Hierarchical behavior categorization using correlation based adaptive resonance theory

This paper introduces a new model for robot behavior categorization. Correlation based adaptive resonance theory (CobART) networks are integrated hierarchically in order to develop an adequate categorization, and to elicit various behaviors performed by the robot. The proposed model is developed by adding a second layer CobART network which receives first layer CobART network categories as an input, and back-propagates the matching information to the first layer networks. The first layer CobART networks categorize self-behavior data of a robot or an object in the environment while the second layer CobART network categorizes the robot's behavior with respect to its effect on the object. Experiments show that the proposed model generates reasonable categorization of behaviors being tested. Moreover, it can learn different forms of the behaviors, and it can detect the relations between them. In essence, the model has an expandable architecture and it contains reusable parts. The first layer CobART networks can be integrated with other CobART networks for another categorization task. Hence, the model presents a way to reveal all behaviors performed by the robot at the same time.     

A probabilistic framework for next best view estimation in a cluttered environment

In this article, we present an information gain-based variant of the next best view problem for occluded environment. Our proposed method utilizes a belief model of the unobserved space to estimate the expected information gain of each possible viewpoint. More precise, this belief model allows a more precise estimation of the visibility of occluded space and with that a more accurate prediction of the potential information gain of new viewing positions. We present experimental evaluation on a robotic platform for active data acquisition, however due to the generality of our approach it also applies to a wide variety of 3D reconstruction problems. With the evaluation done in simulation and on a real robotic platform, exploring and acquiring data from different environments we demonstrate the generality and usefulness of our approach for next best view estimation and autonomous data acquisition.     

Child–robot interaction across cultures: How does playing a game with a social robot compare to playing a game alone or with a friend?

The present study investigates how children from two different cultural backgrounds (Pakistani, Dutch) and two different age groups (8 and 12 year olds) experience interacting with a social robot (iCat) during collaborative game play. We propose a new method to evaluate children’s interaction with such a robot, by asking whether playing a game with a state-of-the-art social robot like the iCat is more similar to playing this game alone or with a friend. A combination of self-report scores, perception test results and behavioral analyses indicate that child–robot interaction in game playing situations is highly appreciated by children, although more by Pakistani and younger children than by Dutch and older children. Results also suggest that children enjoyed playing with the robot more than playing alone, but enjoyed playing with a friend even more. In a similar vein, we found that children were more expressive in their non-verbal behavior when playing with the robot than when they were playing alone, but less expressive than when playing with a friend. Our results not only stress the importance of using new benchmarks for evaluating child–robot interaction but also highlight the significance of cultural differences for the design of social robots.     

工业机器人运动学逆向建模

针对工业机器人在缺乏运动学参数的情况下,进行机器人运动学逆向建模的研究,给出了机器人机座坐标系位置及杆件参数的识别方法,建立了机器人的运动学方程。并分析了计算的末端位姿与示教器显示末端位姿存在的偏差,指出从机器人示教器上读出的关节转角数据存在舍入误差,采用遗传算法分别以位置误差和位姿误差为目标函数进行了辨识。辨识结果表明,以位姿误差为目标函数来辨识关节转角误差的辨识结果是准确的。试验结果表明,提出的机器人运动学逆向建模方法是有效的。     

SafeNet: A methodology for integrating general-purpose unsafe devices in safe-robot rehabilitation systems

Robot-assisted neurorehabilitation often involves networked systems of sensors (“sensory rooms”) and powerful devices in physical interaction with weak users. Safety is unquestionably a primary concern. Some lightweight robot platforms and devices designed on purpose include safety properties using redundant sensors or intrinsic safety design (e.g. compliance and backdrivability, limited exchange of energy). Nonetheless, the entire “sensory room” shall be required to be fail-safe and safely monitored as a system at large. Yet, sensor capabilities and control algorithms used in functional therapies require, in general, frequent updates or re-configurations, making a safety-grade release of such devices hardly sustainable in cost-effectiveness and development time. As such, promising integrated platforms for human-in-the-loop therapies could not find clinical application and manufacturing support because of lacking in the maintenance of global fail-safe properties.     

Visual classification of waste material for nuclear decommissioning

Redundant and nonoperational buildings at nuclear sites go through the process of ‘decommissioning’, involving decontamination of nuclear waste material and demolition of physical infrastructure. One challenging problem currently faced by the nuclear industry during this process is the segregation of redundant waste material into a choice of ‘post-processes’ based upon the nature and extent of its radioactivity that may pose a serious threat to the environment. Following an initial inspection, waste materials are subjected to treatment, disruption and consigned to various types of export containers. To date, the process of objects (waste) classification is performed manually. In order to automate this process, robotic platforms can be deployed that utilise robust and fast vision systems for visual classification of nuclear waste material. This paper proposes a novel solution incorporating a machine vision system for autonomous identification of waste material from decommissioned nuclear plants. Rotation and scale invariant moments are used to describe object shapes in the visual scene whereas a random forest learning algorithm performs object classification. Using nuclear waste simulants (from the nuclear plant decommissioning process), an exhaustive ‘proof-of-concept’ quantitative assessment of the proposed technique is performed, in order to test its applicability within the current problem domain.     

RT-ROS: A real-time ROS architecture on multi-core processors

ROS, an open-source robot operating system, is widely used and rapidly developed in the robotics community. However, running on Linux, ROS does not provide real-time guarantees, while real-time tasks are required in many robot applications such as robot motion control. This paper for the first time presents a real-time ROS architecture called RT-RTOS on multi-core processors. RT-ROS provides an integrated real-time/non-real-time task execution environment so real-time and non-real-time ROS nodes can be separately run on a real-time OS and Linux, respectively, with different processor cores. In such a way, real-time tasks can be supported by real-time ROS nodes on a real-time OS, while non-real-time ROS nodes on Linux can provide other functions of ROS. Furthermore, high performance is achieved by executing real-time ROS nodes and non-real-time ROS nodes on different processor cores. We have implemented RT-ROS on a dual-core processor and conducted various experiments with real robot applications. The experimental results show that RT-ROS can effectively provide real-time support for the ROS platform with high performance by exploring the multi-core architecture.     

用于硅片传输系统的机械手设计

介绍了用于硅片传输系统的机械手发展现状,主要针对300 mm全自动探针测试台的工艺需要进行机械手系统设计,对机械手结构以及设计中需要注意的关键技术进行了阐述。该设计可以推广到整个半导体生产线中其他工艺设备的应用,对未来大尺寸半导体生产线的国产化具有很大意义。     

工业机器人研究现状及发展趋势

作为最典型的机电一体化的高科技装备,工业机器人得到了非常广泛的应用.综述了国内外工业机器人的研究热点现状,并预测了其发展趋势.     

具有面部表情识别与再现的机器人头部系统的研制

研制出具有对人类表情识别和表情再现机能的仿人机器人头部系统,该系统由仿人机器头部本体、图像采集设备、表情及面部器官控制系统、表情识别系统等部分组成;该系统从图像采集装置获得人类表情图像,然后采用SVMs和ASM结合的方法进行视频序列的人脸表情识别,设计了眼球、眼睑、下颚等面部器官和面部表情机构及驱动系统对机器人面部皮肤和头部器官进行运动控制,并根据识别结果,可实现七种不同特征的面部表情的再现。