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Self-contained measurement of dynamic legged locomotion: Design for robot and field environments.

机译:动态行程运动的独立测量:针对机器人和现场环境的设计。

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Currently there is no robotic solution that surpasses the grace, capabilities, and speed of mammalian legged locomotion. Underlying an ability to analyze and synthesizes this motion is the challenge of efficiently overcoming the discontinuous dynamics, without constraining the agility, of dynamic legged locomotion.; Recent strides in biology and robotics have shed new light on the governing principles underlying this motion. The dynamics are central to modeling and driving the legged motion. Consideration of these principles has lead to the research and design of the Kinetically Ordered Locomotion Tetrapod (KOLT) galloping robot.; This research recasts galloping in an engineering framework and defines a simple gait classification method based energy phases. This framework is also used to define the gallop using a hybrid model consisting of flight phase, single-contact, and double-contact. This research leverages theory in sensor design and estimation to develop an integrated hybrid estimation method based on the eight-step model. This was tested via laboratory experiments on KOLT and demonstrated in the field using a Labrador retriever.; This results of this work show improved motion estimation by combining kinetic state models with inertial measurements and measurement aids (such as: visual or range data). The major contributions of this research program are three-fold: (1) the discovery of new theories/methods to relate different, but related, sensor measurements to gain a more certain state estimate; (2) the integration of these methods into a field robust hardware package and, (3) the demonstration of collection and analysis of biomechanical data in field.; The matter of how a robot (or animal) reacts over terrain is deceptively simple, i.e., it thrusts and the rest is governed by Newton's laws of motion. Modeling this in detail however, still remains a significant challenge. By providing a robust estimate of the motion and its dynamics, the methods presented in this thesis move one step closer towards the great promise of fielded dynamic legged locomotion.
机译:当前,没有任何一种机器人解决方案能够超越哺乳动物腿式运动的优雅,能力和速度。分析和合成该运动的基本能力是有效克服动态腿运动的不连续动力学而又不限制其敏捷性的挑战。生物学和机器人技术的最新进展为该议案所依据的管理原则提供了新的思路。动力学是建模和驱动腿部运动的关键。考虑这些原则已导致对运动有序四脚架(KOLT)舞动机器人的研究和设计。这项研究在工程框架中重塑了疾驰,并定义了一种基于能量阶段的简单步态分类方法。该框架还用于通过包含飞行阶段,单接触和双接触的混合模型来定义驰the。这项研究利用传感器设计和估计中的理论,开发了基于八步模型的集成混合估计方法。这是通过在KOLT上进行的实验室实验测试的,并使用拉布拉多猎犬在野外进行了证明。这项工作的结果表明,通过将运动状态模型与惯性测量和测量辅助工具(例如:视觉或范围数据)结合起来,可以改善运动估计。该研究计划的主要贡献有三方面:(1)发现与不同但相关的传感器测量相关的新理论/方法,以获得更确定的状态估计; (2)将这些方法集成到强大的现场硬件包中,以及(3)现场采集和分析生物力学数据的演示;机器人(或动物)如何在地形上做出反应的问题看似简单,即,它可以推开,其余的则由牛顿运动定律控制。但是,对此进行详细建模仍然是一个重大挑战。通过提供运动及其动力学的可靠估计,本文提出的方法朝着现场动态腿运动的巨大希望迈进了一步。

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