Disclosed are a collision avoidance method for a moving device, a collision avoidance apparatus for a moving device, and a computer-readable storage medium. This application relates to the field of artificial intelligence technologies. According to the method, a parking direction of a moving device in an avoidance area is adjusted, so that a startup time used by the moving device after avoidance completes may be reduced. The method includes: determining a target path direction of a moving device; determining a first candidate parking direction and a second candidate parking direction; determining, based on the target path direction, a target parking direction of the moving device from the first candidate parking direction and the second candidate parking direction; and controlling, based on the target parking direction, the moving device to be parked in the avoidance area.

An information processing apparatus includes a controller. The controller is configured to obtain a measurement value of a sensor provided in mechanical equipment. The controller is configured to generate a first model by machine learning using the measurement value of the sensor measured in a first period of the mechanical equipment and store the first model in a storage portion. The controller is configured to generate a second model by machine learning using the measurement value of the sensor measured in a second period after a trigger event has occurred in the mechanical equipment and store the second model in the storage portion. The controller is configured to determine a state of the mechanical equipment by using the measurement value of the sensor measured in an evaluation period and the first model and the second model stored in the storage portion.

A robot device (10) acquires object information related to an object to be gripped by the robot device including a grip unit (32) that grips an object. The robot device (10) then determines, based on operation contents executed by the robot device with the object gripped and the object information, a constraint condition when the operation contents are executed.

It is disclosed a method and a predicting system for automatic prediction of porosity appearance generated during Laser Powder Bed Fusion (L-PBF), performed by an additive manufacturing system from at least one material. The method comprises steps for training a neural network comprising: generating labels of pore in every pixel using a porosity simulator; pre-training, comprising a first sub-step and a second sub-step, the second sub-step comprises using the data set created from the first sub-step to generate a pre-trained ML model; and training, comprising a first sub-step and a second sub-step, the second sub-step comprises using the data set created from the first sub-step to train the pre-trained ML model to generate a trained ML model.

A brain-like decision-making and motion control system is disclosed, this system comprises an active decision-making module, an automatic decision-making module, an evaluation module, a memory module, a perceptual module, a compound control module, an input channel module, an output channel module, and a controlled object module. The three working modes supported by the system include an active supervision mode, an automatic mode, and a feedback-driven mode, which enables the robot to make autonomous decisions to select targets and operations and delicately control actions in the process of interacting with the environment, and can make the robot learn new operations by trial and error, imitation, demonstration, with the flexibility to adapt to the complex task and the environment.

This tool selection method is provided with: a step for respectively calculating, with respect to a plurality of known workpieces, feature amounts based on the shapes of a plurality of machining surfaces wherein, to each of the plurality of known workpieces, one main tool which is preselected from a tool list that includes a plurality of tools is allocated as being suitable for machining the plurality of machining surfaces; a step for executing, with respect to the plurality of known workpieces, machine learning by taking the feature amounts as inputs and the main tools as outputs; a step for calculating a feature amount for a target workpiece; and a step for selecting, with respect to the target workpiece, a main tool from the tool list on the basis of a machine learning result obtained by using the feature amount of the target workpiece as an input.

A method and a device for simulating a machining process of a workpiece on an NC-controlled machine tool by means of a self-learning artificial neural network. Process parameters both from a machining process on a real machine tool located in a manufacturing section and a digital machine model implemented in a simulation section are provided to the artificial neural network to learn the behavior of the machine tool including the tools and workpieces used and are reformatted into input parameters by means of mathematical transformation. By learning the behavior of the machining process, the artificial neural network ca, send output files back to the simulation software of the simulation section and optimally adapt the behavior of the digital machine model to the conditions of the real machine tool by adapting the simulation parameters and make it more efficient in order to optimize the machining process on the machine tool.

A prediction method of part surface roughness and tool wear based on multi-task learning belong to the file of machining technology. Firstly, the vibration signals in the machining process are collected; next, the part surface roughness and tool wear are measured, and the measured results are corresponding to the vibration signals respectively; secondly, the samples are expanded, the features are extracted and normalized; then, a multi-task prediction model based on deep belief networks (DBN) is constructed, and the part surface roughness and tool wear are taken as the output of the model, and the features are extracted as the input to establish the multi-task DBN prediction model; finally, the vibration signals are input into the multi-task prediction model to predict the surface roughness and tool wear.

Systems, methods, and apparatus for prediction of maintenance service for machines. In one example, one or more sensors are configured to generate a sensor data stream during operation of a machine. An artificial neural network (ANN) is configured to receive the sensor data stream and predict a maintenance service for the machine based on the sensor data stream. For example, the ANN can be trained using the sensor data stream collected within a predetermined time period of a machine being newly-installed in an assembly line or other industrial automation facility. The machine can be considered to be operating in a normal condition during the predetermined time period such that the ANN can be trained to detect anomaly that deviates from the normal patterns of the sensor data stream. For example, the ANN can be a spiking neural network (SNN).

An operation path generation unit (210) generates an operation quantity time series for an actuator (111) based on a measurement state quantity output from a state sensor (101). A predictive model unit (220) generates a state quantity predictive time series by calculating a predictive model by using as an input the measurement state quantity and the operation quantity time series. A neural network unit (230) corrects the state quantity predictive time series by performing arithmetic operation of a neural network, by using as an input a measurement environment quantity output from an environment sensor (102) and the state quantity predictive time series. A state quantity evaluation unit (240) generates an evaluation result for the state quantity time series after the correction. The operation path generation unit outputs an operation quantity at the head of the operation quantity time series to the actuator when the evaluation result fulfils an appropriate criterion.