Method and system for optimizing the arrangement of a set of aircraft parts on a plate. The system includes a selection module which selects, from a set of data on the material of the set of parts and a set of geometric data associated with the set of parts, part grouping criteria including at least one optimized plate thickness value, an optimization module for optimizing the arrangement of the set of parts on a plate as a function of the part grouping criteria, cutting criteria and optimization criteria and a transmission module generating and transmitting, to a user system, optimization data representative of the optimization of the arrangement of the set of parts. The system makes it possible to take account of the parameters linked to the dimensions of the plate to optimize the arrangement of the set of parts.

A manipulator system includes a manipulator configured for guiding an instrument. The system furthermore includes a controller configured to actuate the manipulator such that the instrument is pressed with a pressing force against a human body. A force reduction input device is provided separately from the manipulator and is operable by an operator to reduce the pressing force.

A tool for validating a wire-electric-discharge-machining (wEDM) operation to be performed using a wEDM machine comprises a body including an engagement feature shaped to removably hold a validation coupon to be machined in the wEDM operation, the validation coupon sized larger than a size of a cut-out to be made in a part using the wEDM machine. A method of manufacturing the tool and a wEDM machine assembly are also provided.

A measurement operation parameter adjustment apparatus that enables efficient measurement of the placement position of an object to be measured even in the case where there are variations in the placement positions, the sizes, and the product types of objects to be measured includes a machine learning device. The machine learning device observes measurement operation parameter data representing the measurement operation parameter of the measurement operation and measurement time data representing time taken to perform the measurement operation as a state variable representing a current environmental state and performs learning or decision-making using a learning model obtained by modeling adjustment of the measurement operation parameter based on the state variable.

A measurement operation parameter adjustment apparatus that enables efficient measurement of the placement position of an object to be measured even in the case where there are variations in the placement positions, the sizes, and the product types of objects to be measured includes a machine learning device. The machine learning device observes measurement operation parameter data representing the measurement operation parameter of the measurement operation and measurement time data representing time taken to perform the measurement operation as a state variable representing a current environmental state and performs learning or decision-making using a learning model obtained by modeling adjustment of the measurement operation parameter based on the state variable.

Gerber data for a substrate includes coordinates for physical features on the substrate. The coordinates are relative to a substrate origin point on the substrate. The gerber data allows a user to specify any of the physical features as soldering targets of a soldering apparatus that includes a motor for moving a soldering iron according to coordinates relative to a system origin point of the soldering apparatus. When the substrate is placed on the soldering apparatus, its substrate origin point differs from the system origin point of the soldering apparatus. The user may input coordinates for the substrate origin point, which are used by the soldering apparatus to derive coordinates, usable by soldering apparatus, from coordinates in the gerber data. In this way, it is possible to reduce the workload of the user when programming the soldering apparatus to perform a soldering process.

Gerber data for a substrate includes coordinates for physical features on the substrate. The coordinates are relative to a substrate origin point on the substrate. The gerber data allows a user to specify any of the physical features as soldering targets of a soldering apparatus that includes a motor for moving a soldering iron according to coordinates relative to a system origin point of the soldering apparatus. When the substrate is placed on the soldering apparatus, its substrate origin point differs from the system origin point of the soldering apparatus. The user may input coordinates for the substrate origin point, which are used by the soldering apparatus to derive coordinates, usable by soldering apparatus, from coordinates in the gerber data. In this way, it is possible to reduce the workload of the user when programming the soldering apparatus to perform a soldering process.

A numerically controlled turning center (1; 100), includes a support and rotation unit (19) having a first pair of support and rotation members (21, 23) aligned with each other along a first rotation axis (A1) and a second pair of support and rotation members (25, 27) aligned with each other along a second rotation axis (A2) parallel first rotation axis (A1). There is also provided a machining head (13) with a rotary tool (13.1) movable along a first numerically controlled translation axis (X) parallel to the rotation axes (A1, A2) of the first pair of support and rotation members and of the second pair of support and rotation members. The machining head (13, 15, 17) and the support and rotation unit (19) are movable one with respect to the other in a direction (Z) orthogonal to the first rotation axis (A1) of the first pair of support and rotation members (21, 23) and to the second rotation axis (A2) of the second pair of support and rotation members (25, 27).

Example implementations described herein are directed to a simulation environment for a real world system involving one or more robots and one or more sensors. Scenarios are loaded into a simulation environment having one or more virtual robots corresponding to the one or more robots, and one or more virtual sensors corresponding to the one or more virtual system to train a control strategy model from reinforcement learning, which is subsequently deployed to the real world environment. In cases of failure of the real world environment, the failures are provided to the simulation environment to generate an updated control strategy model for the real world environment.

Methods, systems, and apparatus, including computer-readable storage devices, for robot navigation using 2D and 3D path planning. In the disclosed method, a robot accesses map data indicating two-dimensional layout of objects in a space and evaluates candidate paths for the robot to traverse. In response to determining that the candidate paths do not include a collision-free path across the space for a two-dimensional profile of the robot, the robot evaluates a three-dimensional shape of the robot with respect to a three-dimensional shape of an object in the space. Based on the evaluation of the three-dimensional shapes, the robot determines a collision-free path to traverse through the space.