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Robot autonomous exploration method and system based on deep learning

A deep learning and robotics technology, applied in the field of robotics, can solve problems such as large boundaries, poor flexibility in map construction, and difficulty in obtaining information, and achieve the effect of increasing speed

Pending Publication Date: 2022-07-12
SHANDONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] (1) Low exploration efficiency
When performing large-scale mapping, extracting the map boundary requires a lot of computing resources, and it is difficult to obtain the boundary with a large amount of information, resulting in low efficiency of robot exploration
[0006] (2) The map integrity is low
Due to the limitations of the area selected by the target point when the robot is performing exploratory mapping, the final environmental map drawn is incomplete
[0007] (3) Poor map building flexibility
When the robot selects the target point, it is easy to select a point that is too close to the obstacle, which makes it difficult for the robot to reach the target point

Method used

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  • Robot autonomous exploration method and system based on deep learning
  • Robot autonomous exploration method and system based on deep learning
  • Robot autonomous exploration method and system based on deep learning

Examples

Experimental program
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Embodiment 1

[0049] In order to achieve high-precision indoor scene mapping, a deep learning-based robot autonomous exploration method is disclosed in this embodiment, including:

[0050] Obtain the coordinate points of the robot and the current indoor scene graph constructed;

[0051] Determine the boundary of the current indoor scene graph through a deep learning network;

[0052] Select the boundary center closest to the robot coordinate point as the candidate point;

[0053] Generate tree-like paths between robot coordinate points and candidate points through fast growing random tree algorithm;

[0054] Select the penultimate node on the tree path as the target point;

[0055] Generate a global path based on robot coordinate points and target points;

[0056] Drive the robot to move along the global path at a set speed to acquire new laser point cloud data;

[0057] The current indoor scene map is updated through the new laser point cloud data to complete the indoor scene mapping. ...

Embodiment 2

[0111] In this embodiment, a robot autonomous exploration system based on deep learning is disclosed, including:

[0112] The data acquisition module is used to acquire the coordinate points of the robot and the current indoor scene graph constructed;

[0113] The target selection module determines the boundary of the current indoor scene graph through the deep learning network; selects the boundary center closest to the robot coordinate point as the candidate point; generates a tree-like path between the robot coordinate point and the candidate point through the fast growing random tree algorithm; select The penultimate node on the tree path is the target point;

[0114] The path planning module is used to generate a global path according to the robot coordinate points and target points;

[0115] The robot drive module is used to drive the robot to move along the global path at a set speed to obtain new laser point cloud data;

[0116] The mapping module is used to update t...

Embodiment 3

[0118] In this embodiment, an electronic device is disclosed, which includes a memory and a processor, and computer instructions stored in the memory and executed on the processor. When the computer instructions are executed by the processor, the based on Steps described in a deep learning approach to autonomous robotic exploration.

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Abstract

The invention discloses a robot autonomous exploration method and system based on deep learning. The robot autonomous exploration method comprises the steps that robot coordinate points and a constructed current indoor scene graph are acquired; determining the boundary of the current indoor scene graph through a deep learning network; selecting a boundary center closest to the coordinate point of the robot as a candidate point; generating a tree-shaped path between the robot coordinate point and the candidate point through a fast growth random tree algorithm; selecting the last but one node on the tree-shaped path as a target point; generating a global path according to the robot coordinate point and the target point; driving the robot to move along the global path at a set speed to obtain new laser point cloud data; the current indoor scene graph is updated through the new laser point cloud data, and indoor scene mapping is completed. And high-precision indoor scene mapping is realized.

Description

technical field [0001] The present invention relates to the field of robotics, in particular to a method and system for autonomous exploration of robots based on deep learning. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] Mobile robots have the advantages of flexible action, convenient operation and strong robustness, and have broad application prospects in medical, military, aerospace, logistics and other fields. Autonomous exploration of mobile robots means that robots in an unknown environment, through autonomous movement and environmental perception, finally build a complete environmental map. Robotic autonomous exploration can help people achieve map reconstruction of complex terrain, greatly reducing extreme environmental conditions. (such as small size, high temperature, etc.) adversely affect people, which provides convenience...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05D1/02
CPCG05D1/024G05D1/0214G05D1/0221
Inventor 王超群王银川赵昊宁荣学文宋锐
Owner SHANDONG UNIV
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