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Robot path planning method based on ANFIS fuzzy neural network

A fuzzy neural network and path planning technology, applied in the field of robotics, can solve problems such as path redundancy, complex trap paths, etc., and achieve the effect of reducing workload

Inactive Publication Date: 2017-09-15
CHINA UNIV OF MINING & TECH
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Problems solved by technology

[0003] Purpose of the invention: In order to solve the problem of complex trap path reciprocation and path redundancy in reactive navigation in the prior art, the present invention provides a robot path planning method based on ANFIS fuzzy neural network, which can not only reduce the workload of logical reasoning, And it can get rid of the trap state when the robot is running towards the target

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  • Robot path planning method based on ANFIS fuzzy neural network
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  • Robot path planning method based on ANFIS fuzzy neural network

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[0025] Below in conjunction with accompanying drawing and specific embodiment, further illustrate the present invention, should be understood that these examples are only for illustrating the present invention and are not intended to limit the scope of the present invention, after having read the present invention, those skilled in the art will understand various aspects of the present invention All modifications of the valence form fall within the scope defined by the appended claims of the present application.

[0026] A robot path planning method based on the ANFIS fuzzy neural network. Firstly, a kinematics model is established for the mobile robot; with the help of the autonomous learning function of the neural network and the fuzzy reasoning ability of the fuzzy theory, a fuzzy neural network for mobile robot navigation is proposed. Network controller; based on the adaptive fuzzy neural network structure, a Takagi-Sugeno type fuzzy inference system is constructed and used...

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Abstract

The invention discloses a robot path planning method based on an ANFIS fuzzy neural network, and mainly solves the problems of complex trap path reciprocating and path redundancy in conventional reactive navigation. The method comprises the following steps: to begin with, establishing a kinematic model for a mobile robot; providing a mobile robot navigation controller based on the fuzzy neural network by means of autonomous learning function of the neural network and fuzzy reasoning ability of the fuzzy theory; constructing a Takagi-Sugeno fuzzy inference system based on an adaptive fuzzy neural network structure and serving the Takagi-Sugeno fuzzy inference system as a reference model for local reaction control of the robot; the fuzzy neural network controller outputting offset angle and operation speed in real time, and adjusting the migration direction of the mobile robot online to enable the mobile robot to be able to adjust speed automatically and approach the goal collisionless; and through an improved virtual target method, selecting an optimal path capable of allowing the robot to escape a trapping state.

Description

technical field [0001] The invention belongs to the technical field of robots, and in particular relates to path planning of mobile robots, which can be used for autonomous navigation of various mobile robots. Background technique [0002] The path planning problem is one of the key technologies of mobile robot navigation. The main task is to find an optimal or near-optimal collision-free path from the starting point to the target point in an environment with obstacles and according to certain performance indicators. . According to the difference of robot's awareness of environmental information, path planning can be divided into two types: global path planning with fully known environmental information and local path planning with completely unknown or partially unknown environmental information. Global path planning is generally carried out offline, and the commonly used methods mainly include intelligent algorithms such as visual map method, grid method, structure space ...

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Application Information

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IPC IPC(8): G05D1/02
CPCG05D1/0217
Inventor 程刚蒯墨深
Owner CHINA UNIV OF MINING & TECH
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