Foot robot motion behavior identification method and system based on multi-modal perception

Abstract

The invention discloses a foot robot motion behavior identification method and system based on multi-modal perception. The method comprises the steps that multi-modal sensing data information generated when a foot robot repeatedly walks on different types of grounds by a preset distance is collected based on multiple types of sensors; data set division is performed on the multi-modal sensing datainformation according to the different types of grounds, and multi-modal sensing data sets of different ground types are obtained; a hidden Markov model of a sticky-level Dirichlet process is trainedbased on the multi-modal sensing data sets of the different ground types, and an optimal hidden Markov model of the sticky-level Dirichlet process is obtained; to-be-identified sample information is input into the optimal hidden Markov model of the sticky-level Dirichlet process, and the sum of log-likelihood function values is calculated; and foot robot motion behavior identification is performedbased on a calculation result. Through the embodiment, the reliability and accuracy of motion behavior identification are improved.

Description

technical field [0001] The present invention relates to the technical field of robot motion behavior recognition, in particular to a multi-modal perception-based method and system for legged robot motion behavior recognition. Background technique [0002] Footed robots have good environmental adaptability, wide range of motion, and strong load capacity, and can perform tasks such as rugged mountain transportation, dangerous disaster rescue, and military reconnaissance. Therefore, being able to recognize in real time the motion behavior and motion characteristics of the legged robot walking on different types of ground (such as sand, cement, grass, wood, tiles, etc.) will be able to directly adjust the control and gait of the robot, and improve the environment. Adaptability and robustness of the system. Therefore, it is the key technology for the frontier research of legged robots to carry out motion behavior recognition of legged robots based on joint encoders, IMUs, joint ...

AI Technical Summary

Problems solved by technology

However, as the complexity of the environment and the diversity of tasks increase, there are mainly two problems: (1) only considering the single-modal sensing information, it is impossible to accurately realize the perception of the environment and the state estimation of the robot system, reducing the It improves the fault tolerance of motion behavior recognition; at the same time, (2) the use of parametric HMM modeling method will have the problems of uncertain number of recessive states and rapid transition of recessive states, which cannot be learned from complex sensor data. Actual latent patterns, reducing the accuracy of motor behavior recognition

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