Title: Terrain frames classification based on HMC for quadruped robot
Authors: Zhe Li; Yibin Li; Xuewen Rong; Hui Zhang
Addresses: China Water Resources Beifang Investigation, Design and Research Co. Ltd., Tianjin, 300222, China; School of Control Science and Engineering, Shandong University, Jinan, 250061, China ' School of Control Science and Engineering, Shandong University, Jinan, 250061, China ' School of Control Science and Engineering, Shandong University, Jinan, 250061, China ' School of Electrical Engineering and Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong, China
Abstract: As a multi-body nonlinear rigid-flex system, the quadruped robot must maintain the correct perception and classification capabilities for the external environment. This ability is necessary to help quadruped robots make path planning, gait adjustment and attitude control while maintaining complex interactions with the external environment. This paper proposes a terrain classification algorithm based on HMC (HMRF-MAP-CNN) as the basis for robot motion control strategy selection. Different from the classification method based on image features, the terrain-based classification method has higher accuracy and better computational efficiency. In the process of solving the actual terrain classification problem, the algorithm firstly uses HMRF to classify the obtained terrain frames into two categories, flat and rugged, and then use CNN to filter, according to the causes of rugged terrain frames. Through the simulation experiment and comparative analysis, the superiority of HMC terrain frame classification algorithm is confirmed.
Keywords: quadruped robot; terrain classification; Hidden Markov random field; HMRF; Maximum a Posteriori; MAP; convolutional neural network; CNN.
International Journal of Innovative Computing and Applications, 2021 Vol.12 No.5/6, pp.273 - 280
Received: 04 Apr 2020
Accepted: 21 Apr 2020
Published online: 01 Dec 2021 *