A method for determining a control position of a robot includes (a) acquiring N real reference positions in a real space and N control reference positions in a robot control coordinate system, (b) setting M figures having vertices in a plurality of real reference positions of the N real reference positions within the real space, and obtaining a transform function expressing a correspondence relationship between a real position and a control position within each figure, (c) receiving input of a target position of the control point in the real space, (d) selecting an object figure for calculation of a control position for the target position from the M figures, and (e) calculating a target control position for the target position using the transform function with respect to the object figure.

A robot system includes a robot arm, a driving control section configured to control driving of the robot arm based on position information output by a first position detecting section and a second position detecting section, a monitoring section configured to determine, based on the position information, whether the operation of the robot arm is normal, a first communication line for coupling the driving control section and the first position detecting section and coupling the driving control section and the second position detecting section to perform half duplex communication, and a second communication line for coupling the monitoring section and the driving control section, coupling the monitoring section and the first position detecting section, coupling the monitoring section and the second position detecting section to perform the half duplex communication. The driving control section performs first communication with the first position detecting section via the first communication line and second communication with the second position detecting section via the second communication line in a temporally overlapping manner.

A mechanical arm calibration system and a mechanical arm calibration method are provided. The method includes: locating a position of an end point of a mechanical arm in a three-dimensional space to calculate an actual motion trajectory of the end point when the mechanical arm is operating; retrieving link parameters of the mechanical arm, randomly generating sets of particles including compensation amounts for the link parameters through particle swarm optimization (PSO), importing the compensation amounts of each of the sets of particles into forward kinematics after addition of the corresponding link parameters, to calculate an adaptive motion trajectory of the end point; calculating position errors between the adaptive motion trajectory and the actual motion trajectory of each of the sets of particles for a fitness value of the PSO to estimate a group best position; and updating the link parameters by the compensation amounts corresponding to the group best position.

An articulated arm system is disclosed that includes an articulated arm including an end effector, and a robotic arm control systems including at least one sensor for sensing at least one of the position, movement or acceleration of the articulated arm, and a main controller for providing computational control of the articulated arm, and an on-board controller for providing, responsive to the at least one sensor, a motion signal that directly controls at least a portion of the articulated arm.

A robotic surgical system includes a robotic arm, a surgical device coupled with the robotic arm and configured to extend through a body wall of a patient, and a controller in communication with the robotic arm. The controller is configured to determine a position of the surgical device relative to the patient. The controller is also configured to acknowledge a maximum allowable metric associated with the body wall at the determined position, and determine a metric associated with the body wall at the determined position. The controller is furthermore configured to drive the robotic arm to manipulate the surgical device such that the determined metric does not exceed the maximum allowable metric.

In one embodiment, the present disclosure includes a cloud computer system for controlling a plurality of remote devices comprising a cloud server including a cloud based operating system comprising a data model stored in a computer memory. The data model includes commands that may be performed by a plurality of remote devices in a remote system and, for each remote device, one or more operations for triggering processes executed by the remote device. The cloud based operating system generates a set of instructions from the plurality of commands and corresponding operations to control a portion of the remote devices to perform a task.

An image processing system configured to obtain a mesh representation of a scene, wherein the mesh representation comprises a plurality of polygons defined by respective vertices associated with an in-plane position, the in-plane position being in a plane comprising a first dimension and a second dimension, and the vertices having an associated vertex depth value in a third dimension different from the first dimension and the second dimension. The image processing system comprises an in-plane position estimation network configured to process image data representative of an image of the scene to estimate the in-plane positions associated with respective vertices of the mesh representation. The image processing system further comprises a depth estimation engine configured to process the in-plane positions and the image data to estimate the associated vertex depth values for the respective vertices of the mesh representation.

A conveying apparatus that facilitates setting of an operation mode and setting of appropriate conditions during teaching is provided. An input determination section determines whether or not a combination of a conveyable weight and an operation mode input from an input portion is appropriate. A motor parameter determination section determines one or more motor parameters of at least one servoamplifier for one or more servomotors based on the operation mode and the conveyable weight determined as appropriate by the input determination section. The one or more motor parameters include a maximum speed and a maximum acceleration of the one or more servomotors. A parameter change section allows a speed and an acceleration of the one or more servomotors to be changed up to the maximum speed and the maximum acceleration, respectively.

Arobot controlling method, system and warehouse-transport robot, which relates to the field of robot control. The method comprises: receiving, by a chassis controller, an information synchronization instruction sent by a main controller; performing, by the chassis controller, information synchronization with a rotation controller according to the information synchronization instruction; sending, by the chassis controller, a synchronous rotation instruction to the rotation controller, and controlling a chassis motor to drive a robot chassis to rotate at a predetermined angular velocity relative to the ground; controlling, by the rotation controller, a rotation motor to drive a robot rotation mechanism to synchronously rotate at an angular velocity relative to the rotating the robot chassis according to the synchronous rotation instruction.

According to one embodiment, a control system controls a robot. The control system includes a first system and a second system. The first system transmits a first command and supplementary data. The first command is represented using a specification different from a control command specification used by a controller of the robot. The supplementary data corresponds to the first command. The second system generates a second command based on the first command, attaches the supplementary data to the second command, and transmits the second command to the controller. The second command corresponds to the control command specification.