Previewing control humanoid robot gait planning method based on deep reinforcement learning

A humanoid robot and reinforcement learning technology, which is applied in the field of gait planning of pre-view control humanoid robots based on deep reinforcement learning, and can solve problems such as inability to effectively solve walking and other problems.

Active Publication Date: 2018-09-18
SOUTH CHINA UNIV OF TECH
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Problems solved by technology

[0004] The present invention mainly studies the gait planning function of humanoid robots walking in complex ground environments. Aiming at the fact that the existing control theory cannot effectively solve the problem of walking in complex environments, a preview control human

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  • Previewing control humanoid robot gait planning method based on deep reinforcement learning
  • Previewing control humanoid robot gait planning method based on deep reinforcement learning
  • Previewing control humanoid robot gait planning method based on deep reinforcement learning

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Embodiment Construction

[0065] The present invention will be further described below in conjunction with specific examples.

[0066] The gait planning method of the preview control humanoid robot based on deep reinforcement learning provided by this embodiment is as follows:

[0067] 1) Acquisition of the state of the humanoid robot

[0068] The status information is obtained through the sensors assembled on the humanoid robot. The stability of the humanoid robot when walking is mainly affected by the steering gear in the pitch direction of the supporting foot. Therefore, in the defined state information, the information of the supporting foot and the angle information of the pitching steering gear on the supporting foot should be provided. In addition, the values ​​of acceleration and angular velocity are also needed to judge the stability of the walking process of the humanoid robot. Then make real-time adjustments to the offline gait so that it can adapt to uneven terrain environments.

[0069]...

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Abstract

The invention discloses a previewing control humanoid robot gait planning method based on deep reinforcement learning. The method comprises steps of 1) acquiring state information through a sensor assembled on a humanoid robot; 2) improving a current deep reinforcement learning network, and defining totally new states, action vectors and awarding functions; 3) using the defined action vectors to correct output of a previewing controller, calculating the angle of each steering engine of the feet of the humanoid robot and guiding the humanoid robot to walk; and 4) during walking processes of thehumanoid robot, using values of the states, the action vectors and the awarding functions to update the improved deep reinforcement learning network. According to the invention, the walking problem of the humanoid robot under a complex environment can be effectively solved; and a test is carried out on a simulation platform and a real robot to verify the validity of the method.

Description

technical field [0001] The invention relates to the technical field of humanoid robots, in particular to a gait planning method for preview control humanoid robots based on deep reinforcement learning. Background technique [0002] An essential function of a humanoid robot is to walk stably. However, due to the complexity of the structure of the humanoid robot, the strong coupling relationship, and the poor independence of the modules, it is difficult to realize the stable walking function of the humanoid robot. Therefore, the gait control and planning of humanoid robots have become a research hotspot in related fields. Traditional gait control methods can be roughly divided into two categories: methods based on modern control theory and methods based on walking mechanism. However, most of these methods are old and not suitable for the increasingly complex model mechanisms. The recent continuous proposal and innovation of various machine learning methods has also stimulat...

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Application Information

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IPC IPC(8): G05B13/04
CPCG05B13/042
Inventor 毕盛刘云达董敏张英杰闵华清
Owner SOUTH CHINA UNIV OF TECH
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