基于门控循环神经网络的四足机器人静步态规划方法

doi:10.13229/j.cnki.jdxbgxb20221200

摘要/Abstract

摘要：

为使四足机器人在不配备地形感知设备的情况下，依然能够根据地形崎岖度变化自主调整自身步态规划参数，以提高其在复杂未知地形中的稳定性和适应性，提出了基于门控循环单元（GRU）模型的静步态规划方法。首先，给出了一种连续矩形迈步轨迹生成方法，能够保证机器人的摆动足在未知地形上顺利摆动至目标落足点且整个迈步过程连续。然后，提出了可通过调整参数改变躯干运动轨迹的规划方法。最后，利用GRU模型及各摆动足迈步时间实现对躯干运动轨迹规划中可调整参数的预测，以使四足机器人生成与地形崎岖度变化相适应的运动，且运动过程中能够兼顾能量消耗和自身稳定性。实验结果表明了本文方法的正确性和有效性。

关键词: 机器人, 静步态规划, 门控循环神经网络, 迈步轨迹

Abstract:

In order to enable quadruped robot to independently adjust its gait planning parameters according to change of terrain without terrain sensing system， a static gait planning method based on GRU （Gate Recurrent Unit） model was proposed. Firstly， a method of generating continuous rectangular trajectory for swing foot was presented， which can ensure swing foot of robot move smoothly to target landing point on unknown terrain. Then， a planning method which can change the trajectory of body by adjusting parameters was proposed. Finally， GRU model and step times of each swing foot are used to realize prediction of the adjustable parameters in body trajectory planning， so that quadruped robot can generate a motion adapted to the change of terrain， and energy consumption and self-stability were considered in motion planning. The experimental results show that the proposed method is correct and effective.

Key words: robot, static gait planning, gate recurrent neural network, step trajectory trajectory

中图分类号:

TP242

张帅帅,尹燕芳,肖林京,姜帅. 基于门控循环神经网络的四足机器人静步态规划方法[J]. 吉林大学学报(工学版), 2023, 53(3): 902-912.

Shuai-shuai ZHANG,Yan-fang YIN,Lin-jing XIAO,Shuai JIANG. Static gait planning method for quadruped robot based on gate recurrent neural network[J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(3): 902-912.

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参数		数值
测试地形尺寸/m	长	2
测试地形尺寸/m	宽	1.6
障碍物个数/个		12
单个障碍物尺寸/m	长	［0.1，0.5］
	宽	［0.1，0.5］
	高	［0.05，0.15］