Robot motion replanning based on user motion

Patent metadata

Field	Value
Publication number	US-9452530-B2
Application number	US-201414485491-A
Country	US
Kind code	B2
Filing date	Sep 12, 2014
Priority date	Sep 12, 2014
Publication date	Sep 27, 2016
Grant date	Sep 27, 2016

Abstract

The disclosure includes a system and method for determining a robot path based on user motion by determining a current position of a robot with a processor-based computing device programmed to perform the determining, receiving sensor readings on positions, directions, and velocities of a visually-impaired user and other users, generating a model of the motions of the visually-impaired user and the other users, the model including a user path for the visually-impaired user and a robot path for the robot, generating a collision prediction map to predict collisions between at least one of the robot, the visually-impaired user, and the other users, determining whether there is a risk of collision for either the visually-impaired user or the robot, and responsive to the risk of collision, updating at least one of the user path and the robot path.

First claim

What is claimed is:

1. A method comprising:
determining a current position of a robot with a processor of the robot;
receiving sensor readings on positions, directions, and velocities of a visually-impaired user and other users who are not visually-impaired in an environment where the visually-impaired user is known from the other users;
generating a model of motions of the visually-impaired user and the other users, the model including a user path for the visually-impaired user and a robot path for the robot;
generating a collision prediction map to predict collisions between at least one of the robot, the visually-impaired user, and the other users;
determining whether there is a risk of collision for either the visually-impaired user or the robot; and
responsive to the risk of collision, updating at least one of the user path and the robot path based on the risk of collision;
wherein generating the model of the motions further comprises:
receiving torso-directed movement data for the visually-impaired user;
determining a face direction for the visually-impaired user;
determining a walking direction for the visually-impaired user;
determining whether the visually-impaired user exhibits low-consistency movement or high-consistency movement based on the torso direction and the walking direction; and
determining human motion uncertainty based on the consistency of movement.

2. The method of claim 1 , further comprising responsive to an absence of the risk of collision, providing directions to the visually-impaired user based on the user path.

3. The method of claim 1 , further comprising:
instructing the robot to follow the robot path with the processor; and
providing directions of the visually-impaired user based on the user path.

4. The method of claim 1 , wherein generating the model of the motions further comprises:
receiving torso-directed movement data for the other users who are not visually-impaired;
determining the face direction for the other users;
determining the walking direction for the other users;
determining whether the other users exhibit low-consistency movement or high-consistency movement based on the torso direction and the walking direction of the other users; and
determining human motion uncertainty for the other users based on the consistency of movement of the other users.

5. The method of claim 1 , further comprising using a probabilistic method to estimate a degree of human reaction by updating each variant scale parameter between a torso direction and current movement.

6. The method of claim 1 , wherein generating the collision prediction map further comprises:
generating pairs between the visually-impaired user and each of the other users;
determining a difference of arrival time between members of each pair; and
determining whether the difference of arrival time is less than a boundary parameter.

7. The method of claim 6 , wherein responsive to the difference of arrival time being less than the boundary parameter, further comprising:
increasing a collision probability; and
generating the collision map based on the collision probability for each pair.

8. The method of claim 6 , wherein responsive to the difference of arrival time being less than the boundary parameter, further comprising:
setting a collision probability to zero; and
generating the collision map based on the collision probability for each pair.

9. A computer program product comprising a non-transitory computer-usable medium including a computer-readable program, wherein the computer-readable program when executed on a computer causes the computer to:
determine a current position of a robot;
receive sensor readings on positions, directions, and velocities of a visually-impaired user and other users in an environment where the visually-impaired user is known from the other users, wherein the other users are not visually-impaired;
generate a model of motions of the visually-impaired user and the other users, the model including a user path for the visually-impaired user and a robot path for the robot;
generate a collision prediction map to predict collisions between at least one of the robot, the visually-impaired user, and the other users;
determine whether there is a risk of collision for either the visually-impaired user or the robot; and
responsive to the risk of collision, update at least one of the user path and the robot path based on the risk of collision;
wherein generating the model of the motions further comprises:
receiving torso-directed movement data for the visually-impaired user;
determining a face direction for the visually-impaired user;
determining a walking direction for the visually-impaired user;
determining whether the visually-impaired user exhibits low-consistency movement or high-consistency movement based on the torso direction and the walking direction; and
determining human motion uncertainty based on the consistency of movement.

10. The computer program product of claim 9 , wherein the computer-readable program is further configured to:
generate velocity constraints for the robot to follow a new path; and
instruct the robot to move to a goal position based on the new path and the velocity constraints.

11. The computer program product of claim 9 , wherein the computer-readable program is further configured to:
instruct, by the computer, the robot to follow the robot path; and
provide directions of the visually-impaired user based on the user path.

12. The computer program product of claim 9 , wherein generating the collision prediction map further comprises:
generating pairs between the visually-impaired user and each of the other users;
determining a difference of arrival time between members of each pair; and
determining whether the difference of arrival time is less than a boundary parameter.

13. The computer program product of claim 12 , wherein the computer-readable program is further configured to use a probabilistic method to estimate a degree of human reaction by updating each variant scale parameter between a torso direction and current movement.

14. The computer program product of claim 9 , wherein generating the model of motions further comprises:
receiving torso-directed movement data for the other users who are not visually-impaired;
determining the face direction for the other users;
determining the walking direction for the other users;
determining whether the other users exhibit low-consistency movement or high-consistency movement based on the torso direction and the walking direction of the other users; and
determining human motion uncertainty for the other users based on the consistency of movement of the other users.

15. A system comprising:
a processor; and
a non-transitory memory storing instructions that, when executed, cause the system to:
determine a current position of a robot;
receive sensor readings on positions, directions, and velocities of a visually-impaired user and other users in an environment where the visually-impaired user is known from the other users, wherein the other users are not visually-impaired;
generate a model of motions of the visually-impaired user and the other users, the model including a user path for the visually-impaired user and a robot path for the robot;
generate a collision prediction map to predict collisions between at least one of the robot, the visually-impaired user, and the other users;
determine whether there is a risk of collision for either the visually-impaired user or the robot; and
responsive to the risk of collision, update at least

Assignees

• Toyota Motor Co Ltd

Inventors

• Sisbot Emrah Akin
• Kim Young-Ho

Classifications

• G01S19/14

  specially adapted for specific applications · CPC title

• G01S5/0294

  Trajectory determination or predictive filtering, e.g. target tracking or Kalman filtering · CPC title

• G01S19/393

  Trajectory determination or predictive tracking, e.g. Kalman filtering · CPC title

• G06T7/20

  Analysis of motion (motion estimation for coding, decoding, compressing or decompressing digital video signals H04N19/43, H04N19/51) · CPC title

• G05B2219/40476

  Collision, planning for collision free path · CPC title