SOFTWARE ARCHITECTURE FOR HIGH-SPEED TRAVERSAL OF PRESCRIBED ROUTES

• US 20080059015A1
• Filed: 06/11/2007
• Published: 03/06/2008
• Est. Priority Date: 06/09/2006
• Status: Abandoned Application

• Current Assignee: Carnegie Mellon University
• Original Assignee: Carnegie Mellon University
• Inventors: Hodge, Vanessa, Urmson, Chris, Snider, Jarrod, Peterson, Kevin, Whittaker, William
• Application Number:
  US11/761,362
• Publication Number:
  US 20080059015A1
• Time in Patent Office: Days
• US Class Current: 701/23
• CPC Class Codes: G05D 1/024 in combination with a laser lasers per se H01S
• CPC Class Codes: G05D 1/0257 using a radar radar systems designed for anti-collision purposes between land vehicles or between land vehicle and fixed obstacles G01S13/931
• CPC Class Codes: G05D 1/027 comprising intertial navigation means, e.g. azimuth detector inertial navigation G01C21/16; inertial navigation combined with non-inertial navigation instruments G01C21/165
• CPC Class Codes: G05D 1/0274 using mapping information stored in a memory device navigation using map-matching G01C21/30
• CPC Class Codes: G05D 1/0278 using satellite positioning signals, e.g. GPS
• CPC Class Codes: G08G 1/161 Decentralised systems, e.g. inter-vehicle communication
• CPC Class Codes: G08G 1/22 Platooning, i.e. convoy of communicating vehicles

Abstract

Systems, methods, and apparatuses for high-speed navigation. The present invention preferably encompasses systems, methods, and apparatuses that provide for autonomous high-speed navigation of terrain by an un-manned robot. By preferably employing a pre-planned route, path, and speed; extensive sensor-based information collection about the local environment; and information about vehicle pose, the robots of the present invention evaluate the relative cost of various potential paths and thus arrive at a path to traverse the environment. The information collection about the local environment allows the robot to evaluate terrain and to identify any obstacles that may be encountered. The robots of the present invention thus employ map-based data fusion in which sensor information is incorporated into a cost map, which is preferably a rectilinear grid aligned with the world coordinate system and is centered on the vehicle. The cost map is a specific map type that represents the traversability of a particular environmental area using a numeric value. The planned path and route provide information that further allows the robot to orient sensors to preferentially scan the areas of the environment where the robot will likely travel, thereby reducing the computational load placed onto the system. The computational ability of the system is further improved by using map-based syntax between various data processing modules of the present invention. By using a common set of carefully defined data types as syntax for communication, it is possible to identify new features for either path or map processing quickly and efficiently.

Claims

1. A method of autonomously navigating an environment by a robot, comprising the steps of:

collecting data about at least a portion of said environment using at least one sensor;

assessing said collected sensor data to identify obstacles within at least a portion of said environment;

assessing said collected sensor data to determine terrain roughness and terrain slope for at least a portion of said environment to establish a traversability rating for said portion of said environment;

generating a cost map of said environment incorporating said identified obstacles and said traversability rating;

planning a path through said environment using said cost map; and

traversing said environment.

2. The method of claim 1, wherein said at least one sensor is housed on said robot.

3. The method of claim 2, wherein said sensor is selected from the group consisting of RADAR-based sensor, LIDAR-based sensor, GPS-based sensor, and digital camera.

4. The method of claim 3, wherein said LIDAR-based sensor is housed in a gimbal.

5. The method of claim 4, wherein said gimbal is adapted to rotate and aim said LIDAR-based sensor.

6. The method of claim 3, wherein robot includes at least eight sensors.

7. The method of claim 6, wherein said five sensors include five LIDAR-based sensors, one RADAR-based sensor, one GPS-based sensor, and one digital camera.

8. The method of claim 2, wherein said at least one sensor is capable of being oriented.

9. The method of claim 8, wherein said at least one sensor is adapted to collect data about terrain directly in front of said robot.

10. The method of claim 1, further comprising estimating the pose of said robot.

11. The method of claim 10, wherein said pose of said robot includes information about robot orientation, location, and speed.

12. The method of claim 11, said pose of said robot is used to interpret said collected sensor data.

13. The method of claim 1, wherein said cost map is centered on said vehicle.

14. The method of claim 13, further comprising providing said cost map to a conformal planner.

15. The method of claim 14, wherein said conformal planner executes the planning of said path.

16. The method of claim 15, further comprising provide said path to a speed planner.

17. The method of claim 16, wherein said speed planner plans a speed for said robot.

18. The method of claim 17, further comprising a controlling step wherein said speed planner generates a series of commands to said robot to control said speed of said robot.

19. The method of claim 15, further comprising providing said path to a tracker.

20. The method of claim 19, further comprising a controlling step wherein said tracker employs said path to generate a series of commands to said robot to control said path of said robot.

21. The method of claim 20, wherein said series of commands includes steering commands.

22. The method of claim 15, wherein said conformal planner provides said path to a sensor pointer.

23. The method of claim 22, wherein said sensor pointer controls a gimbal.

24. The method of claim 23, wherein said gimbal houses a sensor.

25. The method of claim 24, wherein said sensor is a LIDAR-based sensor.

26. The method of claim 1, wherein said assessing said collected sensor data to identify obstacles step is accomplished by using at least one LIDAR-based sensor.

27. The method of claim 1, wherein said assessing said collected sensor data to identify obstacles step is accomplished by using at least one RADAR-based sensor.

28. The method of claim 1, wherein said assessing said collected sensor data to identify obstacles step is accomplished by using at least one LIDAR-based sensor and at least one RADAR-based sensor.

29. The method of claim 1, further comprising developing a pre-planned route, pre-planned path, and pre-planned speed prior to said robot being placed in said environment.

30. The method of claim 29, wherein said pre-planned route is generated using a series of waypoints.

31. The method of claim 30, wherein said waypoints are GPS coordinates.

32. The method of claim 29, wherein said pre-planned route defines said portion of said environment about which data is collected by said robot during said navigation.

33. The method of claim 30, further comprising interpolating said pre-planned path between said waypoints.

34. The method of claim 33, wherein said interpolating step employs splines.

35. The method of claim 34, wherein said splines are converted to tightly spaced waypoints.

36. The method of claim 35, wherein said tightly spaced waypoints are approximately 1 meter apart.

37. The method of claim 34, wherein a human modifies said splines.

38. The method of claim 29, wherein said planning of said pre-planned route, pre-planned path, and pre-planned speed includes estimating a risk of said environment.

39. The method of claim 38, wherein said estimating said risk includes information about terrain in said environment.

40. The method of claim 39, wherein said estimating said risk is performed by a human.

41. The method of claim 29, wherein said pre-planned route, pre-planned path, and pre-planned speed are used by said robot to orient said at least one sensor to collect information about at least a portion of said environment.

42. The method of claim 1, wherein said identifying is a binary process.

43. An apparatus for the autonomous navigation of an environment, comprising:

a chassis;

a plurality of sensors adapted to generate data about said environment;

an engine adapted to drive said apparatus;

a steering mechanism capable of steering said apparatus;

a gimbal, wherein said gimbal houses at least one of said plurality of sensors; and

at least one computer processor, wherein said computer processor is adapted to;

evaluate said sensor data to identify obstacles in said environment;

evaluate said sensor data to determine terrain roughness and terrain slope for at least a portion of said environment to establish a traversability rating for said portion of said environment; and

generate a cost map of said environment incorporating said identified obstacles and said traversability rating.

44. The apparatus of claim 43, wherein said plurality of sensors are selected from the group consisting of LIDAR-based sensor, RADAR-based sensor, GPS-based sensor, and digital camera.

45. The apparatus of claim 44, wherein said LIDAR-based sensor is a single-line scan LIDAR-based sensor.

46. The apparatus of claim 44, wherein said gimbal houses a LIDAR-based sensor.

47. The apparatus of claim 43, further comprising a brake controller.

48. The apparatus of claim 43, further comprising a throttle body controller, wherein said throttle body controller is adapted to control the state of the throttle.