The present invention relates to a system for detecting the collision of a robot manipulator using an artificial neural network. The system may comprise: joint driving units provided in a plurality of joints of the robot manipulator to drive the plurality of joints, respectively; encoder units provided on sides of the joint driving units to measure the angles of the plurality of joints; and a neural network calculation unit for training the neural network with a large amount of data and inferring, via a preprocessing calculation, to detect that the plurality of joints collide with the outside.

Apparatus for acquiring data for configuring a pneumatic air gun comprises a data storage device and a controller. The controller is configured to determine a required velocity for a firing and determine a pressure of an air reservoir, or a pressure in an air flow path at a position downstream of the air reservoir of the gun. The controller is configured to determine a value of a valve activating parameter for the firing and determine a measured velocity of a projectile fired from the gun using the determined value of the valve activating parameter. The controller is configured to determine whether a difference between the measured velocity and the required velocity is within a required limit. The controller is configured to store the determined pressure and the determined value of the valve activating parameter in the data storage device as mapping data for configuring a future firing of the gun when the difference between the measured velocity and the required velocity is within the required limit.

Some robotic arms may include vacuum-based grippers. Detecting the seal quality between each vacuum assembly of the gripper and a grasped object may enable reactivation of some vacuum assemblies, thereby improving the grasp. One embodiment of a method may include activating each of a plurality of vacuum assemblies of a robotic gripper by supplying a vacuum to each vacuum assembly, determining, for each of the activated vacuum assemblies, a first respective seal quality of the vacuum assembly with a first grasped object, deactivating one or more of the activated vacuum assemblies based, at least in part, on the first respective seal qualities, and reactivating each of the deactivated vacuum assemblies within a reactivation interval.

Systems and methods related to intelligent grippers with individual cup control are disclosed. One aspect of the disclosure provides a method of determining grip quality between a robotic gripper and an object. The method comprises applying a vacuum to two or more cup assemblies of the robotic gripper in contact with the object, moving the object with the robotic gripper after applying the vacuum to the two or more cup assemblies, and determining, using at least one pressure sensor associated with each of the two or more cup assemblies, a grip quality between the robotic gripper and the object.

A motor control device. When the motor control device executes pressure control of which a minor loop is speed control or position control, the pressure control is executed in a manner that pressurization or depressurization is performed while a control parameter of the speed control is fixed; a control parameter of the pressure control is gradually increased; an oscillation amount is successively detected and stored. If the oscillation amount exceeds an acceptable value, on the basis of the control parameter of the pressure control and the oscillation amount stored during adjustment, the control parameter of the pressure control is adjusted such that the oscillation amount is equal to or less than the acceptable value.

A model formation module (25) is provided for creating a model for controlling a pressure regulating system (7) of a water supply network (5), wherein the water supply network (5) is equipped with one or more pressure sensors of which at least one remote pressure sensor (17a,b) is arranged remotely from the pressure regulating system (7), the model formation module (25) being configured to communicate with the at least one remote pressure sensor (17a,b). The model formation module (25) is configured to create said model without a measured, determined or estimated flow value on the basis of at least one remote pressure value determined by the at least one remote pressure sensor (17a,b) and on the basis of at least one load-dependent variable of the pressure regulating system (7), said model representing at least one pressure control curve for controlling the pressure regulating system (7).

An article of footwear with a dynamic support system that controls arrays of tiles in the upper of the footwear to adjust the level of support provided in different regions of the upper. Sensors in the sole of the footwear, in the upper of the footwear and/or in an article worn by the wearer of the footwear measure the level of stress or other characteristics and provide input to one or more microprocessors that control motors located in the sole or in the upper of the footwear. When the motors are activated, they may compress or loosen arrays of tiles to adjust the stiffness of the upper in one or more regions of the upper.

A controller for an agricultural sprayer machine is configured to receive for each of a plurality of nozzle sets a respective upper pressure limit, a nozzle reference flow, and a nozzle reference pressure. For each one of the plurality of nozzle sets, a speed setpoint is calculated based upon the application rate setpoint, the nozzle reference flow, the nozzle reference pressure, and the product pressure setpoint.

An apparatus (1) for positioning the body of a user (100), wherein the apparatus can be used in a seat or in the form of a rest, comprising a seating element (20), a pelvis and lumbar-spine module (13) and a computer unit (40), wherein the pelvis and lumbar-spine module comprises an adjusting element (117) and a sensor (113), and the seating element comprises an adjusting element (108) and three sensors (111, 112), wherein the sensors (111, 112, 113) sense seat-related and rest-related pressures, wherein the computer unit activates the adjusting elements, wherein a first and second sensor (112) and a third sensor (111) sense the seat-related pressure of the first and second ischial tuberosities (102) and of the coccyx (104), wherein the first and the second ischial tuberosities define an ischial-tuberosity level, wherein the adjusting element of the seating element compensates for the ischial-tuberosity level horizontally about the user's sagittal axis, and whereupon the adjusting element of the pelvis and lumbar-spine module tilts the user's pelvis (101) about the horizontal axis of the same until the seat-related and/or rest-related pressure of the coccyx has a value substantially equal to zero.

A buoyancy adjusting apparatus includes: a volume-changeable portion configured to be coupled to a buoyancy-adjustment target; and an operation receiver configured to receive an operation from a user to change a volume of the volume-changeable portion. The volume of the volume-changeable portion is changeable according to the operation received by the operation receiver, to change the buoyancy of the buoyancy-adjustment target in fluid.