TRAINED HUMAN-INTENTION CLASSIFIER FOR SAFE AND EFFICIENT ROBOT NAVIGATION

• US 20170190051A1
• Filed: 01/06/2016
• Published: 07/06/2017
• Est. Priority Date: 01/06/2016
• Status: Active Grant

• Current Assignee: Disney Enterprises Incorporated (The Walt Disney Company)
• Original Assignee: Disney Enterprises Incorporated (The Walt Disney Company)
• Inventors: O'SULLIVAN, CAROL ANN, PARK, CHONHYON, GILBERT, MAX L., ONDREJ, JAN, FREEMAN, KYLE G.
• Granted Patent: US 9,776,323 B2
• Time in Patent Office: Days
• CPC Class Codes: B25J 9/163 learning, adaptive, model based, rule based expert control
• CPC Class Codes: B25J 9/1666 Avoiding collision or forbidden zones
• CPC Class Codes: G05D 1/0088 characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours using knowledge based models G06N5/00
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: Y10S 901/01 Mobile robot
• CPC Class Codes: Y10S 901/46 Sensing device
• CPC Class Codes: Y10S 901/49 Protective device

Abstract

A trained classifier to be used with a navigation algorithm for use with mobile robots to compute safe and efficient trajectories. An offline learning process is used to train a classifier for the navigation algorithm (or motion planner), and the classifier functions, after training is complete, to accurately detect intentions of humans within a space shared with the robot to block the robot from traveling along its current trajectory. At runtime, the trained classifier can be used with regression based on past trajectories of humans (or other tracked, mobile entities) to predict where the humans will move in the future and whether the humans are likely to be blockers. The planning algorithm or motion planner generates trajectories based on predictions of human behavior that allow the robot to navigate amongst crowds of people more safely and efficiently.

Claims

1. A mobile robot, comprising:

a drive system operating to move the mobile robot in a workspace;

a robot controller transmitting control signals to the drive system to follow a trajectory through the workspace;

a motion planner periodically updating the trajectory; and

a human-intention classifier generating a prediction of a behavior for each of a set of mobile entities in the workspace,

wherein the motion planner performs the updating of the trajectory using the behavior predictions for the set of mobile entities, and

wherein the human-intention classifier is trained to perform the prediction generation in an offline process completed prior to runtime of the mobile robot.

2. The mobile robot of claim 1, wherein the behavior involves a behavior that blocks future movement of the mobile robot along the trajectory.

3. The mobile robot of claim 2, wherein the behavior predictions comprise a blocking score for each of the mobile entities defining a probability that the mobile entity will block travel of the mobile robot.

4. The mobile robot of claim 3, wherein the updating of the trajectory involves avoiding a subset of the mobile entities with a blocking score corresponding with blocking entities or moving toward one or more of the mobile entities with a blocking score corresponding with non-blocking entities.

5. The mobile robot of claim 3, wherein the offline process comprises:

gathering, while a robot moves along a goal trajectory through a space shared with a plurality of humans, a set of trajectories for the humans;

identifying a subset of the gathered trajectories for the humans as interfering with movement of the robot along the goal trajectory; and

assigning a blocking score indicating each of the subset of the gathered trajectories is associated with one of the humans that blocks the mobile robot.

6. The mobile robot of claim 5, wherein the prediction generation includes receiving a past trajectory for one of the mobile entities in the workspace and comparing the past trajectory with the subset of the gathered trajectories from the offline process to generate a blocking score for the one of the mobile entities associated with the past trajectory, whereby the past trajectory is used to provide the behavior prediction.

7. The mobile robot of claim 1, wherein the prediction generating and the updating of the trajectory are performed during runtime of the mobile robot.

8. The mobile robot of claim 1, wherein the motion planner computes a repulsive potential for each of the mobile entities based on the behavior prediction and generates a predicted trajectory in the workspace for each of the mobile entities using trajectory regression.

9. The mobile robot of claim 8, wherein the motion planner performs the updating of the trajectory based on the repulsive potentials and the predicted trajectories.

10. A method of creating a human-intention classifier for a mobile robot, comprising:

for a predefined time period, controlling a robot to move along a goal trajectory in a space shared with a plurality of humans;

during the predefined time period, recording trajectories of the robot and the humans; and

classifying each of the recorded trajectories for the humans as being associated either with blocking of the robot on the goal trajectory or non-blocking of the travel of the robot on the goal trajectory.

11. The method of claim 10, further including assigning a first blocking score to each of the recorded trajectories associated with the blocking and a second blocking score, differing from the first blocking score, to each of the recorded trajectories associated with the non-blocking of the travel of the robot.

12. The method of claim 11, wherein the recorded trajectories for the humans are associated with the blocking of the robot when the recorded trajectories correspond with a modification of a trajectory of the robot from the goal trajectory.

13. The method of claim 10, further comprising extracting from the recorded trajectories at least one variable from the group consisting of absolute position in the space, velocity, and acceleration, whereby the extracted variable is associated with the recorded trajectory for use in comparing tracked trajectories of a human to provide predictions of future behavior by the human.

14. The method of claim 10, further comprising extracting, relative to the recorded trajectories for the robot, from the recorded trajectories for the humans at least one variable from the group consisting of relative position in the space, relative velocity, and relative acceleration, whereby the extracted variable is associated with the recorded trajectory for use in comparing tracked trajectories of a human to provide predictions of future behavior by the human.

15. A method of navigating a robot through a space shared with one or more mobile humans, comprising:

positioning a mobile robot in a space;

first controlling the mobile robot to move along a first trajectory toward a goal location in the space;

determining a past trajectory of a human in the space;

assigning a blocking score to the past trajectory for the human by comparing the past trajectory to a set of prerecorded trajectories each being pre-classified with a probability of blocking behavior; and

second controlling the mobile robot to move along either the first trajectory or along a second trajectory toward the goal location in the space, wherein the second trajectory is computed based on the blocking score.

16. The method of claim 15, wherein the set of prerecorded trajectories are associated with a first set of trajectories for humans moving within a test space identified as blocking travel of a test robot concurrently moving in the test space and with a second set of trajectories for humans in the test space identified as not blocking travel of the test robot.

17. The method of claim 16, wherein the past trajectory includes at least one of variable in the group consisting of absolute position, velocity, acceleration, position relative to the mobile robot, velocity relative to the mobile robot, and acceleration relative to the mobile robot.

18. The method of claim 17, wherein a value of the at least one variable is compared with a value of a matching variable extracted from the trajectories in the first and second sets.

19. The method of claim 15, wherein the second trajectory is computed based on a repulsive potential value assigned to the human and wherein the repulsive potential value being computed based on the blocking score.

20. The method of claim 19, wherein the second trajectory is computed to avoid the human in the space when the blocking score is greater than a first predefined value or to move toward the human in the space when the blocking score is less than a second predefined value.