关键词： Dynamic movement primitives   Humanoid robot   Knowledge acquisition   Gesture representation  

摘要： A system for learning and executing gestures in a humanoid robot has been developed and implemented in this work. Gestures are represented via the use of dynamical movement primitives on the robotic platform REEM. Since agnostic knowledge is considered when designing trajectories, our approach can be easily extended to other robots. Implemented work involves recording of gestures using three different procedures, from the Own robot, with the help of a user, and from external devices. Next, the dynamic movement primitives representing the motions are generated to describe trajectories that will finally be executed on the real humanoid robot. Several experiments are provided illustrating how knowledge is acquired by the robot, represented in the form of dynamical systems, generalized and reproduced from different starting conditions. (C) 2015 Elsevier B.V. All rights reserved.

关键词： 薄煤层工作面   巡检   关节式机器人   动力学建模   越障  

摘要： 设计了一种适应煤矿井下薄煤层工作面环境的关节式巡检机器人。完成了巡检机器人的机械结构设计,对攀越障碍时的动作进行了规划,并对越障过程中的初始状态和临界状态进行了动力学建模和分析,得到了驱动电机扭矩与越障高度和自身倾角之间的关系,为电机选型奠定了理论基础,进一步提高了巡检机器人的环境适应能力。

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： 机器人竞赛   easy   综合技能   地图   赛场   So   设计   软件  

摘要： 机器人综合技能比赛是中国青少年机器人竞赛项目之一，要求参赛代表队在现场自行拼装机器人、编制机器人运行程序、调试和操作机器人。根据竞赛规则，比赛场地图和任务只有在赛前才公布，因此参赛队员平时应注重不同图形、不同任务、不同难度场地的训练，以提高机器人的适应性。

关键词： COMPARATIVE studies   ADNEXAL diseases   BENIGN tumors   LAPAROSCOPY   SURGICAL robots   ONCOLOGY  

关键词： Robot learning   Learning from experience   Experiential learning in robots   Planning and plan execution   Action execution monitoring   Real-world experimentation   Failure contexts  

摘要： Learning is essential for cognitive robots as humans to gain experience and to adapt to the real world. We propose an experiential learning method for robots to build their experience online and to transfer knowledge among appropriate contexts. Experience gained through learning is used as a guide to future decisions of the robot for both efficiency and robustness. We use Inductive Logic Programming (ILP) learning paradigm to frame hypotheses represented in first-order logic that are useful for further reasoning and planning processes. Furthermore, incorporation of background knowledge is also possible to generalize the framed hypotheses. Partially specified world states can also be easily represented by these hypotheses. All these advantages of ILP make this approach superior to the other supervised learning methods. We have analyzed the performance of the learning method on our autonomous mobile robot and on our robot arm both building their experience on action executions online. It has been observed in both domains that our experience-based learning and learning-based guidance methods frame sound hypotheses that are useful for constraining and guiding the future tasks of the robots. This learning paradigm is promising especially for the contexts where abstraction is useful for efficient transfer of knowledge.

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： robot dynamics   software engineering   dynamic behavior   dynamic variability   robotic control system   robotic software system   serious development cost   Context modeling   Navigation   Robot kinematics   Runtime   Software engineering   Software Technologies   dynamic variability   robotics   software engineering  

摘要： The complexity necessary for robotics software systems to dynamically respond to or handle a variety of evolving scenarios translates into serious development costs and engineering challenges. Dynamic variability is one new strategy for supporting the dynamic behavior of robotics control systems.