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Hexapod-robot real-time gait planning method based on deep reinforcement learning

A hexapod robot and reinforcement learning technology, which is applied in the field of real-time gait planning of hexapod robots based on deep reinforcement learning, can solve the problem of long-distance autonomous walking and unstable end position, and the hexapod robot gait planning technology cannot adapt to complex terrain environment and other issues, to achieve real-time gait planning, solve the dimensional disaster, and promote convergence.

Inactive Publication Date: 2017-12-08
唐开强
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Problems solved by technology

[0003] The technical problem to be solved by the present invention is that the existing hexapod robot gait planning technology cannot adapt to the complex terrain environment, and the situation of long-distance autonomous walking and terminal position is not fixed

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  • Hexapod-robot real-time gait planning method based on deep reinforcement learning

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Embodiment Construction

[0044] Such as figure 1 As shown, a real-time gait planning method for a hexapod robot based on deep reinforcement learning of the present invention operates on a system including: a satellite navigation system, a machine vision and image processing system, a central control system, and an underlying motion system.

[0045] Among them, the satellite navigation system is mainly composed of satellite map software installed on the hexapod robot. After inputting the destination, it can quickly complete the path planning and transmit the path planning information to the central control system; the image processing system is mainly installed on the The camera at the front of the hexapod robot and the matlab software installed on the industrial computer; the central control system is mainly composed of a dynamics simulation platform installed on the industrial computer to pre-train the deep reinforcement learning network and communication module based on deep deterministic policy grad...

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Abstract

The invention provides a hexapod-robot real-time gait planning method based on deep reinforcement learning. The method comprises the following steps of using a hexapod robot to acquire environment road condition information and making an integral movement track; through a camera, acquiring an environment photograph, according to the photograph, using a binocular range finding method to calculate road condition information of a target track, and using the calculated road condition information of the track in robot center of mass movement track navigation; in a foot end swinging space range of robot legs, taking photographs of a road condition environment, and through a trained deep reinforcement learning network based on a depth determinacy policy gradient (DDPG), carrying out data dimension reduction and feature extraction on the photograph; and according to a feature extraction result, acquiring a control policy of a hexapod robot, wherein the hexapod robot controls foot laying of a robot according to the control policy so that real-time walking of the hexapod robot is realized. By using the gait planning method, a complex and non-structural environment of a road condition can be planned in real time. The method has an important meaning for increasing an environmental adaptive capacity of the hexapod robot.

Description

technical field [0001] The invention relates to a method for real-time gait planning of a hexapod robot, in particular to a real-time gait planning method for a hexapod robot based on deep reinforcement learning. Background technique [0002] Robot technology is a high degree of integration of materials science, mechanism science, bionics, mechatronics technology, control technology, sensor technology, artificial intelligence and other disciplines, and is an important manifestation of the country's industrial development level and scientific and technological strength. The multi-legged bionic robot that completes gait planning autonomously is a highly intelligent mobile robot that can learn and complete gait planning independently of the external environment. The road conditions are complex and diverse, and the traditional pre-programmed gait planning methods for hexapod robots have great limitations. In order to improve the environmental adaptability of the hexapod robot, ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05D1/02
CPCG05D1/0251G05D1/0276
Inventor 唐开强刘佳生洪俊孙建侯跃南钱勇潘东旭
Owner 唐开强
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