The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jul. 01, 2025

Filed:

Sep. 27, 2023
Applicant:

Dalian University of Technology, Liaoning, CN;

Inventors:

Xin Yang, Liaoning, CN;

Xiaopeng Wei, Liaoning, CN;

Yang Wang, Liaoning, CN;

Qiang Zhang, Liaoning, CN;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G05D 1/00 (2024.01); G05D 1/02 (2020.01); G06V 10/80 (2022.01); G06V 10/82 (2022.01); G06V 20/58 (2022.01);
U.S. Cl.
CPC ...
G05D 1/0088 (2013.01); G05D 1/0214 (2013.01); G05D 1/0248 (2013.01); G06V 10/80 (2022.01); G06V 10/82 (2022.01); G06V 20/58 (2022.01);
Abstract

The present invention provides a robot dynamic obstacle avoidance method based on a multimodal spiking neural network. The present invention realizes a robot obstacle avoidance method in a dynamic environment by fusing laser radar data and processed event camera data and combining with the intrinsic learnable threshold of the spiking neural network for a scenario comprising dynamic obstacles. It solves the difficulty of failure of obstacle avoidance due to the difficulty in perceiving the dynamic obstacles in the obstacle avoidance task of a robot. The present invention helps the robot to fully perceive the static information and the dynamic information of the environment, uses the learnable threshold mechanism of the spiking neural network for efficient reinforcement learning training and decision making, and realizes autonomous navigation and obstacle avoidance in the dynamic environment. An event data enhanced model is combined to better adapt to the dynamic environment for obstacle avoidance.


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