US 12,138,813 B2
Adaptive scouting using multi-legged robots
Yueqi Li, San Jose, CA (US); and Alexander Ngai, Irvine, CA (US)
Assigned to DEERE &CO., Moline, IL (US)
Filed by Deere & Company, Moline, IL (US)
Filed on May 10, 2022, as Appl. No. 17/741,102.
Prior Publication US 2023/0364796 A1, Nov. 16, 2023
Int. Cl. B25J 13/08 (2006.01); B25J 9/16 (2006.01); B25J 15/00 (2006.01); B25J 19/02 (2006.01); B62D 57/032 (2006.01); G06V 20/10 (2022.01); H04N 13/204 (2018.01); H04N 13/239 (2018.01); H04N 13/296 (2018.01)
CPC B25J 9/1697 (2013.01) [B25J 13/08 (2013.01); B25J 15/0066 (2013.01); B62D 57/032 (2013.01); G06V 20/188 (2022.01); H04N 13/204 (2018.05); H04N 13/296 (2018.05)] 20 Claims
OG exemplary drawing
 
1. A method implemented using one or more processors, the method comprising:
operating, based on a type and arrangement of a crop field, a multi-legged robot to travel along a trajectory through the crop field using a first gait, wherein the multi-legged robot includes one or more vision sensors and the first gait includes a first repeating cycle of poses of the multi-legged robot;
synchronizing operation of one or more of the vision sensors with one or more poses of the first repeating cycle of poses of the multi-legged robot to capture one or more initial sequences of images depicting one or more points-of-interest of crops growing in the crop field;
processing the one or more initial sequence of images using one or more phenotypic machine learning models to infer one or more phenotypic traits of the crops;
selecting a second gait based on one or more of the inferred phenotypic traits of the crops, wherein the second gait includes a second repeating cycle of poses of the multi-legged robot that is different than the first repeating cycle of poses of the multi-legged robot; and
operating the multi-legged robot to travel along a portion of the trajectory using the second gait.