Caterpillar Inc.

Benjamin J Hodel

Jeffrey Kent Berry

Daniel Peter Sergison

Adam Martin Nackers

Matthew James Beschorner

Jean-Jacques Clar

Zhijun Cai

Eric Alan Reiners

Justin Lee Steinlage

Daniel J Martinez

Benjamin L Naasz

Nicholas Payne

Mo Wei

Evan Earl Jacobson

Corey Lee Gorman

Michael D Anderson

Peter Joseph Petrany

Nathan Stephen Pauli

Christopher M Sprock

Jeffry Neil Sundermeyer

Vamsi Krishna Pannala

Russell Schloss

Qiang Chen

David A Villero

Nathan J Wieland

Matthew James Lossmann

Jim A Nygaard

Sangkyum Kim

Paul Lee

Cynthia Marie Mann Rohde

Benjamin Floyd

A control system for a machine includes a machine body, a linkage assembly, a bucket coupled to the machine body via the linkage assembly, a user interface remote from the machine body, and a controller in communication with the user interface. The controller is configured to receive one or more signals defining a dump region. The dump region includes a front-most end point and a rear-most end point. The controller is also configured to receive one or more signals defining a dump order or dump specification and initiate an autonomous digging and dumping procedure.

Systems and methods for controlling a digging and dumping machine

A control system for a machine includes a machine body, a linkage assembly, an implement coupled to the machine body via the linkage assembly, one or more perception systems on the machine, and a controller in communication with the one or more perception systems. The controller is configured to receive one or more signals defining a dig region, define one or more dump regions based at least on the dig region and information from the one or more perception systems, and identify a position of a haul truck relative to the defined one or more dump regions.

Systems and methods for controlling a digging machine and positioning a transport vehicle

A machine, including a bucket linkage that defines dig block constraints, including: a reach inside and outside limits; swing first and second limits; and a depth limit. Sensors can be positioned on the machine to count dig passes. One or more memory devices have instructions that when executed by one or more processors cause the processors to: generate an excavation plan, including: determining a number of elevations; determining a number of rings in each elevation; and determining a number of sectors in each ring; and direct the excavator to perform dig passes in order to sequentially excavate each sector, wherein the sectors in each elevation are excavated from the reach outside limit to the reach inside limit and from the swing first limit to the swing second limit; wherein the system moves to the next sector when a threshold number of dig passes is reached.

Systems and methods for planning an excavator dig cycle

A machine may include an implement; one or more sensor devices; and a controller. The controller may be configured to receive, from the one or more sensor devices, data regarding a ground surface on which the machine is to perform a digging operation; transmit the data to one or more remote computing devices to cause the one or more remote computing devices to generate digging information based on the data; and receive the digging information from the one or more remote computing devices. The digging information may include information identifying a sequence of digging locations in an area of the ground surface and information identifying corresponding dumping locations. Based on the digging information, the controller may be configured to cause the machine to navigate to a digging location of the digging locations, and cause the implement to initiate the digging operation at the digging location.

Selectively causing remote processing of data for an autonomous digging operation

An inference system for monitoring a cementitious mixture for three-dimensional printing is provided. The inference system includes an ambient condition sensor, a temperature sensor, a moisture sensor and an image capturing device. The inference system also includes a controller coupled to the ambient condition sensor, the temperature sensor, the moisture sensor, and the image capturing device. The controller receives sensed ambient conditions, a temperature signal, and a moisture content signal. The controller receives an image feed of a portion of a cementitious mixture. The controller also receives signals indicative of a motor speed and a motor torque associated with a mixing container. The controller builds a model and determines a material suitability of the cementitious mixture using the model based on the received ambient conditions, the temperature signal, the moisture content signal, the image feed, the motor speed, and the motor torque and determines one or more corrective actions.

Monitoring system for three-dimensional printing

A controller may receive, from one or more sensor devices of the work machine, sensor data of an environment that includes a ground surface on which the work machine is located. The controller may determine whether a wireless connection is viable. The controller may selectively transmit the sensor data to one or more remote computing devices for processing or processing the sensor data locally by the work machine based on whether the wireless connection is viable. When the wireless connection is viable, the sensor data is transmitted to the one or more remote computing devices to cause the one or more remote computing devices to process the sensor data to provide first processed data. When the wireless connection is not viable, the sensor data is processed locally by the work machine to generate second processed data.

Remote processing of sensor data for machine operation

A method includes obtaining first fluidic pressure information indicative of fluidic pressure in a hydraulic cylinder of a machine as a function of time, and generating a first training set including second fluidic pressure information associated with impeller clutch engagement values. A training system associated with the machine computes a first plurality of test system response values based on the first training set, compares first plurality of test system response values with a plurality of observed response values, and determines a first response error. The training system determines whether the first response error is less than or equal to a threshold error value, generates a time-step predictive analytical model associated with the first training set, and provides the time-step predictive analytical model to an electronic control module of the machine. The time-step predictive analytical model is usable to control machine torque and/or an impeller clutch of the machine.

Analytical model training for a machine impeller control system

A machine control system can store model weights determined via machine learning using a training dataset correlating preset hydraulic valve displacements to measured movement parameters of a machine component. The machine control system can receive an input command for the component and machine state data from machine sensors. A control mapping model can use the model weights to map a combination of the input command and the machine state data into a predicted displacement of the hydraulic valve that causes movement of the component in response to the input command.

Control mapping for hydraulic machines

A system and method for monitoring objects in the vicinity of a work machine that is in motion includes obtaining, using a camera associated with the work machine, first image data of a field-of-view of the camera at a first position; identifying an object-of-interest within the first image data; associating an alarm with the object-of-interest, the alarm configured to indicate the presence of the object-of-interest; presenting the alarm through an output device; receiving an input to snooze the alarm for the object-of-interest; generating a mask for the object-of-interest using image coordinates of the object-of-interest within the first image data; obtaining, using the camera, second image data of the field-of-view of the camera at a second position during motion of the work machine; identifying an object within the second image data; and selectively aborting the alarm presentation through the output device in response to determining, using the mask, that the object is the object-of-interest.

Object tracking for work machines

A system for controlling a machine impeller clutch includes a power source, a transmission unit, an impeller clutch operatively coupling the power source to the transmission unit, and an input device configured to generate a force data signal indicative of a working fluid pressure. An electronic control module in communication with the input device is configured to execute a time-step predictive analytical model for impeller clutch engagement. The electronic control module configured to receive the force data from the input device, determine an impeller clutch engagement value based at least in part on the force data signal and utilizing the time-step predictive analytical model, and cause engagement of the machine impeller clutch according to the impeller clutch engagement value.

Automated machine impeller clutch

A system for determining an origin of a fluid from a machine may include a spectral measurement device configured to generate spectral data for a fluid, and a transmitter in communication with the spectral measurement device and configured to transmit the spectral data. The system may also include a receiver in communication with the transmitter and configured to receive a transmission indicative of the spectral data from the transmitter. The system may further include a processor in communication with the receiver and configured to cause execution of an analytical model configured to determine, based at least in part on the spectral data, fluidic information related to the fluid, the fluidic information including an indication of an origin of the fluid. The system may also include an output device in communication with the processor and configured to output the indication of the origin of the fluid.

System and method for determining fluid origin

A machine includes a frame, an engine supported on the frame, ground-engaging elements supported on the frame and driven by the engine to propel the machine, a machine light structure supported on the frame, a multiple-axis accelerometer positioned to generate an acceleration signal relative to a base of the machine light structure, and a controller. The controller is programmed to receive the acceleration signal, partition the acceleration signal into overlapped window periods, transform the acceleration signal of each overlapped window period into a power spectral density, and pass the power spectral densities of each overlapped window period through a strain power spectral densities prediction model to generate predicted strain power spectral densities of the machine light structure. A health monitor receives the predicted strain power spectral densities and is configured to generate a notification responsive to the predicted strain power spectral densities.

System and method for predicting strain power spectral densities of light machine structure

A method of using a virtual machine operator model includes providing a virtual machine operating environment having a current state and including a virtual machine and a virtual operator agent acting within the virtual machine operating environment. The method also includes executing a control action, by the virtual operator agent, relative to the virtual machine based on the virtual machine operator model. The method also includes analyzing a new state of the virtual machine operating environment resulting from execution of the control action, assigning a positive reward or a negative reward to the control action based on the new state, assigning the negative reward to the control action resulting in a high implement jerk movement of the virtual machine, and executing a learning algorithm to update the virtual machine operator model based on the positive reward or the negative reward.

System and method for using virtual machine operator model

A system for analyzing one or more operations associated with a component of a machine is disclosed. The system includes a data warehouse for storing data associated with the machine. The system includes a data extraction module, which extracts the data from the data warehouse and receives a customer input. The data extraction module generates an input parameter based on the data from the data warehouse and the customer input. The system includes a failure injection module for storing information of the one or more failures associated with the component of the machine. The system also includes a machine model, which is in communication with the data extraction module and the failure injection module. The machine model derives an output parameter associated with the one or more operations of the component of the machine, based on the input parameter and the information of the one or more failures.

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Dunlap, IL, United States of America

Benjamin J Hodel