Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (3): 902-912.doi: 10.13229/j.cnki.jdxbgxb20221200

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Static gait planning method for quadruped robot based on gate recurrent neural network

Shuai-shuai ZHANG(),Yan-fang YIN,Lin-jing XIAO,Shuai JIANG()   

  1. Department of Electrical and Information,Shandong University of Science and Technology,Jinan 250031,China
  • Received:2022-09-16 Online:2023-03-01 Published:2023-03-29
  • Contact: Shuai JIANG E-mail:zhangshuais@sdust.edu.cn;jiang_jackson@163.com

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

CLC Number: 

  • TP242

Fig.1

Experimental model of quadruped robot"

Fig.2

Coordinate systems in the gait planning"

Fig.3

Gait event of quadruped robot walking with static gait"

Fig.4

Schematics diagram of walkingcycle of quadruped robot"

Fig.5

Schematics diagram of motion planning of robot"

Fig.6

Rectangular plantar trajectory and its parameters"

Fig.7

Coordinate system {FO} of swing foot"

Fig.8

Determination of waypoints of body trajectory"

Fig.9

Distribution of stability margin within DST"

Fig.10

Internal structure and model structure of GRU unit"

Fig.11

Flow chart of acquisition process of T"

Fig.12

Sample experimental scenes to get the training set"

Table 1

Simulation environment parameter settings"

参数数值
测试地形尺寸/m2
1.6
障碍物个数/个12
单个障碍物尺寸/m[0.1,0.5]
[0.1,0.5]
[0.05,0.15]

Fig.13

Mean absolute error of different values of k"

Fig.14

Test results of RNN prediction model"

Fig.15

Test terrain"

Fig.16

Screenshot of robot motion process"

Fig.17

Change of roll angle and its phase diagram in the process of robot walking"

Fig.18

Changing curve of ?^i+1 during walking process"

Fig.19

Position variation curve of COG in side direction with respect to moving direction"

Fig.20

Velocity and acceleration variation curves of robot's body during robot walking process"

Fig.21

Change of the roll angle of body during the movement of the robot"

Fig.22

Energy consumption of robot in motion"

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