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:
Aug. 20, 2019

Filed:

May. 26, 2017
Applicants:

Boston Incubator Center, Llc, Waltham, MA (US);

East Japan Railway Company, Saitama-shi, Saitama-prefecture, JP;

Inventors:

Haruhiko Harry Asada, Lincoln, MA (US);

Kota Weaver, Allston, MA (US);

Baldin Lloren-Bonilla, Cambridge, MA (US);

Sheng Liu, Cambridge, MA (US);

Jun Maruyama, Minami-ku, JP;

Assignees:

Boston Incubator Center, LLC, Lexington, MA (US);

East Japan Railway Company, Saitama, JP;

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G05D 1/00 (2006.01); G05D 1/02 (2006.01); G08G 1/005 (2006.01);
U.S. Cl.
CPC ...
G05D 1/0088 (2013.01); G05D 1/0223 (2013.01); G05D 2201/0217 (2013.01); G08G 1/005 (2013.01);
Abstract

Methods and systems for navigating a mobile robot through a crowded pedestrian environment based on a trained navigation model are described herein. The trained navigation model receives measurement data identifying nearby pedestrians and the velocity of each nearby pedestrian relative to the mobile robot, and the current position and desired endpoint position of the mobile robot in the crowded pedestrian environment. Based on this information, the trained navigation model generates command signals that cause the mobile robot to adjust its velocity. By repeatedly sampling the velocities of surrounding pedestrians and current location, the navigation model directs the mobile robot toward the endpoint location with a minimum of disruption to the pedestrian traffic flows. The navigation model is trained to emulate desirable pedestrian walking behaviors in a crowded pedestrian environment by tracking the movement of a behavioral trainer through a crowded pedestrian environment.


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