Granted Patent B2

US 12,524,728 · App. 17/650,289 · Granted Jan 13, 2026

Inventors: Shubhashree Venkatesh (Fremont, CA); Noe Brito (Cupertino, CA); Yee-Ning Cheng (Sunnyvale, CA); Madhav Chhura (Whittier, CA); Sebastian Dovenor (Pittsburgh, PA); John Drake (Cranbury, NJ); Jonathan Pan (Campbell, CA); Jason Parraga (Fremont, CA); Scott Plant (San Jose, CA)

Assignee: Volkswagen Group of America Investments, LLC

G06Q10/083B60W50/0097B60W60/0011B60W60/00253B60W60/00256G01C21/36G01C21/3841G01C21/3856G01C21/387G06F16/285G06Q10/06311G08G1/20H04L9/3213H04L63/0807H04L63/0823H04L63/101H04L63/102H04L63/105H04L63/107B60W2552/00B60W2554/00B60W2556/40B60W2556/45

Abstract

Claims (67)

1 . A method for obtaining serviceable areas for a robotic system in a metropolitan area, comprising:

prior to generating a route for the robotic system in the metropolitan area, obtaining a map of the areas in the metropolitan area that are serviceable by the robotic system by:

obtaining, by a computing device, information including at least one of (i) a list of possible first places where the robotic system can route to and from in the metropolitan area, and (ii) a list of possible second places where the robotic system can stop in the metropolitan area;

determining whether there are any possible first or second places that should be removed from the list by using at least one of predicted high traffic area information and tenant permissions;

modifying the information by removing at least one possible first or second place from the list;

using, by the computing device, the modified information to generate clusters of places where the robotic system can route or stop in the metropolitan area, wherein at least one of said clusters comprises ones of the first and second places that have been grouped together based on a required minimum number of places of a same type as the first places and a required minimum number of places of a same type as the second places;

creating, by the computing device, a geometric shape associated with each said cluster, wherein each geometric shape has a boundary defined by outermost places contained in the cluster;

defining the serviceable areas for the robotic system within the metropolitan area by correlating coordinates of the geometric shape with map coordinates, identifying a map area indicating where the geometric shape resides relative to the metropolitan area, and considering the map area as being one of the serviceable areas; and

using, by the computing device, the serviceable areas to generate a map having a plurality of objects overlaid on geographic areas that each represent a portion of the metropolitan area where a concentrated number of the places exist;

identifying neighbor clusters between which a route exists for the robotic system;

selecting a particular route from a plurality of possible routes for the robotic system that exist between the neighbor clusters; and

updating the map to include the particular route between ones of the plurality of objects overlaid thereon that are associated with the neighbor clusters;

wherein:

the method further comprises classifying each of the first and second places as a core place, a border place or an outlier place;

the clusters comprise core places and border places;

each of the plurality of objects comprises a semi-transparent shape overlaid on the map;

the map includes first visual indicators for first and second places classified as core places and border places that reside under at least a portion of the overlaid semi-transparent shape, and includes different second visual indicators of ones of the first and second places classified as outlier places which reside outside of areas of the map overlaid by the semi-transparent shape.

2 . The method according to claim 1 , further comprising causing, by the computing device, the robotic system to be controlled based on contents of the map.

3 . The method according to claim 2 , wherein the robotic system comprises an autonomous vehicle.

4 . The method according to claim 3 , wherein the autonomous vehicle is caused to autonomously travel from a first place in the metropolitan area to a second different place in the metropolitan area.

5 . The method according to claim 1 , wherein the clusters are generated based on at least one of: a distance between two places where the robotic system can route or stop in the given metropolitan area, a minimum number of places required for a cluster, a minimum number of neighbor places for a given place where the robotic system can route or stop in the given metropolitan area, and a reachability of a place to all other neighboring places where the robotic system can route or stop in the given metropolitan area.

6 . The method according to claim 1 , wherein the clusters are generated using a density-based spatial clustering of applications with noise algorithm, a concave hull algorithm, or a convex hull algorithm.

7 . The method according to claim 1 , wherein the geometric shape is created using a Graham's scan algorithm.

8 . The method according to claim 1 , wherein said determining is further made by using at least one of road repair information, school zone information, pickup-drop off hour information, and environmental condition information.

9 . A system, comprising:

a processor;

a non-transitory computer-readable storage medium comprising programming instructions that are configured to cause the processor to implement a method for obtaining serviceable areas for a robotic system in a metropolitan area, wherein the programming instructions comprise instructions to:

prior to generating a route from the robotic system in the metropolitan area, obtain a map of serviceable areas in the metropolitan area that are serviceable by the robotic system by performing the following operations:

obtaining information including at least one of (i) a list of first places where the robotic system can route to and from in the metropolitan area and (ii) a list of second places where the robotic system can stop in the metropolitan area;

determining whether there are any possible first or second places that should be removed from the list by using at least one of predicted high traffic area information and tenant permissions;

modifying the information based on results of the determining;

using the modified information to generate clusters of places where the robotic system can route or stop in the metropolitan area, wherein at least one of said clusters comprises ones of the first and second places that have been grouped together based on a required minimum number of places of a same type as the first places and a required minimum number of places of a same type as the second places;

creating a geometric shape associated with each said cluster, wherein each geometric shape has a boundary defined by outermost places contained in the cluster;

defining the serviceable areas for the robotic system within the metropolitan area by correlating coordinates of the geometric shape with map coordinates, identifying a map area indicating where the geometric shape resides relative to the metropolitan area, and considering the map area as being one of the serviceable areas;

using the serviceable areas to generate a map displaying at least one geographic area representing a portion of the metropolitan area where a concentrated number of the places exist;

identify neighbor clusters between which a route exists for the robotic system;

select a particular route from a plurality of possible routes for the robotic system that exist between the neighbor clusters; and

update the map to include the particular route between ones of a plurality of objects overlaid thereon that are associated with the neighbor clusters:

wherein:

the method further comprises classifying each of the first and second places as a core place, a border place or an outlier place;

the clusters comprise core places and border places;

each of the plurality of objects comprises a semi-transparent shape overlaid on the map;

the map includes first visual indicators for first and second places classified as core places and border places that reside under at least a portion of the overlaid semi-transparent shape, and includes different second visual indicators of ones of the first and second places classified as outlier places which reside outside of areas of the map overlaid by the semi-transparent shape.

10 . The system according to claim 9 , wherein the programming instructions comprise instructions to cause the robotic system to be controlled based on contents of the map.

11 . The system according to claim 10 , wherein the robotic system comprises an autonomous vehicle.

12 . The system according to claim 11 , wherein the autonomous vehicle is caused to autonomously travel from a first place in the metropolitan area to a second different place in the metropolitan area.

13 . The system according to claim 9 , wherein the clusters are generated based on at least one of: a distance between two places where the robotic system can route stop in the given metropolitan area, a minimum number of places required for a cluster, a minimum number of neighbor places for a given place where the robotic system can route or stop in the given metropolitan area, and a reachability of a place to all other neighboring places where the robotic system can route or stop in the given metropolitan area.

14 . The system according to claim 9 , wherein the clusters are generated using a density-based spatial clustering of applications with noise algorithm, a concave hull algorithm, or a convex hull algorithm.

15 . The system according to claim 9 , wherein the geometric shape is created using a Graham's scan algorithm.

16 . The system according to claim 9 , wherein the determining is further made by using at least one of road repair information, school zone information, pickup-drop off hour information, and environmental condition information.

17 . A computer program product comprising a memory and programming instructions that are configured to cause a processor to:

prior to generating a route for a robotic system, obtain a map of areas in a metropolitan area that are serviceable by the robotic system by:

obtaining information specifying first places where a robotic system can route to and from in a metropolitan area and second places where the robotic system can stop in the metropolitan area;

determining whether there are any possible first or second places that should be removed from the list by using at least one of predicted high traffic area information and tenant permissions;

modifying the information based on results of the determining;

using the modified information to generate clusters of places where the robotic system can route or stop in the metropolitan area, wherein at least one of said clusters comprises ones of the first and second places that have been grouped together based on a required minimum number of places of a same type as the first places and a required minimum number of places of a same type as the second places;

creating a geometric shape for each said cluster, wherein each geometric shape has a boundary defined by outermost places contained in the cluster;

defining serviceable areas for the robotic system within the metropolitan area by correlating coordinates of the geometric shape with map coordinates, identifying a map area indicating where the geometric shape resides relative to the metropolitan area, and considering the map area as being one of the serviceable areas;

using the serviceable areas to generate a map displaying at least one geographic area representing a portion of the metropolitan area where a concentrated number of the places exist;

identifying neighbor clusters between which a route exists for the robotic system;

selecting a particular route from a plurality of possible routes for the robotic system that exist between the neighbor clusters; and

updating the map to include the particular route between ones of a plurality of objects overlaid thereon that are associated with the neighbor clusters;

wherein:

the method further comprises classifying each of the first and second places as a core place, a border place or an outlier place;

the clusters comprise core places and border places;

each of the plurality of objects comprises a semi-transparent shape overlaid on the map;

the map includes first visual indicators for first and second places classified as core places and border places that reside under at least a portion of the overlaid semi-transparent shape, and includes different second visual indicators of ones of the first and second places classified as outlier places which reside outside of areas of the map overlaid by the semi-transparent shape.

Assignments (2)

Continuity (3)

References Cited (109)