초록 ▼

Techniques and methodologies are disclosed for minimizing inaccuracies in distance measurements and location determinations for autonomous vehicles or targets ranging to subsets of beacons. Such techniques and methodologies can be used to better control (e.g., navigate) an autonomous vehicle in an a

Techniques and methodologies are disclosed for minimizing inaccuracies in distance measurements and location determinations for autonomous vehicles or targets ranging to subsets of beacons. Such techniques and methodologies can be used to better control (e.g., navigate) an autonomous vehicle in an area and/or along a pathway, or trajectory.

대표청구항 ▼

1. A method for traversing a trajectory by an autonomous vehicle, comprising: i) associating one or more beacons in one or more subsets with one or more waypoints of the trajectory by an operator or the autonomous vehicle, wherein the one or more beacons are associated in the one or more subsets bas

1. A method for traversing a trajectory by an autonomous vehicle, comprising: i) associating one or more beacons in one or more subsets with one or more waypoints of the trajectory by an operator or the autonomous vehicle, wherein the one or more beacons are associated in the one or more subsets based upon at least confidence indicators for range measurements derived from one or more beacons for one or more waypoints; wherein the confidence indicators are at least in part determined using a combination of signal strength, signal quality, and error values for range measurements; and further wherein one or more beacons comprising a subset of beacons for a waypoint have larger confidence indicators than other beacons when ranging with the autonomous vehicle at the waypoint;ii) ranging by the autonomous vehicle to the one or more beacons in the one or more subsets associated with the one or more waypoints that theautonomous vehicle is either located at, traveling toward, or traveling away from to determine the location of the autonomous vehicle; andiii) traversing the trajectory by the autonomous vehicle by using the location of the autonomous vehicle. 2. The method of claim 1, further comprising the autonomous vehicle acquiring confidence indicators from memory. 3. The method of claim 1, further comprising the autonomous vehicle communicating the confidence indicators. 4. The method of claim 1, wherein the confidence indicators are at least in part determined using a Euclidean norm calculation. 5. The method of claim 1, further comprising the autonomous vehicle storing the confidence indicators for later use or modification. 6. The method of claim 1, further comprising the autonomous vehicle calculating weighted values for one or more beacons by comparing confidence indicators of one or more beacons with other beacons for one or more waypoints, wherein the weighted values determine the influence that range measurements have in determining the location of the autonomous vehicle. 7. The method of claim 1, further comprising the autonomous vehicle prioritizing beacons by confidence indicators, wherein beacons with larger confidence indicators are prioritized over beacons with smaller confidence indicators. 8. The method of claim 1, further comprising the autonomous vehicle calculating voting values for one or more beacons corresponding to confidence indicators of range measurements derived from one or more beacons for one or more waypoints, wherein the voting determine which beacons comprise one or more subsets associated with one or more waypoints. 9. The method of claim 1, further comprising the autonomous vehicle calculating offset values for one or more beacons based upon error values of range measurements derived from one or more beacons for one or more waypoints, wherein the autonomous vehicle adjusts range measurements for the one or more beacons for the one or more waypoints based upon the offset values. 10. A method for an autonomous vehicle to traverse at least a portion of an area having a plurality of beacons, comprising: i) mapping the area into a plurality of cells by an operator or the autonomous vehicle;ii) associating one or more beacons in one or more subsets with one or more cells by the operator or the autonomous vehicle, wherein the one or more beacons are associated in the one or more subsets based upon at least confidence indicators for range measurements derived from one or more beacons for one or more cells; wherein the confidence indicators are at least in part determined using a combination of signal strength, signal quality, and error values for range measurements; and further wherein one or more beacons comprising a subset of beacons for a cell have larger confidence indicators than other beacons when ranging with the autonomous vehicle in the cell;ranging by the autonomous vehicle to the one or more beacons in the one or more subsets associated with the one or more cells that autonomous vehicle is located in to determine the location of the autonomous vehicle; andiv) traversing at least a portion of the area by the autonomous vehicle by using the location of the autonomous vehicle. 11. The method of claim 10, further comprising the autonomous vehicle acquiring confidence indicators from memory. 12. The method of claim 10, further comprising the autonomous vehicle communicating confidence indicators. 13. The method of claim 10, wherein the confidence indicators are at least in part determined using a Euclidean norm calculation. 14. The method of claim 10, further comprising the autonomous vehicle storing the confidence indicators for later use or modification. 15. The method of claim 10, further comprising the autonomous vehicle calculating weighted values for one or more beacons by comparing confidence indicators of one or more beacons with other beacons for one or more cells, wherein the weighted values determine the influence that the range measurements have in determining the position of the autonomous vehicle. 16. The method of claim 10, further comprising the autonomous vehicle prioritizing beacons by confidence indicators, wherein beacons with larger confidence indicators are prioritized over beacons with smaller confidence indicators. 17. The method of claim 10, further comprising the autonomous vehicle calculating voting values for one or more beacons corresponding to confidence indicators of range measurements derived from one or more beacons for one or more cells, wherein the voting values determine which beacons comprise one or more subsets associated with one or more cells. 18. The method of claim 10, further comprising the autonomous vehicle calculating offset values for one or more beacons based upon error values of range measurements derived from one or more beacons for one or more cells, where the autonomous vehicle adjusts range measurements for the one or more beacons for the one or more cells based upon the offset values. 19. A locating system, comprising: i) a plurality of beacons;ii) an autonomous vehicle equipped to communicate with the plurality of beacons;iii) a trajectory for the autonomous vehicle to traverse, said trajectory having one or more waypoints; andiv) subsets of beacons selected from the plurality of beacons, wherein beacons comprising a subset of beacons for a waypoint iv) . . . comprise confidence indicators for range measurements, wherein the confidence indicators are at least in part determined using a combination of signal strength, signal quality, and error values for range measurements; wherein the beacons comprising a subset of beacons for a waypoint have larger confidence indicators than other beacons when ranging to the autonomous vehicle at the waypoint;wherein the autonomous vehicle is located by data communicated between the autonomous vehicle and one or more of the subsets of beacons for a waypoint that the autonomous vehicle is either located at, traveling toward, or traveling away from, further wherein the subsets of beacons are selected based upon an algorithm. 20. The system of claim 19, further comprising one or more sectional maps forming cells wherein each cell is associated with confidence indicators for beacons, wherein the autonomous vehicle ranges to beacons having higher confidence indicators for a particular cell compared to other beacons in the particular cell while the autonomous vehicle is located within the particular cell.