A recoater collision prediction and calibration method for additive manufacturing and a system thereof are provided. The recoater collision prediction and calibration method includes the following steps: loading a printing image file to generate a simulated printing object according to the printing image file; performing a process thermal stress simulation on the simulated printing object to obtain a plurality of simulated deformation variables respectively corresponding to a plurality of prediction results of the simulated printing object in a vertical direction on each layer; obtaining an experimental collision height of an experimental printed object; selecting one of the plurality of simulated deformation variables according to the experimental collision height; calculating a recoater tolerance according to the one of the plurality of simulated deformation variables; calibrating a collision risk formula according to the recoater tolerance; and predicting a collision risk value between the simulated printing object and a recoater according to the collision risk formula.

A recoater collision prediction and calibration method for additive manufacturing and a system thereof are provided. The recoater collision prediction and calibration method includes the following steps: loading a printing image file to generate a simulated printing object according to the printing image file; performing a process thermal stress simulation on the simulated printing object to obtain a plurality of simulated deformation variables respectively corresponding to a plurality of prediction results of the simulated printing object in a vertical direction on each layer; obtaining an experimental collision height of an experimental printed object; selecting one of the plurality of simulated deformation variables according to the experimental collision height; calculating a recoater tolerance according to the one of the plurality of simulated deformation variables; calibrating a collision risk formula according to the recoater tolerance; and predicting a collision risk value between the simulated printing object and a recoater according to the collision risk formula.

A method performed by a risk management node for autonomous devices. The risk management node may determine state parameters from a representation of an environment. The representation of the environment may include an object, an autonomous device, and a set of safety zones. The risk management node may determine a reward value based on evaluating a risk of a hazard with the object based on the determined state parameters and current location and speed of the autonomous device relative to a safety zone from the set of safety zones. The risk management node may determine a control parameter based on the determined reward value, and may initiate sending the control parameter to the autonomous device to control action of the autonomous device. The control parameter may be dynamically adapted to reduce the risk of hazard with the object based on reinforcement learning feedback from the reward value.

A mobile robot is configured for operation in a commercial or industrial setting, such as an office building or retail store. The robot can patrol one or more routes within a building, and can detect violations of security policies by objects, building infrastructure and security systems, or individuals. In response to the detected violations, the robot can perform one or more security operations. The robot can include a removable fabric panel, enabling sensors within the robot body to capture signals that propagate through the fabric. In addition, the robot can scan RFID tags of objects within an area, for instance coupled to store inventory. Likewise, the robot can generate or update one or more semantic maps for use by the robot in navigating an area and for measuring compliance with security policies.

The present invention features a computer-implemented method of planning a processing path relative to a three-dimensional workpiece for a plasma arc cutting system coupled to a robotic arm. The method includes receiving input data from a user comprising (i) Computer-Aided Design (CAD) data for specifying a desired part to be processed from the three-dimensional workpiece, and (ii) one or more desired parameters for operating the plasma arc cutting system. A plurality of features of the desired part to be formed on the three-dimensional workpiece are identified based on the CAD data. The method also includes dynamically filtering a library of cut charts based on the plurality of features and the desired operating parameters to determine a recommended cut chart for processing the plurality of features. The method further includes generating the processing path based on the recommended cut chart and the plurality of features to be formed.

The present invention provides a collision prevention control system based on an electric lifting table and a collision prevention method thereof. The collision prevention control system includes a lifting table, an electronic lifting table control box disposed on the lifting table, and a single-chip microcomputer disposed in the electric lifting table control box. The electric lifting table control box is internally provided with an acceleration and gyroscope chip, the acceleration and gyroscope chip performing synchronous motion with the lifting table, the acceleration and gyroscope chip being in information interaction with the single-chip microcomputer, and motion control on the lifting table being performed through the single-chip microcomputer. The present invention has the advantageous effects as follows: the system has higher sensitivity and more reliable operation, and senses a slight angular change or an acceleration change when the lifting table encounters resistance.

A control device includes a machine learning unit that performs machine learning of control for an operation of a control target device, an avoidance command value calculation unit that obtains an avoidance command value that is a control command value for the control target device, the control command value which satisfies constraint conditions including a condition for the control target device not to come into contact with an obstacle, and the control command value that an evaluation value obtained by applying the control command value to an evaluation function satisfies a prescribed end condition, and a device control unit that controls the control target device on the basis of the avoidance command value, in which a parameter value obtained through the machine learning in the machine learning unit is reflected in at least one of the evaluation function and the constraint condition.

A simulation method of specifying a relative position between a first base of a first robot manipulator and a second base of a second robot manipulator, including: determining a first working area of the first robot manipulator, wherein the first working area determines a finite plurality of tuples from possible positions of the first end effector and possible orientations of the first end effector in respective positions of the first end effector; determining, for each of a specified plurality of possible relative positions between the first base and the second base, a number of the tuples from the first working area as evaluation variables, for which a second end effector is capable of being positioned in a predefined orientation and/or at a predefined distance relative to the first end effector; and determining and outputting the relative position between the first base and the second base with a highest evaluation variable.

A numerical controller performs control to rotate a turret, which holds a plurality of tools on the outer peripheral portion thereof, thereby moving one (selected tool) of the tools selected by a command to a predetermined position (selected position). The numerical controller sets and holds a partial area of the outer peripheral portion of the turret as an area (passage-prohibited area) prohibited from passing through the selected position and determines whether or not the selected tool is present in the passage-prohibited area or whether or not the passage-prohibited area passes through the selected position as the selected tool is moved to the selected position.

Systems and methods for collision detection and avoidance are provided. In one aspect, a robotic medical system including a first set of links, a second set of links, a console configured to receive input commanding motion of the first set of links and the second set of links, a processor, and at least one computer-readable memory in communication with the processor. The processor is configured to access the model of the first set of links and the second set of links, control movement of the first set of links and the second set of links based on the input received by the console, determine a distance between the first set of links and the second set of links based on the model, and prevent a collision between the first set of links and the second set of links based on the determined distance.