Robot passing planning method and device, robot and storage medium

Abstract

The invention discloses a robot passing planning method and device, a robot and a storage medium. The method comprises the steps of constructing an obstacle grid map which is composed of grids, the grids carrying grid contents, and the grid contents at least comprising at least one of the following: an obstacle type, a grid type or height detection data; and planning a walking path of the robot according to the grid contents in the obstacle grid map. The walking path can be planned in combination with the content of each grid, and the passing success rate of the robot can be improved.

Description

technical field [0001] The present application relates to the technical field of robot traffic, and in particular to a robot traffic planning method, device, robot and storage medium. Background technique [0002] With the rapid development of science and technology and the continuous improvement of people's living standards, more and more robots are entering people's lives and work. For example, cleaning robots can automatically complete floor cleaning in the room with certain artificial intelligence. However, the current robot simply analyzes the presence or absence of obstacles on the map when passing, and in the path planning process, the obstacle area is not directly included in the plan, but in fact some obstacles can pass, such as the threshold , some depressions, etc. Therefore, without evaluating the difficulty of obstacles, the obstacle area is directly not included in the plan, and it is easy to ignore some passable paths, resulting in a low pass success rate of t...

AI Technical Summary

Problems solved by technology

However, the current robot simply analyzes the presence or absence of obstacles on the map when passing, and in the path planning process, the obstacle area is not directly included in the plan, but in fact some obstacles can pass, such as the threshold , some depressions, etc. Therefore, the difficulty level of obstacles is not evaluated, and the obstacle area is directly not included in the plan, which is prone to ignore some passable paths, resulting in a low pass success rate of the robot

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