Autonomous behaviors for a remote vehicle

• US 8,326,469 B2
• Filed: 07/16/2007
• Issued: 12/04/2012
• Est. Priority Date: 07/14/2006
• Status: Active Grant

• Current Assignee: Flir Detection, Inc. (Teledyne Technologies Incorporated)
• Original Assignee: iRobot Corporation
• Inventors: Phillips, Emilie, Powers, Aaron, Shein, Andrew, Jamieson, Josef P., Sawyer, Tyson
• Primary Examiner(s): Algahaim, Helal A
• Application Number:
  US11/826,486
• Publication Number:
  US 20080027590A1
• Time in Patent Office: 1,968 Days
• Field of Search: 701/2, 701/24, 701/29
• US Class Current: 701/2
• 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

Abstract

A method for enhancing operational efficiency of a remote vehicle using a diagnostic behavior. The method comprises inputting and analyzing data received from a plurality of sensors to determine the existence of deviations from normal operation of the remote vehicle, updating parameters in a reference mobility model based on deviations from normal operation, and revising strategies to achieve an operational goal of the remote vehicle to accommodate deviations from normal operation. An embedded simulation and training system for a remote vehicle. The system comprises a software architecture installed on the operator control unit and including software routines and drivers capable of carrying out mission simulations and training.

Claims

1. A method for operating a remote vehicle using a diagnostic behavior, the method comprising:

inputting and analyzing data received from a plurality of sensors to determine the existence of deviations from normal operation of the remote vehicle;

updating parameters, using diagnostic assembly, in reference mobility model comprising a computer model configured to predict vehicle performance for operation in a variety of environments and based on one or more of the parameters, the parameters being updated based on the determined deviations from normal operation; and

revising strategies to achieve an operational goal of the remote vehicle to accommodate the determined deviations from normal operation,

wherein the data regards the status of the remote vehicle'"'"'s components.

2. The method of claim 1, wherein the diagnostic behavior is persistent.

3. The method of claim 1, wherein the data received from the plurality of sensors includes preventative sensor data.

4. The method of claim 3, wherein the preventative sensor data includes changes in upcoming terrain.

5. The method of claim 1, wherein the data regards one or more of the remote vehicle'"'"'s external environment and the remote vehicle'"'"'s internal environment.

6. The method of claim 1, further comprising adding a payload capable of collecting the data and dedicated to overseeing data collection from designated sensors of the remote vehicle.

7. The method of claim 6, wherein processing circuits included in the payload are capable of outputting flags indicative of system faults when a sensed state persists for a predetermined period of time.

8. The method of claim 7, further comprising displaying system faults to an operator of the remote vehicle.

9. The method of claim 1, further comprising calculating system faults using sensors already on the vehicle.

10. The method of claim 1, further comprising synthesizing pre-existing sensor data to determine whether a system fault has occurred.

11. The method of claim 1, further comprising performing routines and inputting and analyzing responsive data received from predetermined sensors to determine whether a system fault has occurred.

12. The method of claim 11, wherein analysis and updating are performed by a diagnostic assembly of behavior-level software of the remote vehicle.

13. The method of claim 1, wherein deviations from normal operation include one or more of a flat tire, a low battery, low fuel, jammed tracks, broken flippers, malfunctioning sensors, an overloaded arm, extra payloads, and mud encumbering the vehicle.

14. The method of claim 1, further comprising altering control commands to actuators of the remote vehicle based on the reference mobility model.

15. The method of claim 1, wherein the reference mobility model is stored in the remote vehicle'"'"'s memory.