US 12,222,727 B2
Robot control device, robot control method, and learning model generation device
Kei Ota, Tokyo (JP)
Assigned to MITSUBISHI ELECTRIC CORPORATION, Tokyo (JP)
Filed by Mitsubishi Electric Corporation, Tokyo (JP)
Filed on Jun. 9, 2022, as Appl. No. 17/836,542.
Application 17/836,542 is a continuation of application No. PCT/JP2020/008096, filed on Feb. 27, 2020.
Prior Publication US 2022/0300005 A1, Sep. 22, 2022
Int. Cl. G05D 1/00 (2024.01)
CPC G05D 1/0221 (2013.01) [G05D 1/0214 (2013.01)] 5 Claims
OG exemplary drawing
 
1. A robot control device comprising:
a computer processor;
a memory storing instructions executed by the computer processor to
acquire a moving route of a robot from a first learning model, by giving, to the first learning model, observation data indicating a position of an obstacle being present in a region where the robot moves and state data indicating a moving state of the robot at a movement start point where the robot starts moving among moving states of the robot in the region where the robot moves, the first learning model outputting route data including coordinate data of a next position on the moving route to which robot is to move from the movement start point, and
generate at least one control value for the robot, the at least one control value indicating an acceleration by which the robot is to move at a particular time to proceed along the acquired moving route,
wherein the computer processor gives, to a second learning model, state data indicating a moving state of the robot when the robot is moving in the region and the acquired moving route including the route data outputted by the first learning model, and acquires the at least one control value for the robot from the second learning model; and
a controller to control the robot based on the at least one control value.
 
4. A learning model generation device comprising:
a computer processor; and
a memory storing instructions executed by the computer processor to
generate teacher data indicating a route along which a robot can move in each of a plurality of regions without colliding with an obstacle being present in each of the regions;
generate a first learning model that learns a moving route of the robot by using observation data indicating a position of the obstacle being present in each of the plurality of regions, state data indicating a moving state of the robot at a movement start point at which the robot starts moving among moving states of the robot in each of the regions, and the generated teacher data, and outputs moving route data for the robot, when observation data indicating a position of an obstacle being present in a region where the robot moves and state data indicating a moving state of the robot at a movement start point at which the robot starts moving among moving states of the robot in the region where the robot moves are given, the moving route data including coordinate data of a next position on the moving route to which the robot is to move from the movement start point, and
generate a second learning model that
learns a control value for the robot by using state data indicating a moving state of the robot when the robot is moving in each of the regions and the generated teacher data, or the moving route data for the robot output from the first learning model, the control value indicating an acceleration by which the robot is to move; and
outputs the control value to a controller, when state data indicating a moving state of the robot when the robot is moving in a certain region and a moving route of the robot output from the first learning model are given, the controller using the control value to control the at least one of the speed and acceleration by which the robot moves at a particular time to proceed along the moving route.