Terrain classification and adaptive locomotion for a hexapod robot Qingzhui

doi:10.1007/s11465-020-0623-1

Front. Mech. Eng.

2021, Vol. 16

Issue (2) : 271-284 https://doi.org/10.1007/s11465-020-0623-1

RESEARCH ARTICLE

Terrain classification and adaptive locomotion for a hexapod robot Qingzhui

Yue ZHAO, Feng GAO(

), Qiao SUN, Yunpeng YIN

State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

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Abstract

Legged robots have potential advantages in mobility compared with wheeled robots in outdoor environments. The knowledge of various ground properties and adaptive locomotion based on different surface materials plays an important role in improving the stability of legged robots. A terrain classification and adaptive locomotion method for a hexapod robot named Qingzhui is proposed in this paper. First, a force-based terrain classification method is suggested. Ground contact force is calculated by collecting joint torques and inertial measurement unit information. Ground substrates are classified with the feature vector extracted from the collected data using the support vector machine algorithm. Then, an adaptive locomotion on different ground properties is proposed. The dynamic alternating tripod trotting gait is developed to control the robot, and the parameters of active compliance control change with the terrain. Finally, the method is integrated on a hexapod robot and tested by real experiments. Our method is shown effective for the hexapod robot to walk on concrete, wood, grass, and foam. The strategies and experimental results can be a valuable reference for other legged robots applied in outdoor environments.

Keywords terrain classification hexapod robot legged robot adaptive locomotion gait control

Corresponding Author(s): Feng GAO

Online First Date: 14 May 2021 Issue Date: 15 June 2021

Cite this article:

Yue ZHAO,Feng GAO,Qiao SUN, et al. Terrain classification and adaptive locomotion for a hexapod robot Qingzhui[J]. Front. Mech. Eng., 2021, 16(2): 271-284.

URL:

https://academic.hep.com.cn/fme/EN/10.1007/s11465-020-0623-1
https://academic.hep.com.cn/fme/EN/Y2021/V16/I2/271

Fig.1 Robot structure and definitions of the coordinate systems of the robot Qingzhui.

Fig.2 Components of the driver unit in the leg joint.

Fig.3 Sensors of the robot Qingzhui. IMU: Inertial measurement unit.

Fig.4 Hierarchical control framework of the motion planning used in the robot Qingzhui. IMU: Inertial measurement unit.

Fig.5 Flow chart of terrain classification system. SVM: Support vector machine.

Fig.6 Schematic diagram of leg mechanism.

Fig.7 Recorded data about (a) tip force and (b) tip position of leg Group A. MF: Middle front; RB: Right back; RF: Right front.

Fig.8 Samples of training data (in each diagram, terrain types from left to right are concrete, foam, and wood): Data of (a) leg 1, (b) leg 2, (c) leg 3, (d) leg 4, (e) leg 5, and (f) leg 6.

Fig.9 Result of grid research in the terrain classification model.

Fig.10 State machine in tripod trotting gait: (a) Diagram of gait sequence. Feet in support is filled with black, and feet in swing is filled with white. (b) Diagram of state transformation. T: The time of one gait cycle. MF: Middle front; MB: Middle back; RB: Right back; RF: Right front; LB: Left back; LF: Left front.

Fig.11 Leg impedance control: (a) Definitions of Cartesian and spherical coordinate system; (b) impedance model of the leg.

Fig.12 Flow chart of leg impedance control.

Fig.13 Simulation curves with different stiffnesses: (a) Soft stiffness (param1= 5 kN/m), (b) appropriate stiffness (param2= 15 kN/m), and (c) hard stiffness (param3= 25 kN/m).

Tab.1 Confusion matrix of the first leg

Tab.2 Precision and recall of the classification

Fig.14 Experimental scene of the second experiment.

Fig.15 Experimental results of the second experiment: (a) Acceleration of the robot in the y-axis, (b) acceleration boxplot of two kinds of parameters, (c) positions of the robot in the y-axis, and (d) position boxplot of two kinds of parameters.

Fig.16 Experimental results of the robot walking at a speed of 0.6 m/s: (a) Acceleration of the robot in the y-axis while walking on concrete, (b) acceleration boxplot of two kinds of parameters while walking on concrete, (c) acceleration of the robot in the y-axis while walking on foam, and (d) acceleration boxplot of two kinds of parameters while walking on foam.

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