots can learn to choose a path that enables them to have the larger visual coverage which shows a better navigation performance.     

Analyzing the Multiple-target-multiple-agent Scenario Using Optimal Assignment Algorithms

This work considers the problem of maximum utilization of a set of mobile robots with limited sensor-range capabilities and limited travel distances. The robots are initially in random positions. A set of robots properly guards or covers a region if every point within the region is within the effective sensor range of at least one vehicle. We wish to move the vehicles into surveillance positions so as to guard or cover a region, while minimizing the maximum distance traveled by any vehicle. This problem can be formulated as an assignment problem, in which we must optimally decide which robot to assign to which slot of a desired matrix of grid points. The cost function is the maximum distance traveled by any robot. Assignment problems can be solved very efficiently. Solution times for one hundred robots took only seconds on a Silicon Graphics Crimson workstation. The initial positions of all the robots can be sampled by a central base station and their newly assigned positions communicated back to the robots. Alternatively, the robots can establish their own coordinate system with the origin fixed at one of the robots and orientation determined by the compass bearing of another robot relative to this robot. This paper presents example solutions to the multiple-target-multiple-agent scenario using a matching algorithm. Two separate cases with one hundred agents in each were analyzed using this method. We have found these mobile robot problems to be a very interesting application of optimal assignment algorithms, and we expect this to be a fruitful area for future research.