关键词： 空间机器人   动力学建模   参数辨识   关节摩擦  

摘要： 研究了考虑关节摩擦影响的空间机器人系统的动力学建模与参数辨识问题.采用单向递推组集方法和虚功率原理建立了含有关节摩擦的多体系统动力学方程,推导了关节摩擦对系统动力学方程的贡献,采用基于腕力传感器信号和最小二乘法的辨识方法进行了系统惯性参数的辨识.数值仿真结果验证了数学模型的正确性与辨识方法的有效性.

关键词： 遥操作机器人   机器人技术   力觉临场感   人机交互式   传感技术   控制技术   研究与发展   机器人学  

摘要： 人机交互式机器人是当前机器人学研究的前沿和热点之一,而临场感(telepresence)技术是人与机器人交互的核心。本文通过阐述力觉临场感遥操作机器人技术的产生、发展和现状,介绍了力觉临场感遥操作机器人的系统组成,指出力觉临场感遥操作机器人面临的3个难题:力感知、力反馈和大时延力控制。针对以上问题,对力觉临场感遥操作机器人的四大关键技术:传感技术、力反馈与触觉再现技术、大时延控制技术、虚拟预测环境建模技术进行了综述;介绍了东南大学仪器科学与工程学院机器人传感与控制技术研究所30年来开展人机交互遥操作机器人技术研究的进展及其在核探测、康复医疗领域应用的情况。展望了人机交互力觉临场感遥操作机器人技术未来研究与发展的重点。

关键词： 嵌入式产品开发   智能循迹机器人   高职学生   创新能力  

摘要： 高职学生创新能力的培养在各高职院校都得到了很好的重视,嵌入式产品开发赛项作为一种高科技创新活动,为高职学生创新能力的培养提供了广阔的舞台.特别是智能循迹机器人是嵌入式产品领域内非常重要,且被广泛研究的一种智能移动装置,本文重点是研究如何通过嵌入式产品的开发提高学生的创新能力。

关键词： 凯恩方法   柔性关节   传动比   ADAMS   S函数  

摘要： 针对柔性关节机器人的动力学建模问题,首先在已存在的柔性关节机器人凯恩动力学求解方法的基础上对广义主动力的求解进行分类与合并,详细地推导出传动比不为1的多连杆柔性关节机器人的动力学方程。然后分别运用ADAMS与S函数对柔性关节机器人进行正动力学仿真与数值仿真。最后通过对比仿真,证明了用凯恩方法建立的柔性关节机器人动力学方程的正确性。

关键词： 可重构机器人   动力学自动建模   递归牛顿-欧拉方法   结构设计  

摘要： 针对可重构机器人单元模块结构及对接机构的局限性,提出了一种可移动重构机器人模块单元,该单元具有12种连接方式,设计了一种独特的正弦加速度传动槽与插销组合的对接锁紧机构。针对可重构机器人几何构型参数的不确定性问题,构建双模块空间方位变换表,基于递归牛顿-欧拉方法提出多支链机器人系统的动力学方程自动生成方法,并给出自动生成的算法流程。最后以6个模块组成的双支链构型为例,进行了自动生成动力学方程的计算与仿真分析,验证了该自动建模方法的可行性和有效性。

关键词： Neurobiology   Neuromorphics   Neural networks (Computer science)   Cognition   Robots -- Control systems  

关键词： movement   proprioception   rehabilitation   robotics   stroke  

摘要： Background and Purpose-Developing a better understanding of the trajectory and timing of stroke recovery is critical for developing patient-centered rehabilitation approaches. Here, we quantified proprioceptive and motor deficits using robotic technology during the first 6 months post stroke to characterize timing and patterns in recovery. We also make comparisons of robotic assessments to traditional clinical measures. Methods-One hundred sixteen subjects with unilateral stroke were studied at 4 time points: 1, 6, 12, and 26 weeks post stroke. Subjects performed robotic assessments of proprioceptive (position sense and kinesthesia) and motor function (unilateral reaching task and bimanual object hit task), as well as several clinical measures (Functional Independence Measure, Purdue Pegboard, and Chedoke-McMaster Stroke Assessment). Results-One week post stroke, many subjects displayed proprioceptive (48% position sense and 68% kinesthesia) and motor impairments (80% unilateral reaching and 85% bilateral movement). Interindividual recovery on robotic measures was highly variable. However, we characterized recovery as early (normal by 6 weeks post stroke), late (normal by 26 weeks post stroke), or incomplete (impaired at 26 weeks post stroke). Proprioceptive and motor recovery often followed different timelines. Across all time points, robotic measures were correlated with clinical measures. Conclusions-These results highlight the need for more sensitive, targeted identification of sensory and motor deficits to optimize rehabilitation after stroke. Furthermore, the trajectory of recovery for some individuals with mild to moderate stroke may be much longer than previously considered.

关键词： Off-line learning   Adaptive robot   Sequential task   Dynamic neural field   Persistent activity   Social learning  

摘要： There is currently an increasing demand for robots able to acquire the sequential organization of tasks from social learning interactions with ordinary people. Interactive learning-by-demonstration and communication is a promising research topic in current robotics research. However, the efficient acquisition of generalized task representations that allow the robot to adapt to different users and contexts is a major challenge. In this paper, we present a dynamic neural field (DNF) model that is inspired by the hypothesis that the nervous system uses the off-line re-activation of initial memory traces to incrementally incorporate new information into structured knowledge. To achieve this, the model combines fast activation-based learning to robustly represent sequential information from single task demonstrations with slower, weight-based learning during internal simulations to establish longer-term associations between neural populations representing individual subtasks. The efficiency of the learning process is tested in an assembly paradigm in which the humanoid robot ARoS learns to construct a toy vehicle from its parts. User demonstrations with different serial orders together with the correction of initial prediction errors allow the robot to acquire generalized task knowledge about possible serial orders and the longer term dependencies between subgoals in very few social learning interactions. This success is shown in a joint action scenario in which ARoS uses the newly acquired assembly plan to construct the toy together with a human partner. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Autism spectrum disorder   Intervention   Treatment   Robotics   Vocational   Social skills  

摘要： This pilot study evaluated a novel intervention designed to reduce social anxiety and improve social/vocational skills for adolescents with autism spectrum disorder (ASD). The intervention utilized a shared interest in robotics among participants to facilitate natural social interaction between individuals with ASD and typically developing (TD) peers. Eight individuals with ASD and eight TD peers ages 12-17 participated in a weeklong robotics camp, during which they learned robotic facts, actively programmed an interactive robot, and learned "career" skills. The ASD group showed a significant decrease in social anxiety and both groups showed an increase in robotics knowledge, although neither group showed a significant increase in social skills. These initial findings suggest that this approach is promising and warrants further study.