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A walking pattern generation method of humanoid robot MAHRU-R

机译:人形机器人MAHRU-R的行走模式生成方法

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摘要

This paper proposes an omni-directional walking pattern generation method for a humanoid robot MAHRU-R. To walk stably without falling down, a humanoid robot needs the walking pattern. Our previous walking pattern method generated the walking pattern with linear polynomials of the zero moment point (ZMP). It implemented the simple walking like forward/backward walking, side step walking and turning. However, this method was not sufficient to satisfy the various walking which is combined by forward/backward walking, side step walking and turning. We needed to upgrade the walking pattern generation method to implement an omni-directional walking. We use the linear inverted pendulum model consisted of ZMP and center of mass in order to simplify the computation of walking pattern. The proposed method assumes that the state of the following stride is same to the state of the current stride. Using this assumption of walking pattern, the proposed method generates the stable walking pattern for various walking. And the proposed scheme generates the ZMP trajectory with the quartic polynomials in order to reduce the fluctuation of ZMP trajectory by various walking. To implement the efficient walking pattern, this method proposes three walking modules: periodic step module, transient step module and steady step module. Each step module utilizes weighted least square method with future ZMP position information. The effectiveness of the proposed method is verified by simulations of various walking. And the proposed method is confirmed by the experiment of real humanoid robot MAHRU-R.
机译:提出了一种仿人机器人MAHRU-R的全向行走模式生成方法。为了稳定行走而不摔倒,类人机器人需要行走模式。我们以前的步行模式方法使用零矩点(ZMP)的线性多项式生成了步行模式。它实现了简单的步行,如向前/向后步行,侧步步行和转弯。然而,这种方法不足以满足通过向前/向后步行,侧步步行和转弯相结合的各种步行。我们需要升级步行模式生成方法,以实现全方位步行。为了简化步行模式的计算,我们使用了由ZMP和质心组成的线性倒立摆模型。所提出的方法假设跟随步幅的状态与当前步幅的状态相同。使用这种步行模式的假设,所提出的方法为各种步行产生稳定的步行模式。提出的方案利用四次多项式生成ZMP轨迹,以减少由于各种行走而引起的ZMP轨迹的波动。为了实现有效的步行模式,该方法提出了三个步行模块:周期性步模块,瞬态步模块和稳定步模块。每个步骤模块利用加权最小二乘法结合未来的ZMP位置信息。通过对各种步行运动的仿真验证了该方法的有效性。并通过仿真人形机器人MAHRU-R的实验验证了该方法的有效性。

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