Multi-neural network control planning method for robot path in intelligent environment

An intelligent environment and network control technology, applied in two-dimensional position/course control, vehicle position/route/altitude control, non-electric variable control, etc., can solve the problems of not being able to obtain the global optimal solution and falling into local optimal solutions

Active Publication Date: 2017-10-20
CENT SOUTH UNIV
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Problems solved by technology

For example, Chinese patent CN101604166B discloses a mobile robot path planning method based on particle swarm optimization algorithm. Converge early and fall into local optimum
Another example is Chinese patent CN105116902A, which di

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  • Multi-neural network control planning method for robot path in intelligent environment
  • Multi-neural network control planning method for robot path in intelligent environment
  • Multi-neural network control planning method for robot path in intelligent environment

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Embodiment Construction

[0071] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0072] The flow chart of the path planning of the carrier robot is as follows: figure 1 shown.

[0073] A multi-neural network control planning method for a robot path in an intelligent environment, comprising the following steps:

[0074] Step 1: Construct a three-dimensional coordinate system of the global map for the carrying area of ​​the carrier robot, and obtain the coordinates of the walkable area under the three-dimensional coordinate system of the global map;

[0075] The ground center point of the carrying area is the origin, the due east direction is the X axis, the true north direction is the Y axis, and the vertical ground direction is the Z axis;

[0076] The carrying area of ​​the carrying robot is all the floor connecting areas in a building, and the walkable area refers to the area where obstacles in the building are deleted from all ...

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Abstract

The invention provides a multi-neural network control planning method for a robot path in an intelligent environment. The method comprises the steps that 1 a global map three-dimensional coordinate system is constructed for the carrying area of a carrier robot to acquire a walkable area coordinate in the global map three-dimensional coordinate system; 2 a training sample set is acquired; 3 the global static path planning model of the carrier robot is constructed; and 4 starting and ending coordinates in a transportation task are input into the global static path planning model based on a fuzzy neural network to acquire the corresponding optimal planning path for the carrier robot. According to the invention, the global static path planning model and a local dynamic obstacle avoidance planning model are separately established; the nonlinear fitting property of the neural network is used to find the global optimal solution quickly; and the problem of falling into a local optimum in common path planning is avoided.

Description

technical field [0001] The invention belongs to the field of robot path planning, in particular to a multi-neural network control planning method for robot paths in an intelligent environment. Background technique [0002] With the in-depth development of Made in China 2025, there are more and more applications of robots in smart environments such as smart medical care, smart factories, and smart laboratories. The degree of intelligence of robots determines the automation level of production and scientific research activities and the development level of productivity. Among them, the robot path planning problem is the key to improving the robot's carrying efficiency. How to efficiently and optimally solve the robot path planning problem has always been a difficult point in this field. [0003] Predecessors have proposed many excellent path planning methods, including early visualization method, artificial potential field method, grid method, and later ant colony algorithm, g...

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Application Information

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IPC IPC(8): G05D1/02
CPCG05D1/0221G05D2201/0216
Inventor 李燕飞刘辉黄家豪金楷荣
Owner CENT SOUTH UNIV
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