This description provides an autonomous or semi-autonomous excavation vehicle that is capable of navigating through a dig site and carrying out an excavation routine using a system of sensors physically mounted to the excavation vehicle. The sensors collects any one or more of spatial, imaging, measurement, and location data representing the status of the excavation vehicle and its surrounding environment. Based on the collected data, the excavation vehicle executes instructions to carry out an excavation routine. The excavation vehicle is also able to carry out numerous other tasks, such as checking the volume of excavated earth in an excavation tool, and helping prepare a digital terrain model of the site as part of a process for creating the excavation routine.

A system of this disclosure includes an artificial intelligence module, which may include a neural network or a decision tree architecture, configured to analyze data indicative of the manner in which an operator performs tasks using a heavy machine. The artificial intelligence module is further configured to provide instructions pertaining to the control of at least some components of the heavy machine. As such, the heavy machine is operated in whole or in part based on the direction of the artificial intelligence module, which reduces reliance on a human operator. The artificial intelligence module is highly efficient, and in particular the artificial intelligence module is trained relatively quickly. Further, the artificial intelligence module may be embodied on the heavy machinery itself, as opposed to on a cloud-based system or on a more high-powered computer. Accordingly, the cost of implementing and operating the disclosed system is relatively low.

A wheel loader machine includes a bucket configured to hold a predetermined load of a granular or powder material, a hydraulic system having an electro-hydraulic actuator to move the bucket over tilt angles and to shake the bucket, a hydraulic sensor configured to detect hydraulic pressure, an interface configured to receive input parameters and to output status information of the bucket, and a controller. The input parameters including a target weight of the granular or powder material. The status information including weight of the granular or powder material in the bucket determined using the detected hydraulic pressure. The controller configured to control the hydraulic system to tilt the bucket to a predetermined tilt angle and to shake the bucket in accordance with an agitation pattern until the weight of the granular or powder in the bucket is at or below the target weight of the granular or powder material.

In several aspects, a system and associated method may be used to estimate a load weight carried by an implement of a work vehicle. In doing so, the system and method may allow for the implement load weight to be estimated by taking into account operator-initiated control commands that may impact the accuracy or reliability of the estimated weight.

A virtual guidance apparatus and method assist an operator to maneuver a loader of a work vehicle for material handling of a workpiece. The virtual guidance apparatus includes a guidance control unit, one or more camera devices mounted on the associated work vehicle and operable to obtain an image of a loader of the associated work vehicle, and a display unit operatively coupled with the guidance control unit and displaying on a screen of the display unit that is viewable from an operator's seat of the associated work vehicle visual aids for guiding the operator in operating the loader to position a boom relative to level ground, to position a tool carrier on an end of the boom, to show a preview of a path of the tool carrier and of tools attached with the tool carrier, and to assist in positioning the workpiece load to be manipulated.

A work machine including a frame, a ground-engaging element movably coupled to the frame, a movable grading element, an actuator coupled to the movable grading element to controllably drive movement of the movable grading element engaging material to be graded, a sensor, and a controller. The controller comprises a memory that stores computer-executable instruction and a processor that executes the instructions. The instructions include labelling each of at least a plurality of pixels in a first image as a visual marker; selecting the first image as a reference keyframe; tracking at least one region of a subsequent image including the visual marker relative to the reference keyframe to determine an estimate of the current pose as the work machine moves; and adjusting a position of the movable grading element.

A method is disclosed for controlled loading by a self-propelled work vehicle comprising ground engaging units supporting a main frame, and at least one work attachment moveable with respect to the main frame for loading and unloading material in a loading area external to the work vehicle. Using at least one detector, such as cameras and/or vehicle motion sensors, location inputs for the loading area are detected respective to the main frame and/or at least one work attachment. A trigger input is detected in association with transition of the work vehicle from a first work state to an automated second work state. In the second work state, at least movement of the main frame and/or the at least one work attachment is automatically controlled relative to a defined reference associated with the loading area. Such a system and method facilitates loading operations and accordingly higher productivity regardless of operator experience.

A target posture for a work implement at work is determined. There is provided a working system, comprising a body, a work implement attached to the body, and a computer. The computer has a trained target posture estimation model to determine a target posture for the work implement to assume at work. The computer obtains a target value for an amount of a work performed by the work implement, a period of time elapsing since the work implement started to work, and mechanical data for operation of the body and the work implement, uses the trained posture estimation model to estimate a target posture from the target value, the elapsed period of time and the mechanical data, and thus outputs the estimated target posture.

A method for monitoring a machine operating at a worksite, is provided. The machine includes an implement for performing one or more implement operations and is configured to be propelled by a set of ground engaging members between a first location and second location. A first input indicative of start of a travelling operation of the machine after completion of a first implement operation at the first location, is received. One or more transmission parameters associated with the machine are determined, when the machine moves from first location to second location. A second input indicative of end of the travelling operation at start of a second implement operation at the second location is received. A number of revolutions completed by ground engaging members between the first location and the second location is determined based on the transmission parameters. The number of revolutions is displayed on input/output device associated with machine.

A wheel loader includes: a front frame; a bucket; a boom having a distal end connected to bucket, and a proximal end rotatably supported by front frame; a sensor configured to measure a distance between boom and a loading target; and a controller configured to control an action of wheel loader. The controller causes wheel loader to perform a predetermined action for collision avoidance on condition that a distance to be measured by sensor when wheel loader travels takes a value less than or equal to a threshold value.