Task completion time estimation for autonomous machines

Abstract

A machine is disclosed. The machine can include at least one of a propulsion system or a steering system configured to operate under automatic control in an autonomous mode of the machine; and a controller configured to obtain one or more parameters associated with a task to be performed in the autonomous mode, determine an estimated completion time for the task based on the one or more parameters associated with the task, and perform one or more actions based on the estimated completion time for the task.

Description

TECHNICAL FIELD

[0001] The present invention relates generally to an autonomous machine, and, for example, to task completion time estimation for the autonomous machine. BACKGROUND

[0002] Compacted surface materials, such as soil or asphalt, can increase the strength and stability of a surface to a particular degree required for construction operations. Typically, compaction is performed by a mobile compactor. One type of compactor is an autonomous compactor that performs a compaction task on a defined area using a defined set of compaction settings without control by a human operator. This can increase the productivity of the machine and reduce the human resources required to control the site operations. However, it can be difficult for an administrator of the site to determine a completion time for a task to be performed autonomously by the machine. As a result, machine resource utilization, site scheduling, etc. can not be optimized.

[0003] The completion time estimation system of the present invention addresses one or more of the above-referenced issues and / or other issues in the art. SUMMARY

[0004] According to some implementations, a machine can include at least one of a propulsion system or a steering system configured to operate under automatic control in an autonomous mode of the machine; and a controller configured to obtain one or more parameters associated with a task to be performed in the autonomous mode, determine an estimated completion time for the task based on the one or more parameters associated with the task, and perform one or more actions based on the estimated completion time for the task.

[0005] According to some implementations, a completion time estimation system can include a controller configured to obtain one or more parameters associated with a task to be performed in an autonomous mode of a machine, the task being one of a plurality of tasks to be performed in the autonomous mode of the machine; determine an estimated completion time for the task based on the one or more parameters associated with the task; cause information identifying an association between the task and the estimated completion time to be stored; and selectively cause the task to be initiated in the autonomous mode of the machine in accordance with the association between the task and the estimated completion time based on a selection of whether the task or another of the plurality of tasks.

[0006] According to some implementations, a method may include obtaining one or more parameters associated with a task to be executed in an autonomous mode of a machine; determining an estimated completion time of the task based on the one or more parameters associated with the task; determining an updated estimated completion time based on time elapsed since the task was started while the machine is executing the task in the autonomous mode, and determining the task progress based on the movement of the machine; causing storage of information identifying the association between the updated estimated completion time and the task progress; and performing one or more actions based on the association between the updated estimated completion time and the task progress. Attached Figure Description

[0007] Figure 1 This is a diagram of the example machine described here.

[0008] Figure 2 This is a diagram of the example completion time estimation system described here.

[0009] Figure 3 This is a flowchart of an example process for estimating task completion time for autonomous machines. Detailed Implementation

[0010] This invention relates to a completion time estimation system. This completion time estimation system is universally applicable to any machine capable of autonomous operation, such as compactors, pavers, cold planers, and graders.

[0011] Figure 1 This is a diagram of the example machine 100 described here. (See diagram for example.) Figure 1 As shown, machine 100 can be a compactor (e.g., a vibratory roller compactor), which can be used to compact various materials, such as soil, asphalt, etc. Figure 1 As shown, machine 100 includes a frame 102 attached to and supporting body 104. Frame 102 may include multiple parts and / or separate frames connected to each other. For example, frame 102 may include multiple connected frames configured to be hinged relative to each other.

[0012] The machine 100 also includes a generator 106 configured to generate power to propel the machine 100. The generator 106 includes one or more power generation devices, such as an internal combustion engine (e.g., a gasoline and / or diesel internal combustion engine), an electric motor, etc.

[0013] Generator 106 is operatively coupled to wheel 108. Although not shown, machine 100 includes a brake associated with wheel 108. In some implementations, machine 100 may employ other ground-engaging components besides or in place of wheel 108. For example, machine 100 may include a track. Generator 106 is also operatively coupled to one or more drivetrain components, such as a gearbox configured to transmit power generated by generator 106 to wheel 108. Furthermore, generator 106 may provide power to various operating elements of machine 100, such as one or more actuators attached to machine 100.

[0014] Machine 100 includes a roller 110. The roller 110 is coupled to a frame 102 and configured to rotate relative to the frame 102 about an axis perpendicular to the direction of travel of machine 100. The roller 110 provides compressive force to process materials such as asphalt, soil, etc. For example, the roller 110 provides static compressive force due to its own weight. Additionally, the roller 110 includes one or more mechanisms configured to vibrate the roller and thereby provide strong compressive force. These one or more mechanisms may be surrounded by the roller 110 (e.g., inside the roller 110) and may include a weight that is rotated (e.g., about an axis perpendicular to the direction of travel) to provide vibration to the roller 110. In some configurations, machine 100 may include a second roller as described above, replacing wheel 108.

[0015] In some implementations, machine 100 is a grading machine (not shown). In this example, machine 100 does not include roller 110 and may include blades, a lever-circumference-template assembly, etc. Alternatively, in addition to roller 110, machine 100 may include blades, a lever-circumference-template assembly, etc.

[0016] Machine 100 includes operator station 112. A human operator of machine 100 may occupy operator station 112 to manually control various functions and movements of machine 100 via, for example, steering mechanisms, one or more control inputs (e.g., speed throttle valves, actuator control levers, etc.), consoles and / or other user inputs.

[0017] Furthermore, machine 100 is configured to operate in an autonomous mode (e.g., using a Global Positioning System (GPS), light detection and ranging (LiDAR) system, etc.). A machine configured to operate in an autonomous mode may be referred to as an autonomous machine (although specific operations of the machine can be manually controlled). In autonomous mode, one or more functions of machine 100 can be automatically controlled by the controller 114 of machine 100 (e.g., an Electronic Control Module (ECM)) instead of being manually controlled by a human operator. Figure 1 As shown, controller 114 is located behind seat 116 of machine 100; however, controller 114 may be located in other locations of machine 100.

[0018] In autonomous mode, controller 114 can provide automatic control of propulsion, steering, and / or working operations of machine 100 (e.g., in conjunction with one or more sensors of machine 100). In this case, a human operator can occupy operator station 112 to observe the operation of machine 100 and / or exercise autonomous mode and provide manual control of machine 100 as needed. In some cases, a human operator may not occupy operator station 112 and can remotely observe the operation of machine 100 and / or remotely exercise autonomous mode to provide manual control, for example via a remote control device.

[0019] Controller 114 may include one or more memories and / or one or more processors that implement operations associated with autonomous mode and / or task completion time estimation of machine 100, such as in combination Figure 2 As described. For example, controller 114 can be configured to obtain one or more parameters associated with a task to be performed in autonomous mode, determine an estimated completion time of the task based on the one or more parameters, and perform one or more actions based on the estimated completion time, etc.

[0020] As mentioned above, Figure 1 This is provided as an example. Other examples may differ from this combination. Figure 1 As described.

[0021] Figure 2 This is a graph of the example completion time estimation system 200 described here. (See figure.) Figure 2 As shown, machine 100 includes a completion time estimation system 200. The completion time estimation system 200 includes a controller 114, operator control 202, propulsion system 204, steering system 206, and working system 208. In the example where machine 100 is a compactor, the working system 208 may be a compaction system; in the example where machine 100 is a grading machine, it may be a grading system, etc. In the example where machine 100 is another type of machine (e.g., a paver, a cold planer, etc.), the working system 208 may include other systems.

[0022] Operator control 202 includes one or more input devices configured to receive operator commands from a human operator of machine 100 and provide information related to those commands to controller 114. For example, operator control 202 may be one or more control inputs (e.g., one or more buttons, joysticks, levers, etc.), one or more consoles, and / or other user inputs included in machine 100 and having a wired connection to controller 114, as in combination. Figure 1 As described. As another example, operator control 202 can be one or more remote control devices and / or other user input devices, such as in combination with... Figure 1As described, it is located away from machine 100 and has a wireless connection to controller 114.

[0023] A human operator of machine 100 (e.g., a human operator occupying operator station 112 or remotely located) can use operator control 202 to configure parameters for one or more tasks (e.g., compaction tasks, grading tasks, etc.) to be performed in autonomous mode by machine 100. For example, parameters may include the area in which machine 100 will perform the task (e.g., a geographical area, one or more geographical boundaries, etc.), the amount of time machine 100 will perform the task (e.g., compaction of the area, grading of the area, etc.), and settings for the task (e.g., vibration frequency and / or amplitude to be used for compaction, blade height and / or blade angle to be used for grading, etc.). Controller 114 may store, or cause another device to store, the parameters for one or more tasks.

[0024] Controller 114 may acquire task-related parameters (e.g., from memory or from operator control 202) to determine the estimated completion time of the task. The task may be a compaction task or a grading task, and these parameters relate to one or more of the following: the area in which machine 100 will perform the compaction or grading task (e.g., configured by a human operator), the amount of overlap of the channels configured for that area (e.g., by a human operator), the number of passes configured for that area (e.g., by a human operator), etc. Furthermore, the task-related parameters may relate to the capabilities or specifications of machine 100. For example, these parameters may relate to one or more of the following: the speed at which machine 100 will perform the compaction task (e.g., ground speed), the width of the rollers 110 of machine 100 (e.g., along an axis perpendicular to the direction of travel of machine 100), the blade width of machine 100, the maneuvering distance used by machine 100 when changing channels in an area, or the maneuvering speed used by machine 100 when changing channels in an area (including a deceleration rate for transitioning from speed to operating speed and / or an acceleration rate for transitioning from operating speed to speed), etc.

[0025] Controller 114 can determine the estimated completion time of the task (e.g., compaction task, grading task, etc.) based on these parameters. The estimated completion time indicates the amount of time estimated to be taken from the start of the task to its completion. Controller 114 can also determine the progress of the task before it starts, which may be zero percent, etc.

[0026] Controller 114 can use one or more algorithms, models, etc., to determine the estimated completion time of a task. For example, controller 114 can use parameters associated with an algorithm to determine the estimated race time of a task. As another example, controller 114 can use a model, such as a machine learning model, to determine the estimated completion time of a task. The machine learning model can be trained using historical data related to the parameters used for one or more tasks and the actual completion times for one or more tasks. Controller 114 can input the parameters into the machine learning model and obtain the estimated completion time as the output of the machine learning model.

[0027] The controller 114 may perform one or more actions based on the estimated competition time and / or the determined task progress. Actions performed by the controller 114 may include causing a display on the machine 100 (e.g., a display located in the operator station 112, a remote display associated with the machine 100, etc.) to display the estimated completion time and / or task progress.

[0028] Actions performed by controller 114 may include generating or updating the schedule of machine 100. The schedule may indicate the time when machine 100 will appear at various locations on the site, the time when machine 100 will perform a specific task, etc. This scheduling may be based on estimated completion times and / or progress. For example, if the estimated completion time of a task meets (e.g., is greater than) a threshold time amount, controller 114 may schedule the task for a specific time of day (e.g., in the morning), schedule the task for another day (e.g., another day with fewer tasks scheduled), pair the task on that schedule with another task associated with an estimated completion time that does not meet (e.g., is less than) the threshold time amount, etc.

[0029] Actions performed by controller 114 may include transmitting information identifying the estimated completion time and / or progress to a device (e.g., a server device). The device may use this information to schedule the work site in which machine 100 operates. For example, based on the estimated completion time, the device may determine that machine 100 will be scheduled to perform a task before or after another machine is scheduled to perform another task (e.g., another task that may affect or be affected by its performance).

[0030] Actions performed by controller 114 may include generating an alarm (e.g., displaying it on a display of machine 100). For example, an alarm may be generated based on controller 114 determining that the amount of fuel in machine 100 is insufficient to power machine 100 until the estimated completion time. As another example, an alarm may be generated based on controller 114 determining that the amount of time remaining in a shift, the amount of time before sunset, the amount of time before weather conditions change, or the amount of time before machine 100 is scheduled to another location is less than the estimated completion time.

[0031] Actions performed by controller 114 may include transmitting notifications (e.g., via a communication interface associated with controller 114). Notifications may indicate estimated completion time, progress, scheduling, alarms, etc. Controller 114 may cause notifications to be transmitted to user devices, such as user devices associated with a site manager or a human operator of machine 100. Controller 114 may cause notification transmission when the estimated completion time meets (e.g., exceeds) a threshold time amount. The threshold time amount may be the time at the end of a shift, a time associated with sunset, a time associated with changes in weather conditions, or the time machine 100 is scheduled to be at another location, etc.

[0032] In some implementations, controller 114 stores, or causes another device to store, information identifying the association between tasks and estimated completion times. This association can be used when machine 100 switches between multiple tasks. For example, controller 114 may store, or cause another device to store, information identifying corresponding associations between multiple tasks and multiple estimated completion times (as determined above). Thus, when a particular task among multiple tasks is selected (e.g., by a human operator), controller 114 can determine the estimated completion time of the selected task based on such associations.

[0033] A human operator of machine 100 can use operator control 202 to command the initiation of a task in autonomous mode. In some implementations, autonomous mode is automatically initiated based on one or more criteria for automatically initiating autonomous mode (e.g., by controller 114). Based on the command (e.g., operator command) used to initiate autonomous mode (e.g., via operator control 202), controller 114 can cause the task to be initiated in autonomous mode. The task is then executed in autonomous mode according to the parameters of the task described above.

[0034] Based on a command (e.g., an operator command) used to initiate the execution of a task in autonomous mode (e.g., via operator control 202), controller 114 can obtain stored information identifying the association between estimated completion times and tasks, and can cause the task to be initiated in autonomous mode based on this association. That is, controller 114 can cause a task to be initiated based on an estimated completion time (e.g., by initiating the task with a displayed estimated completion time, or by otherwise notifying a human operator, administrator, device, etc., performing the scheduling). When multiple configured tasks exist, controller 114 can selectively initiate tasks in autonomous mode based on the selection of the human operator of machine 100. In other words, when a human operator selects to initiate a task in autonomous mode, controller 114 can obtain the estimated completion time of the task based on the stored association between estimated completion times and tasks.

[0035] The autonomous mode of machine 100 provides automatic control of propulsion system 204, steering system 206, and / or working system 208 to perform tasks. That is, in autonomous mode, controller 114 provides control of propulsion system 204, steering system 206, and / or working system 208 (e.g., according to task parameters) instead of those systems manually controlled by a human operator.

[0036] The propulsion system 204 includes the systems and mechanisms of the machine 100, which perform operations related to the propulsion (e.g., forward or reverse movement of the machine 100) and braking of the machine 100. In other words, the propulsion system 204 provides the propulsion operation of the machine 100. The propulsion system 204 may include wheels 108, brakes associated with wheels 108, transmissions, other drivetrain components, etc. Therefore, in autonomous mode, the controller 114 provides automatic control of the forward movement, backward movement, speed, acceleration, braking, etc. of the machine 100.

[0037] The steering system 206 includes systems and mechanisms for performing operations related to the steering and directional movement of the machine 100. In other words, the steering system 206 provides steering operations for the machine 100. The steering system 206 may include a steering mechanism, wheels 108, etc. Therefore, in autonomous mode, the controller 114 provides automatic control of the steering, drift correction, etc., of the machine 100.

[0038] The working system 208 (e.g., a vibratory compaction system, a grading system, etc.) includes systems and mechanisms for performing operations related to the working operations of the machine 100. In other words, the working system 208 provides the working operations for the machine 100. The working operations may be compaction operations, grading operations, etc.

[0039] The working system 208 (e.g., a vibratory compaction system) may include a roller 110, a vibration mechanism for the roller 110, etc. Therefore, in autonomous mode, the controller 114 provides automatic control over the vibration of the roller 110, etc. For example, the controller 114 may maintain the vibration of the roller 110 according to a set of parameters of the task, and enable or disable the vibration of the roller 110 based on whether the machine 100 is moving or stationary.

[0040] The working system 208 (e.g., a grading system) may include blades, blade assemblies, tie rod-circumference-template assemblies, one or more hydraulic cylinders (e.g., for positioning the blades), etc. Therefore, in autonomous mode, the controller 114 provides automatic control of blade height, blade angle, whether the blade detaches from the surface material, etc. For example, the controller 114 can maintain the blade height and / or angle based on this set of parameters used in autonomous mode.

[0041] The working system 208 may include another actuator of the machine 100. Therefore, in autonomous mode, the controller 114 provides automatic control of actuator position, actuator function, etc.

[0042] When machine 100 performs a task in autonomous mode, controller 114 can determine the estimated completion time and / or the progress of the task update. The estimated completion time is based on the time elapsed since the task started (e.g., the estimated completion time is the estimated completion time minus the elapsed time). The progress of the task update is based on the movement of machine 100. For example, controller 114 can determine the percentage of the task that has been completed since the task started based on the movement of machine 100. Controller 114 can determine the movement of machine 100 based on location data (e.g., longitude and latitude coordinates) associated with the position of machine 100 since the task started. Therefore, the progress of the task update can be associated with location data that identifies the position of machine 100 when the task progress is updated.

[0043] Additionally, when machine 100 is performing a task in autonomous mode, the human operator of machine 100 can use operator control 202 to command the interruption of task execution. In some implementations, task execution is automatically interrupted based on one or more criteria (e.g., by controller 114). For example, criteria could be whether machine 100 has sufficient fuel to complete the task, whether the shift has ended, whether sunset has occurred, whether weather conditions have changed, etc.

[0044] In some cases, the execution of a task may be interrupted, allowing machine 100 to perform another task. In such cases, as described above, controller 114 may store or cause another device to store information identifying the association between update completion time and update progress (e.g., update completion time and update progress when task execution is interrupted).

[0045] Subsequently, the human operator of machine 100 can use operator control 202 to command the continuation of the task. In some implementations, the task is automatically continued based on one or more criteria (e.g., by controller 114). For example, the criteria could be whether the schedule of machine 100 has been updated, whether the schedule of the work site has been updated, etc.

[0046] Based on a command (e.g., an operator command) to continue task execution (e.g., via operator control 202), controller 114 can obtain stored information identifying the association between the updated estimated completion time and the updated progress, and cause machine 100 to continue task execution in autonomous mode. Controller 114 can cause the task to continue with the updated estimated completion time (e.g., continue the task with the displayed updated estimated completion time, or otherwise notify the human operator, administrator, device, etc., performing the scheduling). Furthermore, the association between the updated estimated completion time and the updated task progress is used to continue task execution. For example, controller 114 can determine a location associated with the progress amount and determine that the estimated completion time from that location (based on the progress amount) is the updated estimated completion time.

[0047] After a task is completed, controller 114 may perform one or more actions. Actions may include generating or updating a schedule based on task completion. Actions may include transmitting a notification indicating task completion (e.g., to a user device, server device, etc.). Actions may include determining the actual time taken to complete the task, which may be used to refine the algorithm or model used to determine the estimated completion time. Actions may include generating reports detailing the task, estimated completion time, actual completion time, task interruptions and / or continuations, etc., which may be used to identify optimal machine utilization, inefficiencies, etc.

[0048] As mentioned above, Figure 2 This is provided as an example. Other examples may differ from this combination. Figure 2 As described.

[0049] Figure 3 This is a flowchart of an example process 300 for estimating task completion time for autonomous machines. Figure 3 One or more process frames can be executed by a controller (e.g., controller 114). Alternatively, Figure 3 One or more process frames may be executed by another device or group of devices that are separate from or include the controller, such as another device or component inside or outside the machine 100.

[0050] like Figure 3 As shown, process 300 may include obtaining one or more parameters (block 310) associated with a task (e.g., a compression task) to be executed in the machine's autonomous mode. For example, a controller (e.g., using a processor, memory, storage unit, input unit, communication interface, etc.) may obtain one or more parameters as described above. The task may be one of several tasks to be executed in the machine's autonomous mode.

[0051] These one or more parameters (which can be configured by the machine operator) may relate to one or more of the following: the speed at which the machine performs the task; the area in which the machine performs the task; the width of the machine's rollers; the width of the machine's blades; the amount of overlap configured for the passage of the area; the number of passes configured for the area; the maneuvering distance used by the machine when changing the passage of the area; or the maneuvering speed used by the machine when changing the passage of the area.

[0052] like Figure 3 As further shown, process 300 may include determining an estimated completion time for the task based on one or more parameters associated with the task (block 320). For example, as described above, a controller (e.g., using a processor, memory, etc.) may determine the estimated completion time. Process 300 may include causing storage of information identifying the association between the task and the estimated completion time.

[0053] like Figure 3 As further shown, process 300 may include performing one or more actions based on the estimated completion time of the task (block 330). For example, a controller (e.g., using a processor, memory, storage unit, input unit, output unit, communication interface, etc.) may perform one or more actions based on the estimated completion time, as described above.

[0054] The one or more actions may include causing the machine's display to show at least one of the estimated completion time, the updated estimated completion time, or the task progress; generating a schedule for the machine based on at least one of the estimated completion time, the updated estimated completion time, or the task progress; updating the machine's schedule based on at least one of the estimated completion time, the updated estimated completion time, or the task progress; generating an alarm based on at least one of the estimated completion time, the updated estimated completion time, or the task progress; or causing a notification to be transmitted based on at least one of the estimated completion time, the updated estimated completion time, or the task progress.

[0055] For example, an alarm can be generated based on the determination that the amount of fuel in the machine is insufficient to power the machine until the estimated completion time. As another example, a notification can be transmitted based on the determination that the estimated completion time meets a threshold. In some implementations, process 300 may include means for transmitting information identifying the estimated completion time to the site where the machine will operate, for scheduling purposes.

[0056] Process 300 may include selectively initiating a task in the machine's autonomous mode based on the correlation between the task and the estimated completion time. Task initiation may be based on selecting (e.g., by a human operator) one task or another of several tasks. For example, the selection could be choosing the task itself and initiating it with the estimated completion time.

[0057] Process 300 may further include determining an updated estimated completion time based on the elapsed time since the task started, and determining task progress based on the machine's movement, while the machine is performing a task in autonomous mode. For example, process 300 may include obtaining location data related to the machine's position while the machine is autonomously performing a task, and determining the machine's movement based on that location data. Additionally, process 300 may include storing information identifying the association between the updated estimated completion time and task progress. Process 300 may include enabling the machine to continue performing the task in autonomous mode using the association between the updated estimated completion time and task progress after the task's execution has been interrupted.

[0058] Although Figure 3 The example box for process 300 is shown, but in some implementations, process 300 may include more than... Figure 3 The boxes depicted may be more, fewer, different, or arranged differently. Alternatively, two or more boxes of process 300 may be executed in parallel.

[0059] Industrial applicability

[0060] The disclosed completion time estimation system 200 can be used with any machine 100 capable of operating in autonomous mode. For example, the completion time estimation system 200 can be used with any machine 100 that is expected to perform a task in autonomous mode. In this way, human operators, administrators, machine 100 controllers, or other devices can perform machine 100 scheduling, site scheduling, etc., based on the estimated completion time. This can improve machine resource utilization, site scheduling, and construction time. Furthermore, the disclosed completion time estimation system 200 allows switching between tasks while maintaining the current estimated completion time of the task, thereby further improving the scheduling of the machine 100 and / or site as the task progresses.

Claims

1. A machine comprising: at least one of a propulsion system or a steering system configured to operate under automatic control in an autonomous mode of the machine; and a controller configured to: obtain one or more parameters associated with a task to be performed in the autonomous mode, wherein the task is one of a plurality of tasks to be performed in the autonomous mode; determine an estimated completion time for the task based on the one or more parameters associated with the task; perform one or more actions based on the estimated completion time for the task; cause information identifying an association between the task and the estimated completion time to be stored; and based on a selection of the task or another task of the plurality of tasks, selectively cause the task to be initiated in the autonomous mode of the machine in accordance with the association between the task and the estimated completion time.

2. The machine of claim 1, wherein the one or more parameters relate to one or more of: a speed at which the machine performs the task, an area in which the machine is to perform the task, a drum width of the machine, a blade width of the machine, an amount of overlap configured for a pass of the area, a number of passes configured for the area, a distance of maneuver used by the machine when changing a pass of the area, or a speed of maneuver used by the machine when changing a pass of the area.

3. The machine of any one of claims 1-2, wherein the controller is further configured to: cause information identifying the estimated completion time to be transmitted to a device that schedules a worksite in which the machine is to operate.

4. The machine of claim 1, wherein the selection is a selection of the task, and the task is initiated with the estimated completion time.

5. A method for task completion time estimation for a machine, comprising: obtaining one or more parameters associated with a task to be performed in an autonomous mode of the machine; determining an estimated completion time for the task based on the one or more parameters associated with the task; determining an updated estimated completion time based on a time elapsed since initiation of the task when the machine performs the task in the autonomous mode, and determining a task progress based on movement of the machine; causing information identifying an association between the updated estimated completion time and the task progress to be stored; and performing one or more actions based on the association between the updated estimated completion time and the task progress.

6. The method of claim 5, wherein the one or more actions comprise: causing a display of the machine to display at least one of the estimated completion time, the updated estimated completion time, or the task progress, generating a schedule for the machine based on at least one of the estimated completion time, the updated estimated completion time, or the task progress; updating a schedule of the machine based on at least one of the estimated completion time, the updated estimated completion time, or the task progress; generating an alert based on at least one of the estimated completion time, the updated estimated completion time, or the task progress, or ​ ​ ​ causing transmission of a notification based on at least one of the estimated completion time, the updated estimated completion time, or the task progress.

7. The method of any one of claims 5-6, further comprising: obtaining location data related to a location of the machine as the machine autonomously performs the task; and determining movement of the machine based on the location data.

8. The method of any one of claims 5-6, further comprising: causing the machine to continue performance of the task in an autonomous mode using an association between an updated estimated completion time and task progress after performance of the task has been interrupted.

9. The method of claim 8, wherein the task progress is to be used to determine a location at which the machine is to continue performance of the task, and the updated estimated completion time is to be used for the location.

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