ROUTE SEARCH PLANNER

• US 20100274487A1
• Filed: 07/12/2010
• Published: 10/28/2010
• Est. Priority Date: 05/17/2006
• Status: Active Grant

• Current Assignee: Chang-Shaung R. Lee, Michael G. Neff, Shirley N. Cheng, Ted L. Johnson
• Original Assignee: Chang-Shaung R. Lee, Michael G. Neff, Shirley N. Cheng, Ted L. Johnson
• Inventors: Neff, Michael G., Cheng, Shirley N., Johnson, Ted L., Lee, Chang-Shaung R.
• Granted Patent: US 9,127,913 B2
• Time in Patent Office: Days
• US Class Current: 701/302
• CPC Class Codes: F41G 7/343 comparing observed and stored data of target position or of distinctive marks along the path towards the target

Abstract

Route search planner methods and systems are described. In an embodiment, a probability map can be generated from previous sensor scans combined with a projected target location of relocatable targets in a target area. A route can be generated by a route generator, based at least in part on the probability map, and based on optimal system performance capabilities utilized to search for at least one of the relocatable targets. A search manager can then assign an evaluation criteria value to the route based on route evaluation criteria, and compare the evaluation criteria value to other evaluation criteria values corresponding to respective previously generated routes to determine an optimal route. The search manager can then determine whether to generate one or more additional routes and assign additional evaluation criteria values for comparison to determine the optimal route.

Claims

3. (canceled)

4. (canceled)

8-20. -20. (canceled)

21. A method of planning a route for a vehicle utilizing onboard sensors during a mission, the method comprising:

(a) starting with an initial route, generating a plurality of possible new routes that the vehicle could follow during a mission;

(b) for each possible new route, determining a contribution towards mission accomplishment that would result from utilization of the sensors along;

(c) using the contributions to select one of the possible new routes, where the selected route results in a larger contribution towards mission accomplishment;

(d) returning to step (a) but using the selected route to generate a plurality of possible new routes;

whereby a route that best accomplishes the mission is iteratively produced.

1. The method of claim 21, wherein:

generating the plurality of possible new routes includes generating a probability map from previous sensor scans combined with a projected target location of one or more relocatable targets in a target area; and

generating a new routes by which to search for at least one of the relocatable targets, the new routes being generated based at least in part on the probability map; and

determining the contribution includes assigning an evaluation criteria value to the route new routes based on route evaluation criteria, the evaluation criteria value being comparable to one or more evaluation criteria values corresponding to respective previously generated routes to determine an optimal route.

2. The method of claim 1, wherein each new route is generated as a flight path for an airborne platform to search and locate the at least one relocatable target.

5. The method of claim 1, wherein the probability map is generated at least in part from the previous sensor scans of a region in the target area, and wherein each new route is generated based at least in part on the probability map, and based on at least one of an initial route heuristic;

a distance offset.

6. The method of claim 1, further comprising developing the projected target location based on target characteristics combined with a previously known target location projected into the future by a future time input.

7. The method of claim 6, further comprising:

receiving a targeting input as at least one of;

a sensor scan input;

a data link input; and

determining the previously known target location from the targeting input.

22. The method of claim 21, wherein the vehicle also includes autonomous target recognition capability, and wherein the contribution towards mission accomplishment results from a probability-based prediction of onboard sensor and autonomous target recognition performance.

23. The method of claim 21, wherein the vehicle also includes commit logic containing mission-specific criteria for determining whether the vehicle can commit to a particular target, including at least one of the likelihood the vehicle can reach an object, the likelihood the object is of a mission-desired type, and the likelihood the object is the desired object;

and wherein the contribution towards mission accomplishment results from a probability-based prediction of onboard sensor performance and commit logic processing.

24. The method of claim 21, wherein determining the contribution includes generating a mission probability model of uncertainty from the previous scans, and determining a conditional probability of accomplishing the mission given the new possible route.

25. The method of claim 24, wherein the model includes estimated types and kinematics of objects in the vehicle'"'"'s environment as well as the ability to project the current model, and its uncertainty, into a future time.

26. The method of claim 24, wherein accomplishing the mission includes searching, recognizing, and committing to a target in a region;

and wherein the probability model includes representations of the target in the region.

27. The method of claim 21, wherein the vehicle is an airborne platform, and wherein the routes are flight paths for the airborne platform.

28. The method of claim 21, wherein determining a contribution towards mission accomplishment is based on a model that characterizes new information gained from utilizing the on-board sensors and also information gained from previous sensor scans.

29. The method of claim 21, wherein at step (a) a route generator is re-initialized with a selected route that has a larger probability of mission accomplishment.