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A(2)ML: A general human-inspired motion language for anthropomorphic arms based on movement primitives

机译:A(2)ML:基于运动原语的拟人臂互动的一般人体风格运动语言

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摘要

The recent increasing demands on accomplishing complicated manipulation tasks necessitate the development of effective task-motion planning techniques. To help understand robot movement intention and avoid causing unease or discomfort to nearby humans toward safe human-robot interaction when these tasks are performed in the vicinity of humans by those robot arms that resemble an anthropomorphic arrangement, a dedicated and unified anthropomorphism-aware task-motion planning framework for anthropomorphic arms is at a premium. A general human-inspired four-level Anthropomorphic Arm Motion Language (A(2)ML) is therefore proposed for the first time to serve as this framework. First, six hypotheses/rules of human arm motion are extracted from the literature in neurophysiological field, which form the basis and guidelines for the design of A(2)ML. Inspired by these rules, a library of movement primitives and related motion grammar are designed to build the complete motion language. The movement primitives in the library are designed from two different but associated representation spaces of arm configuration: Cartesian-posture-swivel-angle space and human arm triangle space. Since these two spaces can be always recognized for all the anthropomorphic arms, the designed movement primitives and consequent motion language possess favorable generality. Decomposition techniques described by the A(2)ML grammar are proposed to decompose complicated tasks into movement primitives. Furthermore, a quadratic programming based method and a sampling based method serve as powerful interfaces for transforming the decomposed tasks expressed in A(2)ML to the specific joint trajectories of different arms. Finally, the generality and advantages of the proposed motion language are validated by extensive simulations and experiments on two different anthropomorphic arms. (C) 2018 Elsevier B.V. All rights reserved.
机译:最近越来越大的对成就复杂操纵任务的要求需要开发有效的任务运动计划技术。为了帮助理解机器人运动意图,避免对附近人类的不安或不适用于安全的人机互动,当这些任务在人类的那些机器人臂中进行了类似于拟人的布置,专用和统一的拟人术语感知任务 - 拟人臂的运动规划框架是溢价。因此,第一次提出了一般人体灵感的四级拟人臂动作语言(A(2)毫升)作为本框架。首先,从神经生理领域的文献中提取六个假设/人体臂的规则,这构成了(2)ml设计的基础和指导。受这些规则的启发,旨在构建完整的运动语言的运动原语库和相关运动语法。图书馆中的运动原语是由臂配置的两个不同但相关的表示空间设计:笛卡尔 - 姿势 - 旋转角度空间和人臂三角空间。由于这两个空间可以始终识别所有拟人臂,所以设计的运动基元和随后的运动语言具有良好的一般性。提出由A(2)mL语法描述的分解技术将复杂的任务分解成运动原语。此外,基于二次编程的方法和基于采样的方法用作强大的接口,用于将以(2)ML中的分解任务转换为不同臂的特定关节轨迹。最后,通过广泛的模拟和两种不同的拟人臂的实验验证了所提出的运动语言的一般性和优点。 (c)2018 Elsevier B.v.保留所有权利。

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