| 引用本文: | 左国玉,刘旭.基于弹簧负载倒立摆模型的仿袋鼠机器人稳定跳跃控制[J].控制理论与应用,2018,35(8):1151~1158.[点击复制] |
| ZUO Guo-yu,LIU Xu.Stable jumping control of bionic kangaroo robot using spring-loaded inverted pendulum model[J].Control Theory and Technology,2018,35(8):1151~1158.[点击复制] |
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| 基于弹簧负载倒立摆模型的仿袋鼠机器人稳定跳跃控制 |
| Stable jumping control of bionic kangaroo robot using spring-loaded inverted pendulum model |
| 摘要点击 3684 全文点击 1611 投稿时间：2017-06-06 修订日期：2018-01-19 |
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| DOI编号 10.7641/CTA.2018.70379 |
| 2018,35(8):1151-1158 |
| 中文关键词 袋鼠机器人 运动稳定性 SLIP模型 解耦控制 |
| 英文关键词 bionic kangaroo robot motion stability SLIP model decoupling control |
| 基金项目 北京工业大学智能制造领域大科研推进计划(JZ041001201702)，北京市教委科研计划(KM201310005005) |
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| 中文摘要 |
| 仿生跳跃机器人具备很强的越障和环境适应能力, 但是由于机器人运动过程中较短的可控时间以及腾空阶段运动的不确定性, 运动的稳定性对于仿生跳跃机器人至关重要. 本文对仿袋鼠机器人跳跃运动过程中的稳定跳跃控制问题进行了研究. 首先采用双质量SLIP(Spring-Loaded Inverted Pendulum) 模型对袋鼠机器人的结构进行简化, 建立了机器人系统的动力学模型, 并对机器人的运动过程以及着地相与腾空相的切换条件进行了分析. 然后采用解耦控制的思想, 将SLIP模型的运动控制分解为水平速度控制和跳跃高度控制两个方面, 分别通过控制着地角度实现对水平运动速度的控制, 通过能量补偿实现对跳跃高度的控制. 最后在ADAMS中建立机器人模型并进行了机器人运动仿真实验. 实验结果表明, 本文提出的方法可以实现仿袋鼠机器人进行稳定的周期性跳跃运动. |
| 英文摘要 |
| Bionic hopping robot has a strong ability to overcome obstacles and adapt to the environment. However, due to the short control time in the robot motion and the uncertainty of movement in its vacated stage, the stability of motion is very important for hopping robot control. This paper studied the control method of stable hopping motion of kangaroo robot. The model structure of kangaroo robot is simplified, and the double-mass Spring-Loaded Inverted Pendulum(SLIP) model is established as the robot dynamic model by analyzing the robot motion process, in which the transition conditions of the support and flight phases of the SLIP model are analyzed. Forward speed control and jump height control for the robot SLIP model are studied respectively. The horizontal motion speed is controlled by controlling the landing angle, and the jump height is controlled by energy compensation. The proposed method for periodic hopping motion of the simulation robot model using ADAMS is verified effective. |