Caterpillar Inc.

Sanat Arun Talmaki

Jason Louis Smallenberger

Scott Alan Leman

Paul Thomas Corcoran

Christopher M Sprock

Praveen Halepatali

Liqun Chi

Brad L Holsapple

Allen John DeClerk

Mark William Whiting

Ryan Baumann

A system for detecting a load location and a dump location at a worksite is provided. The system includes a controller coupled to a position detection module and a speed detection module. The controller receives a position signal and a speed signal associated with the machine. The controller identities a load location based on a relative distance between the machine and a loading tool and the speed of the machine. The controller monitors a velocity of the machine as the machine moves away from the identified load location, gathers data related to a plurality of stops made by the machine, and builds a heuristic model. The controller selects one stop from the plurality of stops as a dump location based on a distance between the respective dump location and the load location, a duration of stopping of the machine, and a confidence score associated with the respective dump location.

System and method for detection of load and dump locations

A method of controlling machines for performing operations at a worksite is disclosed. The method includes receiving pre-construction terrain data, design terrain data, and resource data and then defining a plurality of constraints based on the data. Operations of the machines are simulated based on the data. The method includes estimating process variables associated with the operations and defining and scheduling tasks to be performed by the machines. The method includes collecting real time data from the worksite and updating the estimated process variables and the scheduled tasks of the machines based on the collected real time data. Instructions are then provided to the machines for executing the tasks.

Method of optimizing performance of machines at a worksite

Systems and methods for analyzing and optimizing worksite operations based on video and or audio data are disclosed. One method includes receiving one or more models relating to a worksite, receiving first sensor data associated with the machine at the worksite, receiving second sensor data associated with an operation of the machine at the worksite, wherein the second sensor data is sourced from a sensor that is different from a sensor sourcing the first sensor data, determining, by the one or more processors, a machine state based at least on the first data and the second data, comparing the determined machine state to a modeled machine state represented by the received one or more models to classify site operations and/or detect an irregularity in site operations or an inefficiency in site operations, and generating a response based at least on the detected irregularity or inefficiency.

System and method for determination of machine state based on video and audio analytics

A system is disclosed for monitoring payload at a worksite having multiple machines. The system may have a first sensor located onboard a haul machine and configured to generate a first signal indicative of a total amount of material in the haul machine, and a second sensor located onboard a load machine and configured to generate a second signal indicative of an amount of material being dumped into the haul machine by the load machine. The system may also have at least one controller in communication with the first and second sensors. The at least one controller may be configured to selectively perform a sensor calibration process based on a comparison of the first and second signals.

Payload monitoring system

A material tagging system is disclosed for use with an excavation machine. The material tagging system may have a locating device configured to generate a first signal indicative of a location of the excavation machine at a worksite, and a communication device. The material tagging system may also have at least one of an operator input device and a sensor configured to generate a second signal indicative of an identity of material in a work tool, and a controller. The controller may be configured to receive an electronic map of the worksite predicting locations of different types of material, and to make a comparison of the identity of the material with a type of material predicted to be at a location where the excavation machine was located when the material was loaded. The controller may further be configured to selectively generate and communicate an error flag based on the comparison.

Automated material tagging system

Method of controlling machines at a worksite

Systems and methods for managing and optimizing mixed fleet worksite operations based on video and or audio data are disclosed. One method includes receiving one or more models relating to a fleet of machines at the worksite, wherein the fleet of machines comprises an in-network machine and an out-of-network machine, receiving first sensor data associated with the out-of-network machine at the worksite, receiving second sensor data associated with the in-network machine at the worksite, determining a machine state of each of the in-network machine and the out-of-network machine based at least on the first sensor data and the second sensor data, comparing the determined machine states to a modeled machine state represented by the received one or more models to classify site operations and/or detect an irregularity in site operations or an inefficiency in site operations, and generating a response based at least on the detected irregularity or inefficiency.

System and method for managing mixed fleet worksites using video and audio analytics

A system for allocating resources includes a plurality of first machines at a work area and a plurality of second machines at a remote location. A controller stores a material movement goal for a production time period, and determine a total capacity at the work area based upon a capacity of the first machines and the production time period. The total capacity at the work area is compared to the desired material movement goal and upon the total capacity at the work area exceeding the desired material movement goal, operational instructions are generated for and communicated to selected ones of the first machines. Upon the total capacity at the work area being less than the desired material movement goal an alert command is generated and some of the second machines may be moved to the work area.

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Plainfield, IL, United States of America

Sanat Arun Talmaki