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Robustness dynamic motion method based on reinforced learning and all-body controller

A technology of reinforcement learning and dynamic motion, applied in the direction of program control manipulators, manufacturing tools, manipulators, etc., can solve the problems of low robustness and low computational efficiency.

Inactive Publication Date: 2018-03-30
SHENZHEN WEITESHI TECH
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Problems solved by technology

[0004] Aiming at the problems of low computational efficiency and low robustness, the present invention proposes a robust dynamic walking control consisting of a dynamic motion planning program, a robust reinforcement learning process, and a new whole-body motion controller controller for high computational efficiency and excellent robustness

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  • Robustness dynamic motion method based on reinforced learning and all-body controller
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  • Robustness dynamic motion method based on reinforced learning and all-body controller

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Embodiment Construction

[0057] It should be noted that, in the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The present invention will be further described in detail below with reference to the drawings and specific embodiments.

[0058] figure 1 It is a system frame diagram of a robust dynamic motion method based on reinforcement learning and a whole body controller of the present invention. It mainly includes the phase space planning method based on reinforcement learning and the dynamic control of the whole body.

[0059] Phase space planning method based on reinforcement learning (1), a reinforcement learning process around the phase space planning framework (PSP) is designed, the model is simplified by using the inherent directional walking constraints of PSP, and the effective step size switching information is generated by using the simplified model. Spatial planning methods mainly include phase space plan...

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Abstract

The invention provides a robustness dynamic motion method based on reinforced learning and an all-body controller. The method comprises the steps of designing a reinforced learning process surroundinga phase space planning frame (PSP); utilizing a directional walking simplified restriction model inherent in PSP for carrying out phase space planning, reinforced learning problem and learning strategy evaluation; meanwhile, adopting the all-body dynamic controller as an accelerated speed command for calculating in an operation space; utilizing differential positive motion to convert into joint accelerated speed; and optimizing a counter-acting force of a non-drive robot according to the accelerated speed, so that two parameters adopting position or time as outputs can be calculated at the same time, multiple walking modes can be produced, and the process speed is applicable to real-time control. The invention provides a robustness dynamic walking controller formed by a dynamic motion plan program, a robustness reinforced learning process and a novel all-body motion controller, so that higher calculation efficiency is realized, and excellent robustness is obtained.

Description

technical field [0001] The invention relates to the field of dynamic motion of robots, in particular to a robust dynamic motion method based on reinforcement learning and a whole-body controller. Background technique [0002] Mobility is an important performance index of robots, and it is one of the hotspots in the field of robot research in recent years, involving computer vision, job planning, path planning, static and dynamic walking control, etc. Forward-looking fields such as medical care, military and industry, serve in an environment designed for humans, replace humans in dangerous environments, replace humans and serve humans to a certain extent, and are of great significance to the development of human work and life, but Due to the many joints of the robot, too many active points, and the system involves many fields, it is very difficult to control the dynamic walking. Existing research on dynamic motion planning for robots has consistently performed poorly in term...

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Application Information

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IPC IPC(8): B25J9/16
CPCB25J9/16B25J9/1664B25J9/1679
Inventor 夏春秋
Owner SHENZHEN WEITESHI TECH
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