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.
Patent No.:
Date of Patent:
Oct. 06, 2020
Filed:
Feb. 26, 2018
Applicants:
Mohsen Rohani, Gatineau, CA;
Jun Luo, Toronto, CA;
Song Zhang, Ottawa, CA;
Inventors:
Assignee:
Huawei Technologies Co., Ltd., Shenzhen, CN;
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2006.01); G06K 9/62 (2006.01); G06N 3/00 (2006.01); B25J 9/16 (2006.01); B60W 10/18 (2012.01); B60W 10/20 (2006.01); B60W 30/10 (2006.01); G05D 1/00 (2006.01); G05D 1/02 (2020.01); G06N 3/04 (2006.01); G06N 3/08 (2006.01); G06N 5/00 (2006.01); G06N 5/04 (2006.01);
U.S. Cl.
CPC ...
G06K 9/6262 (2013.01); B25J 9/161 (2013.01); B25J 9/162 (2013.01); B25J 9/163 (2013.01); B25J 9/1661 (2013.01); B25J 9/1664 (2013.01); B25J 9/1697 (2013.01); B60W 10/18 (2013.01); B60W 10/20 (2013.01); B60W 30/10 (2013.01); G05D 1/0088 (2013.01); G05D 1/027 (2013.01); G05D 1/0221 (2013.01); G05D 1/0246 (2013.01); G05D 1/0257 (2013.01); G06K 9/00201 (2013.01); G06K 9/00671 (2013.01); G06K 9/00791 (2013.01); G06K 9/627 (2013.01); G06N 3/008 (2013.01); G06N 3/04 (2013.01); G06N 3/0445 (2013.01); G06N 3/0454 (2013.01); G06N 3/08 (2013.01); B60W 2710/18 (2013.01); B60W 2710/20 (2013.01); G05D 2201/0213 (2013.01); G06K 9/00664 (2013.01); G06N 5/003 (2013.01); G06N 5/046 (2013.01); Y10S 901/01 (2013.01); Y10S 901/47 (2013.01);
Abstract
A multi layer learning based control system and method for an autonomous vehicle or mobile robot. A mission planning layer, behavior planning layer and motion planning layer each having one or more neural neworks are used to develop an optimal route for the autonomous vehicle or mobile robot, provide a series of functional tasks associated with at least one or more of the neural networks to follow the planned optimal route and develop commands to implement the functional tasks.