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Complete coverage path planning method for multi-robot system

A multi-robot, traversal path technology, applied in the field of information technology and robotics, can solve the problems of low activity value and inability to escape from deadlock areas, etc., achieve the effect of improving coverage efficiency, achieving coverage efficiency, and reducing coverage time

Active Publication Date: 2017-07-25
PEKING UNIV SHENZHEN GRADUATE SCHOOL +1
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AI Technical Summary

Problems solved by technology

However, when the next node cannot walk (for example, in a deadlock situation), the robot must wait until the activity value decays to a lower value than the surrounding nodes before walking out
On the other hand, methods based on biologically inspired neural networks cannot guarantee that the path to escape from the deadlock region is optimal

Method used

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Embodiment Construction

[0022] The present invention will be described in further detail below through specific embodiments and accompanying drawings.

[0023] The robot in the present invention works in a variable environment, and what is considered here is the path planning problem in a two-dimensional environment. The entire workspace is divided into small squares (grids). The size of each grid (neuron in the bioinspired neural network) is equal to the size of the robot's actuators. For a sweeping robot, the side length of the grid is equal to the diameter of the chassis of the sweeping robot, which is known. However, the dynamically changing environment information is unknown at the beginning, and the robot needs to detect it step by step through sensors during the walking process.

[0024] figure 1 It is a flow chart of the method for planning a complete traversal path for a multi-robot system of the present invention. Here the workspace is treated as a raster map. Each robot perceives envi...

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Abstract

The present invention discloses a complete coverage path planning method for a multi-robot system. This method combines a biologically inspired neural network model with a backtracking mechanism to reduce the repeated coverage path and shorten the time needed by complete coverage. The method concretely comprises the following steps: every robot uses the biologically inspired neural network model to cover surrounding environment until the robot enters a deadlock state, robots use the backtracking mechanism to select an optimum backtracking node without waiting for the attenuation of a neuron activity value, and a dynamic A star algorithm is used to plan out a path to the backtracking node; and the robots continue to enter a coverage mode after moving to the backtracking node. When one of the robots is deadlocked, the backtracking mechanism of the invention chooses the most suitable backtracking node according to a market mechanism, and the bidding process in the market mechanism considers the path length of the robot during backtracking and the conflict relationship with other robots, so the total time needed by the complete coverage is greatly shortened.

Description

technical field [0001] The invention belongs to the fields of information technology and robot technology, and relates to a path planning method for a mobile robot, in particular to a full-coverage path planning method for a multi-robot system. Background technique [0002] Full-coverage path planning requires the robot to pass through every location in the workspace while avoiding obstacles. Both single-robot and multi-robot full-coverage path planning have a wide range of applications, such as household cleaning robots, demining robots, lawn mowing robots and agricultural automatic harvesters, etc. [0003] There have been many solutions for full traversal path planning methods in static environments, and these solutions are also considered as offline algorithms for known environments. However, in a real application environment, it is unrealistic to know the environment information that does not change in advance. Therefore, online algorithms that use sensors to sense th...

Claims

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Application Information

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IPC IPC(8): G01C21/20
CPCG01C21/20
Inventor 刘宏王芷张国栋宋章军
Owner PEKING UNIV SHENZHEN GRADUATE SCHOOL
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