US 12,440,983 B1
Learning-embedded motion planning
Yongxiang Fan, Palo Alto, CA (US); Te Tang, Fremont, CA (US); and Yiyang Zhou, Hayward, CA (US)
Assigned to Anyware Robotics Inc., Fremont, CA (US)
Filed by Anyware Robotics Inc., Fremont, CA (US)
Filed on Feb. 1, 2024, as Appl. No. 18/429,763.
Claims priority of provisional application 62/430,042, filed on Dec. 4, 2022.
Int. Cl. B25J 9/16 (2006.01); B25J 19/02 (2006.01)
CPC B25J 9/1664 (2013.01) [B25J 9/161 (2013.01); B25J 9/162 (2013.01); B25J 9/163 (2013.01); B25J 9/1671 (2013.01); B25J 19/023 (2013.01)] 16 Claims
OG exemplary drawing
 
1. A method of sequential handling of a plurality of freight units by an end effector of a robotic system, the method comprising:
training, by a programmed computer system, at least one neural network operable to bias a plurality of samples of a configuration space of the end effector; and
after training of the at least one neural network, deploying the neural network, wherein deploying the at least one neural network comprises:
sensing, by a computer system of the robotic system, an arrangement of freight units in a scene based on an image of the scene from a sensor;
choosing, by the computer system of the robotic system, a freight unit based on the at least one trained neural network;
choosing, by the computer system of the robotic system, a starting pose of the robotic end effector as a means to pick the chosen freight unit;
choosing, by the computer system of the robotic system, a goal pose of the robotic end effector as a means to place the chosen freight unit;
planning, by the computer system of the robotic system, a motion of the robotic end effector from the starting pose to the goal pose based on a biased plurality of samples; and
controlling, by the computer system of the robotic system, the motion on the end effector based on the planning.
 
9. A system of sequential handling of a plurality of freight units by an end effector of a robotic system, the system comprising:
a first computer system for training a neural network operable to bias a plurality of samples of a configuration space of the end effector; and
the robotic system comprising a sensor, a robot computer system, and the end effector, wherein:
the sensor is for capturing an image of freight units in a scene; and
the robot computer system is configured to:
sense an arrangement of the freight units in the scene based on the image;
choose a freight unit based on the trained neural network;
choose a starting pose of the robotic end effector as a means to pick the chosen freight unit;
choose a goal pose of the robotic end effector as a means to place the chosen freight unit;
plan a motion of the end effector from the starting pose to the goal pose; and
control the motion on the robotic end effector based on the planning.