公开(公告)号：US11318611B2

公开(公告)日：2022-05-03

申请号：US16848189

申请日：2020-04-14

Applicant: FANUC CORPORATION

Inventor： Yu Zhao ,  Tetsuaki Kato

IPC: B25J9/16

Abstract: A method for controlling a robot to perform a complex assembly task such as insertion of a component with multiple pins or pegs into a structure with multiple holes. The method uses an impedance controller including multiple reference centers with one set of gain factors. Only translational gain factors are used—one for a spring force and one for a damping force—and no rotational gains. The method computes spring-damping forces from reference center positions and velocities using the gain values, and measures contact force and torque with a sensor coupled between the robot arm and the component being manipulated. The computed spring-damping forces are then summed with the measured contact force and torque, to provide a resultant force and torque at the center of gravity of the component. A new component pose is then computed based on the resultant force and torque using impedance controller calculations.

公开(公告)号：US20250026018A1

公开(公告)日：2025-01-23

申请号：US18356525

申请日：2023-07-21

Applicant: FANUC CORPORATION

Inventor： Yu Zhao ,  Tetsuaki Kato

IPC: B25J9/16

Abstract: A method and system for robot skill learning applicable to high precision assembly tasks employing a compliance controller. An actor-critic reinforcement learning controller is coupled to the compliance controller, where an actor neural network provides target position adjustment action data to the compliance controller based on robot state data feedback, and a critic neural network is used to train the actor. The critic neural network receives the robot state data feedback and reward data from the robot, along with the action data from the actor, and correlates optimal actions associated with states in order to maximize the reward. The critic then adjusts the parameters of the actor so that the actor produces effective actions in response to the state data, leading to rapid and reliable completion of the assembly task by the compliance controller/robot system. The critic is no longer used after the actor is adequately trained.

公开(公告)号：US11938633B2

公开(公告)日：2024-03-26

申请号：US17457753

申请日：2021-12-06

Applicant: FANUC CORPORATION

Inventor： Yu Zhao ,  Tetsuaki Kato

IPC: B25J9/16 ,  B23P21/00

CPC classification number: B25J9/1661 ,  B23P21/00

Abstract: A method for tuning the force control parameters for a general robotic assembly operation. The method uses numerical optimization to evaluate different combinations of the parameters for a robot force controller in a simulation environment that is built based on a real-world robotic setup. This method performs autonomous tuning for assembly tasks based on closed loop force control simulation, where random samples from a distribution of force control parameter values are evaluated, and the optimization routine iteratively redefines the parameter distribution to find optimal values of the parameters. Each simulated assembly is evaluated using multiple simulations including random part positioning uncertainties. The performance of each simulated assembly is evaluated by the average of the simulation results, thus ensuring that the selected control parameters will perform well in most possible conditions. Once the parameters have been optimized, they are applied to real robots to perform the actual assembly operation.

公开(公告)号：US20230173673A1

公开(公告)日：2023-06-08

申请号：US17457753

申请日：2021-12-06

Applicant: FANUC CORPORATION

Inventor： Yu Zhao ,  Tetsuaki Kato

IPC: B25J9/16 ,  B23P21/00

CPC classification number: B25J9/1661 ,  B23P21/00

Abstract: A method for tuning the force control parameters for a general robotic assembly operation. The method uses numerical optimization to evaluate different combinations of the parameters for a robot force controller in a simulation environment that is built based on a real-world robotic setup. This method performs autonomous tuning for assembly tasks based on closed loop force control simulation, where random samples from a distribution of force control parameter values are evaluated, and the optimization routine iteratively redefines the parameter distribution to find optimal values of the parameters. Each simulated assembly is evaluated using multiple simulations including random part positioning uncertainties. The performance of each simulated assembly is evaluated by the average of the simulation results, thus ensuring that the selected control parameters will perform well in most possible conditions. Once the parameters have been optimized, they are applied to real robots to perform the actual assembly operation.