TRAINED HUMAN-INTENTION CLASSIFIER FOR SAFE AND EFFICIENT ROBOT NAVIGATION
First Claim
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, andwherein the human-intention classifier is trained to perform the prediction generation in an offline process completed prior to runtime of the mobile robot.
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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.
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Citations
20 Claims
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1. A mobile robot, comprising:
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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. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A method of creating a human-intention classifier for a mobile robot, comprising:
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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. - View Dependent Claims (11, 12, 13, 14)
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15. A method of navigating a robot through a space shared with one or more mobile humans, comprising:
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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. - View Dependent Claims (16, 17, 18, 19, 20)
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Specification