Intelligent trolley obstacle avoidance method based on fuzzy neural network

A fuzzy neural network and smart car technology, applied in the field of robotics, can solve problems such as poor global path planning ability, deadlock state, etc.

Inactive Publication Date: 2021-04-09
SOUTH CHINA UNIV OF TECH +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the single intelligent obstacle avoidance algorithm also has certain shortcomings, such as poor global path planning ability, easy to fall into deadlock state under local minimum point, etc.

Method used

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  • Intelligent trolley obstacle avoidance method based on fuzzy neural network
  • Intelligent trolley obstacle avoidance method based on fuzzy neural network
  • Intelligent trolley obstacle avoidance method based on fuzzy neural network

Examples

Experimental program
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Embodiment

[0079] Such as figure 1 Shown, the present invention, a kind of intelligent car obstacle avoidance method based on fuzzy neural network, comprises the following steps:

[0080] S1. Define input and output variables, specifically:

[0081] Define 6 input variables d 1 、d 2 、d 3 、d 4 、d 5 and θ, respectively represent the distance from the left side, left front side, front, right front side, right side of the smart car to the obstacle and the deflection angle of the smart car. where d 1 、d 2 、d 3 、d 4 and d 5 It is obtained by the ranging sensor installed on the smart car, and θ is measured by the angle sensor installed on the smart car. The output parameter is set as the deflection angle of the smart car, represented by TG.

[0082] In this example, if figure 2 As shown, the smart car includes a sensor detection module, a main control core, a motion module and a power module, and the sensor detection module includes a distance sensor and an angle sensor; the motio...

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Abstract

The invention discloses an intelligent trolley obstacle avoidance method based on a fuzzy neural network. The method comprises the following steps: defining input parameters and output parameters of the fuzzy neural network; a fuzzy neural network is determined, the fuzzy neural network comprises an input layer, a fuzzification layer, a fuzzy reasoning layer, a normalization layer and a defuzzification layer, and each layer comprises a plurality of neuron nodes; training a fuzzy neural network, and determining and optimizing parameters of the fuzzy neural network; and the fuzzy neural network is used for intelligent trolley obstacle avoidance. The intelligent trolley is provided with the distance sensor and the angle sensor, wherein the main control core of the intelligent trolley adopts the fuzzy neural network to perform obstacle avoidance control, and the fuzzy neural network can perform fusion processing on information obtained by the distance sensor and the angle sensor, so that more accurate environmental information is obtained; and thereby, a safer and more reliable obstacle avoidance control command is obtained, so that obstacle avoidance of the intelligent trolley is realized.

Description

technical field [0001] The invention belongs to the technical field of robots, and in particular relates to an obstacle avoidance method for an intelligent car based on a fuzzy neural network. Background technique [0002] As a typical representative of wheeled mobile robots, smart cars can perform tasks that humans cannot or are difficult to complete in complex and harsh environments because of their simple mechanical structure, light weight, small size, low noise, and fast driving speed. , has attracted attention in more and more fields. Extensive social, military and economic needs highlight the urgency of the current research on intelligent unmanned vehicle technology. The obstacle avoidance function is one of the signs of the intelligence of the unmanned car, and the quality of the obstacle avoidance effect seriously affects the level of intelligence of the unmanned car. In order to prevent the smart car from hitting obstacles in front or on the left and right sides w...

Claims

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Application Information

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IPC IPC(8): G05B13/04G05D1/02
CPCG05B13/0285G05B13/042G05D1/0088G05D1/0221G05D1/0242G05D1/0255G05D1/0268
Inventor 屈盛官吕继亮赵馨雨马涛高红云夏雨萌
Owner SOUTH CHINA UNIV OF TECH
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