Patent Publication Number: US-2023138075-A1

Title: Tracking the environment around a machine to define actual cut profile

Description:
TECHNICAL FIELD 
     The present disclosure is related to defining an actual cut profile for a machine. More specifically, the present disclosure relates to systems and methods for generating cut profiles and providing near real-time controls to the machine via a machine-controlled feedback loop. Additionally, the present disclosure relates to systems and methods of determining the actual volume of material removed at a worksite. 
     BACKGROUND 
     At a paving worksite, one or more pieces of paving equipment, such as a cold planer, can be used to remove a portion of a roadway, parking lot, or other such work surface in order to expose a paving surface. Once the portion of the work surface has been removed, a paving machine, such as an asphalt paver, may distribute, profile, and partially compact heated paving material (e.g., asphalt) onto the paving surface. One or more compaction machines may then be used to further compact the paving material until a desired paving material density has been reached. 
     While removing portions of the work surface with the cold planer, it may be useful to know the actual cut depths on each edge of the cold planer, in order to determine whether the machine is cutting too shallow and/or too deep. However, determining actual cut depths often requires personnel at the worksite to manually measure the cut depths for each side of the machine. If the cut depths are outside of a desired range, a cold planer operator may adjust various settings on the cold planer in order to achieve the desired cut depth. Not only is this process potentially dangerous for the worksite personnel, but this process also causes delays and inefficiencies at the worksite, resulting in increased costs. 
     Additionally, it can be useful to know the volume or tonnage of material that has been milled and/or removed from a work surface in order to coordinate the delivery of asphalt to the worksite. However, determining volume removed can be difficult, and estimates of such volumes are often inaccurate. This results in increased costs and inefficiencies at the worksite. 
     An example system for determining milled volume or milled area of a milled surface is described in U.S. Pat. No. 9,121,146 (hereinafter referred to as the &#39;146 reference). For instance, the &#39;146 reference describes a process for determining a volume of material milled as a function of a cross-sectional area of material to be milled in front of the milling drum and a distance traveled by the construction machine while actively milling. As explained in the &#39;146 reference, the cross-sectional area is determined in part by direct machine observation of one or more profile characteristics of a ground surface in front of the milling drum. Additionally, the &#39;146 reference describes that the surface area milled is determined as a function of the width of the area to be milled in front of the milling drum and a distance traveled by the construction machine while actively milling. 
     Although the system described in the &#39;146 reference is configured to determine a volume of material milled as a function of a cross-sectional area of material to be milled in front of the milling drum and a distance traveled by the construction machine while actively milling, the system described in the &#39;146 reference is not configured to, for example, measure the actual cut depth at the sides or rear of the machine, generate an actual cut profile for the rear of the machine, or determine an actual volume of material removed by the machine using the actual cut profile. As a result, the system described in the &#39;146 reference may inefficient, inaccurate, and costly. 
     Example embodiments of the present disclosure are directed toward improving upon the various paving systems described above. 
     SUMMARY OF THE INVENTION 
     In an example of the present disclosure, a method includes receiving, by a processor of a computing device and from a controller of a machine disposed at a worksite, via a network, first information captured by one or more first sensors carried by the machine. The processor generates, based at least partly on the first information, a first profile of a work surface in front of the machine and determines, based at least partly on the first profile, an estimated volume of material removed from the worksite by the machine. The method further includes receiving, by the processor and from the controller of the machine, via the network, second information captured by one or more second sensors carried by the machine. The processor generates, based at least partly on the second information, a second profile of a cut surface behind a cutting tool of the machine and determines, based at least partly on the second profile, a first actual cut depth associated with a first cut edge of the machine and a second actual cut depth associated with a second cut edge of the machine. The processor determines, a difference between one or more of a first expected cut depth associated with the first cut edge and the first actual cut depth or a second expected cut depth associated with the second cut edge and the second actual cut depth, and generates an instruction configured to cause the machine to perform a desired operation, based at least partly on the difference. 
     In another example of the present disclosure, a system includes a machine disposed on a work surface of a worksite and configured to remove at least part of the work surface, a first sensor carried by frame of the machine and disposed at a front end of the machine, the first sensor being configured to capture first information indicative of a first profile of a first portion of the work surface proximate the front end of the machine, a second sensor carried by the frame of the machine and disposed at a back end of the machine opposite the front end, the second sensor being configured to capture second information indicative of a second profile of a second portion of the work surface proximate the back end of the machine, the second portion of the work surface comprising a portion that has been acted on by the machine, and a processor operably connected to a display of a device. The processor is configured to receive the first information captured by the first sensor, generate, based at least partly on the first information, the first profile of the first portion of the work surface, determine, based at least partly on the first profile, an estimated volume of material removed from the worksite by the machine, receive the second information from the second sensor, generate, based at least partly on the second information, the second profile of the second portion of the worksite, determine, based at least partly on the second profile, an actual cut depth associated with a cut edge of the machine, determine a difference between an expected cut depth associated with the cut edge and the actual cut depth, and generate an instruction configured to cause the machine to perform a desired operation, based at least partly on the difference. 
     In yet another example of the present disclosure, one or more non-transitory computer-readable storage media storing instructions that, when executed by a processor, cause the processor to perform acts comprising receiving, via a network, first information captured by one or more first sensors carried by a machine disposed at a worksite, generating, based at least partly on the first information, a first profile of a work surface in front of the machine, determining, based at least partly on the first profile, an estimated volume of material removed from the worksite by the machine, receiving, via the network, second information captured by one or more second sensors carried by the machine, generating, based at least partly on the second information, a second profile of a cut surface behind a cutting tool of the machine, determining, based at least partly on the second profile, an actual cut depth associated with a cut edge of the machine, determining a difference between an expected cut depth associated with the cut edge and the actual cut depth, and generating, by the processor, an instruction configured to cause the machine to perform a desired operation, based at least partly on the difference. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a schematic illustration of a system in accordance with an example of the present disclosure. The example system shown in  FIG.  1    includes a cold planer and a hauling machine. 
         FIG.  2    a side view of a machine having a cutting system, an anti-slabbing system, a conveyor system, and a partial-cut-width sensor system. 
         FIG.  3    is another illustration of the cutting system, anti-slabbing system, conveyor system, and partial-cut-width sensor system of  FIG.  2   . 
         FIG.  4    is a schematic view of an example laser profile scanner system configured to perform triangulation-type measurements of partial-cut widths. 
         FIG.  5    is a perspective view of the laser profile scanner system of  FIG.  4    showing a scanned edge of a partial-cut width strip of material in front of a machine. 
         FIG.  6    is another perspective view of the laser profile scanner system of  FIGS.  4  and  5   , showing a scanned edge of a partial-cut width strip of material behind a machine. 
         FIG.  7    is a flow chart depicting an example method associated with the system shown in  FIG.  1   . 
     
    
    
     DETAILED DESCRIPTION 
     Wherever possible, the same reference numbers will be used throughout the drawings to refer to the same or like parts. Referring to  FIG.  1   , an example system  100  includes one or more machine(s), such as machine  102  and second machine  104 , moveable along a work surface  106  of a worksite  108  to perform various tasks at the worksite  108 . The example system  100  includes at least one example machine  102  configured for use in one or more excavating, hauling, compacting, paving, or other such processes. The machine  102  is illustrated as a cold planer  102  which may be used, for example, for road or highway constructions, and other allied industries. Alternatively, the machine  102  may be any other machine used for depositing asphalt, concrete, or like materials. The second machine  104  is illustrated as a hauling machine  104 . A hauling machine  104  refers to any machine that carries the excavated materials between different locations within worksite  108 . Examples of hauling machines  104  include an articulated truck, an off-highway truck, an on-highway dump truck, and a wheel tractor scraper, among other types of hauling machines  104 . Laden hauling machines  104  carry overburden from areas of excavation within worksite  108 , along haul roads to various dump sites, and return to the same or different excavation areas to be loaded again. Under normal conditions, similar co-located hauling machines  104  perform about the same with respect to productivity and efficiency when exposed to similar site conditions. 
     Each of the machines described herein includes a frame, one or more engines (e.g., diesel engines), battery packs, fuel cells, or other power sources supported by the frame and configured to drive and/or otherwise provide power to various components of the machines, and a display operably connected to a controller  110 . In such examples, a power source of a particular machine may provide power to drive movement of the machine along the work surface  106  of the worksite  108 . Such power sources may also provide power to energize, drive, activate, and/or otherwise operate various parasitic loads (e.g., hydraulic cylinders/systems, cooling systems, electronic systems, pneumatic systems, etc.) of the machine. Each of the machines described herein are in communication with each other and/or with a local or remote system  114  by way of a network  112 . The remote system  114  is remotely located from the worksite  108 . 
     The network  112  comprises a local area network (“LAN”), a WiFi direct network, wireless LAN (“WLAN”), a larger network such as a wide area network (“WAN”), or a collection of networks, such as the Internet. Protocols for network communication, such as TCP/IP, are used to implement the network  112 . Although embodiments are described herein as using a network  112  such as the Internet, other distribution techniques may be implemented that transmit information via memory cards, flash memory, or other portable memory devices. The network  112  facilitates wireless communication between the machines described herein and/or between controllers  110  of such machines and, for example, a system controller  116  and/or processor(s)  132  of the remote system  114 , for the purpose of transmitting and/or receiving operational data. 
     The system controller  116  and/or the controllers  110  may be an electronic controller that operates in a logical fashion to perform operations, execute control algorithms, store and retrieve data, and other desired operations. The system controller  116  and controllers  110  include and/or access memory, secondary storage devices, processors, and any other components for running an application. The memory and secondary storage devices can be in the form of read-only memory (ROM) or random-access memory (RAM) or integrated circuitry that is accessible by the controller. Various other circuits are associated with the system controller  116  and controllers  110  such as power supply circuitry, signal conditioning circuitry, driver circuitry, and other types of circuitry. 
     The system controller  116  and/or a controller  110  is a single controller or includes more than one controller. As used herein, the term “controller” is meant in its broadest sense to include one or more controllers, processors, central processing units, and/or microprocessors that are associated with the system  100 , and that may cooperate in controlling various functions and operations of the machines included in the system  100 . The functionality of the system controller  116  and/or the controllers  110  are implemented in hardware and/or software without regard to the functionality. The system controller  116  and/or the controllers  110  may rely on one or more data maps, look-up tables, neural networks, algorithms, machine learning algorithms, and/or other components relating to the operating conditions and the operating environment of the system  100  that may be stored in the memory of the system controller  116  and/or the memory of controllers  110 . Each of the data maps, look-up tables, neural networks, and/or other components noted above includes a collection of data in the form of tables, graphs, and/or equations to maximize the performance and efficiency of the system  100  and its operation. As will be described in greater detail below, the controllers  110 , system controller  116 , and/or processor(s)  132  are configured to receive various types of data (e.g., worksite data, operations data, raw sensor data, sensor data, etc.) from a controller  110  of the machine  102 , generate cut profiles using the data, provide near real-time controls to the machine  102  via a machine-controlled feedback loop, and determine the actual volume of material removed at a worksite  108 . 
     The machine  102  also includes one or more ECU(s)  118  such as, for example, an electronic control module (ECM), a powertrain control module (PCM), a transmission control module (TCM), a brake control module (EBCM), a central control module (CCM), a central timing module (CTM), a general electronic module (GEM), a body control module (BCM), a suspension control module (SCM), and a control unit, among other types of ECUs. The ECU(s)  118  include hardware and embedded software that assist in the operation of the machines  102 ,  104 . In some examples, the ECU(s)  118  are included as part of controller  110 . 
     Communication devices  120  are operably connected to a controller  110  and communicatively connected to network  112 . In some examples, communication devices  120  are configured to permit wireless transmission of a plurality of signals and/or information between controllers  110  and system controller  116 . Such communication devices  120  may also be configured to permit communication with other machines and systems remote from the worksite  108 . For example, such communication devices  120  includes a transmitter configured to transmit signals (e.g., over the network  112 ) to a receiver of one or more other such communication devices  120 . In such examples, each communication device  120  may also include a receiver configured to receive such signals (e.g., over the network  112 ). In some examples, the transmitter and the receiver of a particular communication device  120  is combined as a transceiver or other such component. In any of the examples described herein, the respective controllers  110  of the machines  102 ,  104 , and/or other machines of the system  100  are substantially similar to and/or the same as the system controller  116 , and includes one or more of the same components thereof. 
     In any of the examples described herein, the communication devices  120  also enable communication (e.g., over the network  112 ) with computing device(s)  122 . The communication device  120  may also be configured to permit wireless transmission of a plurality of signals, instructions, and/or information between the machine  102  and one or more servers, processors, computers, one or more tablets, computers, cellular/wireless telephones, personal digital assistants, mobile devices, computing devices  122 , or other electronic devices, and/or other components of a remote system  114 . Such a remote system  114  may be located at the worksite  108 . Alternatively, one or more components of the remote system  114  may be located remote from the worksite (e.g., at a back office). It is understood that the remote system  114 , and its respective components, may be part of and/or otherwise included in the system  100 . In some examples, the communication device  120  comprises a user interface  124  that displays information to an operator of the machine  102 . The user interface  124  is operatively coupled to the controller  110  and/or application  126 . 
     Computing device  122  comprises one or more tablets, computers, cellular/wireless telephones, personal digital assistants, mobile devices, or other electronic devices located at the worksite  108  and/or remote from the worksite  108 . Computing device  122  comprises one or more processor(s)  132  that include and/or access memory, secondary storage devices, and any other components for running an application. The memory and secondary storage devices can be in the form of read-only memory (ROM) or random-access memory (RAM) or integrated circuitry that is accessible by the processor(s)  132 . In some examples, computing device  122  comprises a mobile phone and/or tablet of worksite personnel (e.g., project managers, foremen, supervisors, etc.) overseeing daily operations at the worksite  108 . For example, the computing device  122  stores worksite data associated with the worksite in a datastore. The worksite data includes information about a first planned depth of cut in a first area of the worksite (e.g., cut a road surface to a first depth) and a second planned depth of cut in a second area of the worksite (e.g., cut an intersection of two roads to a second depth). Accordingly, an application  128  on computing device  122  communicates with machine  102  in order to generate actual cut profiles based on sensor information from sensor(s)  130  of the machine  102  and/or sensor(s)  130  of the second machine  104 . 
     As illustrated in  FIG.  1   , the example machine  102  includes one or more sensor(s)  130  that are carried by and/or mounted to a frame of the machine  102  and configured to capture sensor data in an environment surrounding the machine  102  (e.g., sensors  130  disposed on the machine  102 ). The sensor(s)  130  may be disposed at one or more locations of the machine  102  (e.g., in front of a cutting drum and/or at the front of the machine  102 , behind a cutting drum and/or at the rear of the machine  102 , along the sides of the machine  102 , and/or any other location on the machine  102 ). The sensor(s)  130  include lidar sensors, radar sensors, cameras (e.g., red/green/blue (RGB), infrared (IR), intensity, depth, time of flight, etc.), proximity sensors, cut-depth sensors, audio sensors, ultrasonic transducers, sonar sensors, location sensors (e.g., global positioning system (GPS), compass, etc.), inertial sensors (e.g., inertial measurement units, accelerometers, magnetometers, gyroscopes, etc.), environment sensors (e.g., temperature sensors, humidity sensors, light sensors, pressure sensors, etc.), laser scanners, light emitting diode (LED) scanners, 3-D scanners, 2-D scanners, and the like. 
     In some examples, sensor(s)  130  comprise one or more laser profile scanners (e.g., LPS 36 Laser Measurement System available from Leuze electronic GmbH &amp; Co. KG of Owen, Germany, and/or the LMS100 Laser Measurement System available from Sick, AG of Waldkirch, Germany). In some examples, the sensor(s)  130 A and  130 B comprise one or more laser scanners, LED scanners, three-dimensional scanners, and/or two-dimensional sensors. 
     In some examples, the sensor(s)  130  are located on the front, sides, and rear of the machine  102  and configured to detect obstacles, people, etc. In some examples, the sensor(s)  130  also capture data associated with an area around the machine  102  and send the data to processor(s)  132  of a computing device  122 . The processor(s)  132  use the data to make a determination if an event (e.g., a potential collision) is about to occur between the machine  102  and an object, personnel, or the like at the worksite  108 . For instance, where the processor(s)  132  sensor(s)  130  determine that an event is about to occur, the processor(s)  132  send a desired operation (e.g., a notification, alert, and/or instruction to a controller  110  of the machine  102 ). For instance, the desired operation can include instructing the machine  102  to stop, causing an alert to be displayed on a user interface  124  of a communication device  120  of the machine  102 , among other things. In some examples, the determination of whether an event is about to occur and/or detecting a variety of things at the worksite  108  is performed by a controller  110  of the machine  102 , a system controller  116  and/or processor(s)  132  of a remote system  114 , and/or off-board processor(s)  132  of a computing device  122 . 
     In some examples, a controller  110  of the machine  102  receives raw sensor data from the sensor(s)  130  of the machine  102 . The controller  110  of the machine  102  may send (e.g., using application  126  on communication device  120  and via network  112 ) the raw sensor data to a processor  132  of a computing device  122 . The processor  132  of the computing device  122  processes the raw sensor data (e.g., via image processing, and/or other machine-trained mechanisms) and generates profile(s) for the machine  102 . In some examples, the raw sensor data comprises image data from the sensor(s)  130 . In some examples, the processor  132  of the computing device  122  generates a first cut profile of the machine  102  based on sensor data received from sensor(s)  130  located in a front portion of the machine  102 . For instance, where machine  102  comprises a cold planer  102 , the first cut profile generated by the processor(s)  132  is based on sensor data received from sensor(s) located in front of a cutting drum (e.g., milling drum) of the cold planer  102 . The processor  132  of the computing device  122  also generates a second cut profile based on sensor data received from sensor(s) located on a back portion of the machine  102 . For instance, where machine  102  comprises the cold planer  102 , the processor  132  of the computing device  122  generates a second cut profile corresponding to an actual cut profile for an area behind the cutting drum of the cold planer  102 . In some examples, the actual cut profile includes a substantially vertical cut edge defining a cut depth, and a substantially horizontal cut width, where the cut edge and the cut width are formed on the work surface by the cutting tool of the machine. 
     In some examples, processor(s)  132  of the computing device  122  use the generated cut profile(s) to perform one or more desired operations. For instance, the processor  132  of the computing device  122  determines an estimated volume removed based on the first cut profile of the machine  102 . The processor  132  of the computing device  122  can also determine an actual volume removed using the second cut profile (e.g., actual cut profile) of the machine  102 . By combining the first cut profile and the second cut profile, the processor  132  of the computing device  122  can determine a more accurate actual volume of material removed from the worksite  108  by the machine  102 . Additionally, the processor  132  of the computing device  122  determines, based on the actual cut profile, whether to generate and send one or more instruction(s), alert(s), notification(s), etc., to the controller  110  of the machine  102 . In some examples, the instruction(s), alert(s), notification(s), etc., cause, when executed by the controller  110  of the machine  102 , the controller  110  to perform one or more desired operations (e.g., adjust one or more components of the machine  102 , display an alert, notification, etc. on the user interface  124  of the communication device  120 , among other things). 
     Thus, the systems and methods described herein utilize sensor(s)  130  on a machine  102  (e.g., such as sensor(s)  130  located on the front end, sides, and/or back end of the machine  102 ) to track an environment around (e.g., in front of and behind) the machine  102  to generate profiles. The front profile is used to determine an expected volume of material removed. The back profile (e.g., actual cut profile) of the machine  102  is used to determine an actual volume of material removed by the machine  102 . The actual cut profile can also include actual cut depth(s) associated with cut edge(s) of the machine  102 . The actual cut depth(s) can be used to provide real-time controls to a controller  110  of the machine  102 . 
       FIG.  2    illustrates an example side view of the machine  102  discussed above with respect to  FIG.  1   . As will be explained in greater detail below, and as shown in  FIG.  2   , the example machine  102  includes a cutting system  214 , an anti-slabbing system  230 , a partial-cut-width sensor system  244 , and/or a conveyor system  252 . In the illustrated example, the machine  102  comprises a cold planer  102 . 
     As illustrated, the machine  102  comprises a frame  202 . The frame  202  comprises a front frame end  204  and a back frame end  206 . The machine  102  further comprises front propulsion elements  208  and back propulsion elements  210 , which are coupled to the frame  202  proximate to the front frame end  204  and the back frame end  206 , respectively. The front propulsion elements  208  and back propulsion elements  210  comprise two parallel ground engaging tracks, although the present disclosure is not thereby limited. In some examples, the machine  102  is configured to drive over material  228 , such that front propulsion elements  208  roll on an uncut work surface  106 . The machine  102  is also configured to remove material  228  from the work surface  106  (e.g., such as a road way and leave a cut surface  250  (e.g., a surface from which paving material has been completely removed or a surface of paving material from which an upper-most layer of paving material has been removed) behind. In some examples, the back propulsion elements  210  roll on the cut surface  250  and the cutting system  214  produces an edge of the material  228  between the work surface  106  and the cut surface  250 . 
     As illustrated, the machine  102  further comprises an operator control station  212 . The operator control station  212  is coupled to the frame  202  and comprises a controller  110  configured to perform control and monitoring functions of the machine  102  and a computing device  120 , as described above. The machine  102  includes a cutting system  214 , which is coupled to the frame  202 . The cutting system  214  comprises a housing  216  that defines a cutting chamber  218  for rotatable cutting drum  220  that is carried by the frame  202  of the machine  102 . 
     The machine  102  further comprises elevation control legs, or support posts,  222 A and  222 B configured to raise and lower the housing  216  relative to the work surface  106  and/or the frame  202 , including the cutting chamber  218  with the cutting drum  220 , typically in conjunction with adjustments to a cutting depth of the cutting system  214 . In some examples, the machine  102  includes side plate cylinders  224 , which are configured to adjust the height of one or more side plate(s)  226 . The cutting system  214  further includes a cutting tool, such as rotatable cutting drum  220  that rotates in a direction counter to a forward travel direction of machine  102 . Rotatable cutting drum  220  is fixed within housing  216  and configured to cut material  228  of the work surface  106  underlying the machine  102 . 
     As illustrated, the machine  102  comprises an anti-slabbing system  230 . The anti-slabbing system  230  is coupled to the housing  216  of the cutting chamber  218  and includes an upwardly oriented base plate  232  relative to the work surface  106 , extending across a front side of the cutting chamber  218 . The anti-slabbing system  230  further comprises a plow  234  for pushing loose objects lying upon material  228 , and a plurality of skids  236 . Sides of the cutting chamber  218  that are adjacent to the cutting tool  220  of the cutting system  214  are enclosed by the side plates  226 . The machine  102  further comprises a primary conveyor  238 , which is coupled to and supported by the base plate  232 . The primary conveyor  238  feeds material  228  cut via the cutting drum  220  to a secondary conveyor  240  projecting forward of the front frame end  204 . The secondary conveyor  240  is coupled to a positioning mechanism  242 , which enables left, right, and up and down position control of the secondary conveyor  240 . In some examples, the secondary conveyor  240  is configured to deposit the material  228  into a receptacle, such as a box of a hauling machine (e.g., second machine  104 ). 
     The machine  102  is configured to travel in a forward direction  254  to remove material  228 . The anti-slabbing system  230  travels over the top of material  228  to prevent or inhibit the material  228  from becoming prematurely dislodged during operations for removal of the material  228 . A rotatable cutting drum  220  follows behind the anti-slabbing system  230  to engage the material  228 . The rotatable cutting drum  220  is configured to rotate in a counter-clockwise direction (from the perspective of  FIG.  2   ), such that the material  228  is uplifted and broken up into small pieces by cutting teeth of the rotatable cutting drum  220 . The anti-slabbing system  230  is configured to contain pieces of material  228  within a cutting chamber  218 . The removed pieces of the material  228  are pushed up primary conveyor  238  of the conveyor system  252  and carried in a forward direction  254 , such as by an endless belt, to a secondary conveyor  240  of the conveyor system  252 . The secondary conveyor  240  is cantilevered forward of the front frame end  204  to be positioned over a collection vessel, such as the box of a second machine  104  (e.g., a hauling machine). 
     As illustrated, the machine  102  comprises a partial-cut-width sensor system  244  for determining a width at which the cutting system  214  is cutting the material  228 . The partial-cut-width sensor system  244  comprises a mounting bar  248  and a sensor array  246 . In some examples, the mounting bar  248  is mounted within the machine  102  in front of the cutting system  214  and positions the sensor array  246  across material  228 . In an example, mounting bar  248  is directly connected to the frame  202 . In some examples, the mounting bar  248  is indirectly connected to the frame  202 , such as by attachment to an upper portion of the anti-slabbing system  230  or a lower portion of the anti-slabbing system  230 . In some examples, the partial-cut-width sensor system  244  comprises a plurality of sensors  130  located at intervals across the width of the cutting drum  220 . In some examples, the sensors  130  comprise discrete distance sensors. However, other types of partial-cut-width sensor systems can be used. 
     As illustrated, the machine  102  comprises sensor(s)  130  disposed at various additional locations (e.g., side(s) and/or back end) on the machine  102 . For instance, sensor(s)  130  are configured to capture sensor data in an environment surrounding the machine  102  and include lidar sensors, radar sensors, cameras (e.g., RGB, IR, intensity, depth, time of flight, etc.), cut-depth sensors, audio sensors, ultrasonic transducers, sonar sensors, location sensors (e.g., GPS, compass, etc.), inertial sensors (e.g., inertial measurement units, accelerometers, magnetometers, gyroscopes, etc.), environment sensors (e.g., temperature sensors, humidity sensors, light sensors, pressure sensors, etc.), laser scanners, LED scanners, 3-D scanners, 2-D scanners, and the like. In some examples, the sensor(s)  130  are mounted to the frame  202  of the machine  102 . In some examples, the sensor(s) are fixed to the frame  202  of the machine. For instance, in some examples, one or more of the sensor(s)  130  are mounted at a fixed position on the frame  202  of the machine  102 , such that a field of view of the one or more sensor(s)  130  remains fixed relative to the frame  202  of the machine  102 . In some examples, one or more of the sensor(s)  130  are mounted to the frame  202  of the machine  102  based on the horizon. For instance, in some examples, the machine  102  operates on a work surface  106  that is angled (e.g., inclined or declined). In this example, the one or more of the sensors  130  can be mounted to the frame of the machine using a dynamic mount (e.g., a gimbal mount, or any other similar mount), such that the one or more sensor(s)  130  present a fixed field of view relative to the horizon and/or an axis that is not governed by the angle of the machine  102 . In some examples, the sensor(s)  130  may be disposed at any location on the machine  102 . For instance, one or more sensor(s)  130  may be disposed at one or more location(s) and/or portion(s) of any component(s) of one or more of the cutting system  214 , the anti-slabbing system  230 , the partial-cut-width sensor system  244 , and/or the conveyor system  252  described herein. 
     In some examples, the data from the sensor(s)  130  is used to evaluate the operation and performance of the machine  102 . For instance, roadways are typically multiple times wider than the width of a machine  102 . As such, multiple passes of the machine  102  are used to remove the complete width of the material  228 . However, not all passes of machine  102 , particularly the last pass, extend across a width of material  228  that takes up the full width of cutting system  214  (e.g., the width of cutting drum  220 ). If the partial-cut-width is not taken into account, material removal volume calculations can become skewed, particularly when long stretches of roadway material is being removed. 
     Accordingly, the processor(s)  132  and/or controller(s)  110 ,  116  can be used to evaluate productivity measurements of machine  102  in real-time during an operation and/or at the end of an operation (e.g., cut path, removal of material  228 , etc.). For instance, the machine  102  is configured to remove material  228  at a certain depth (e.g., the difference in height between work surface  106  and cut surface  250 ). As such, the sensor(s)  130  can collect raw sensor data (e.g., associated with cut depth of the cutting system  214 ), which can be sent to a computing device  122 . The processor(s)  132  of the computing device  122  process the raw sensor data (e.g., via image processing and/or machine-trained mechanism(s)) and generates one or more profiles (e.g., cut profiles) indicative of the amount, location, physical dimensions, and/or other characteristics of the material  228  removed by the machine  102 . Based on the one or more profiles, the processor(s)  132  of the computing device  122  can determine, in near real-time, the actual cut depth (e.g., distinguishes between cut and uncut surface). In some examples, the raw sensor data may comprise a speed of the machine, a distance traveled by the machine, along with other data. Processor(s) (e.g., processor(s)  132  of the computing device  122  and/or processor(s)  132  of the remote system  114 ) and/or a controller (e.g., controller  110  and/or system controller  116 ) determine based at least in part on the raw sensor data and a width of the cutting system  214 , a volume (e.g., expected volume and/or actual volume) of material  228  removed by the machine  102  for a given period of time. As described below, the partial-cut-width sensor system  244 , and others described herein, are configured to sense how much of the width of the cutting drum  220  the cutting system  214  is actually cutting material  228 , thereby providing a more accurate determination of volume removed. 
     Machine-learning mechanisms can include, but are not limited to supervised learning algorithms (e.g., artificial neural networks, Bayesian statistics, support vector machines, decision trees, classifiers, k-nearest neighbor, etc.), unsupervised learning algorithms (e.g., artificial neural networks, association rule learning, hierarchical clustering, cluster analysis, etc.), semi-supervised learning algorithms, deep learning algorithms, etc.), statistical models, etc. In at least one example, machine-trained data models can be stored in memory associated with the computing device  122  and/or the remote system  114  for use during operation of the machine  102 . 
     In some examples, data collected by partial-cut-width systems described herein, as well as other information collected by machine  102 , is sent off-board of machine  102  for processing and evaluating at locations outside of machine  102  (e.g., such as computing device  122  and/or remote system  114 ), thereby saving machine  102  from having to include expensive data processing hardware and software having to be frequently updated and/or reduces the risk of damage due to the harsh environments in which the machine  102  is used. While processing of sensor data and determination of volume(s), generation of profile(s) is described herein as occurring off-board of the machine  102  (e.g., at the computing device  122 , the processing, determination(s), generation(s), etc. described herein may be performed by one or more controller(s)  110  on-board the machine  102  and/or system controller(s)  116  of a remote system  114 ). 
       FIG.  3    is another illustration of the cutting system, anti-slabbing system, conveyor system, and partial-cut-width sensor system of  FIG.  2   . As illustrated in  FIG.  3   , the example machine  102  includes the cutting system  214 , anti-slabbing system  230 , partial-cut-width sensor system  244 , and conveyor system  252 , of  FIG.  2    described above. 
     As illustrated in greater detail and described above, the cutting system  214  comprises a rotatable cutting drum  220  positioned behind side plate  226 . Side plate  226  is attached to housing  216  (not shown) defining the cutting chamber  218  (not shown). As described above, the anti-slabbing system  230  is coupled to housing  216  (not shown) defining the cutting chamber  218  (not shown) in front of the cutting drum  220 . As illustrated, the anti-slabbing system  230  comprises plow  234 , to which are mounted skids  236  (not shown) and base plate  232  (not shown). As described above, primary conveyor  238  (not shown) is coupled to the anti-slabbing system  230  and extends from a first end proximate to the anti-slabbing system  230  to a second end forward of anti-slabbing system  230  within the machine  102 . 
     As illustrated, front propulsion element  208 , comprises an endless track  302 . The front propulsion element  208  is coupled to frame  202  by elevation control leg  222 A. As described above, elevation control leg  222 A is operated by controller  110  of the machine  102 . For instance, when the controller  110  of the machine  102  receives instructions related to elevation control leg  222 A. In some examples, the controller  110  executes the instructions and causes the elevation control leg  222 A to change the position of the frame  202  relative to material  228  to, for example, assist in changing the cutting depth of cutting system  214 . Although illustrated in an elevated position above material  228 , the anti-slabbing system  230  can be lowered to rest on top of material  228  at work surface  106 . 
     In some examples, the cutting system  214  is lowered such that side plate  226  rests on the cut surface  250  adjacent to the material  228 . As the front propulsion element  208  (and back propulsion element  210  (not shown)) moves the machine  102  along material  228 , the cutting drum  220  engages an edge of the material  228 . In some examples, such as during a full width pass, material  228  extends all the way across the cutting drum  220  between side plates  226 . In other examples, and as illustrated in  FIG.  3   , during a partial width pass, material  228  extends across a portion of the cutting drum  220  between side plates  226 . In this example, the computing device  122  determines an expected volume of material  228  removed based on data comprising (i) a depth measurement for depth do (equal to the difference in distances d 1  (e.g., difference in elevation and/or depth between sensor array  246  and work surface  106 ) and d 2  (e.g., difference in elevation and/or depth between sensor array  246  and cut surface  250  that is adjacent to the machine  102 ) for work surface  106  and cut surface  250 ), (ii) a width of the cutting drum  220 , (iii) a travel distance measurement of machine  102 . In some examples, the depth measurement do is determined using a data from a cut depth sensor  130 . In some examples, the machine  102  is operating such that the material  228  is cut across, in a Z-direction with respect to an axis shown in the perspective views of  FIGS.  4 - 6    below, a partial width of the cutting drum  222 . In this example, the sensor array  246  includes a plurality of sensors  130  spaced across the width of machine  102  to determine the proportion of the width of cutting drum  220  that is removing material  228 . The determined partial cut width is used in conjunction with the data (e.g., sensor data, operational data, etc.) described above to determine the expected volume of material  228  removed by the machine  102 . 
       FIGS.  4 - 6    illustrate schematic and perspective view(s) of a laser profile scanner system  400  of a machine  102 . While the laser profile scanner system  400  is described as measuring distance to objects using triangulation methods, other methods (e.g., measuring distances using time of flight of reflected signals) may be used. 
       FIG.  4    illustrates a schematic side view  400  for a laser profile scanner system  400  of a machine  102 . The laser profile scanning system  400  comprises a first sensor  130 A and a second sensor  130 B (collectively referred to as sensor(s)  130 ). Sensor(s)  130  are configured to view work surface  106  (e.g., ground in front of drum  220 ). As illustrated, work surface  106  includes a first edge  402 A and a second edge  402 B. The first edge  402 A comprises a previously cut edge by the machine  102 . The second edge  210 B comprises an edge currently being cut by the drum  220  (e.g., cutting drum  220 ) of the machine  102 . In some examples, the first sensor  130 A and the second sensor  130 B are included as part of sensor array  246  described above. In some examples, the exact positions of the first sensor  130 A (e.g., the source), and the second sensor  130 B (e.g., the receiver) relative to the frame  202  of the machine  102 , as well as the angle  406  (e.g., an angular orientation of the first sensor  130 A and the second sensor  130 B relative to the frame  202 ) between the first sensor  130 A and the second sensor  130 B are known to a computing device  122  and/or remote system  114 . For instance, memory of the computing device  122  and/or remote system  114  can store the exact positions of the first sensor  130 A and the second sensor  130 B, and the angle  406 . 
     In some examples, the first sensor  130 A and second sensor  130 B comprise laser profile scanning devices suitable for determining a profile of a surface (e.g., work surface  106 ) in front of the drum  220 . In some examples, a triangulation method is used to measure distances to objects. For instance, in some examples, the first sensor  130 A comprises a laser source and the second sensor  130 B comprises a receiver. In some examples, the machine  102  is moving (e.g., advancing) along work surface  106  in a direction  408 . The first sensor  130 A (e.g., the source) projects a laser beam  404  downward onto the work surface  106  directly in front of the advancing cutting drum  220 . As illustrated, the second sensor  130 B (e.g., the receiver) receives reflected light  410  from the first sensor  130 A. 
     As illustrated in  FIG.  5   , the laser profile scanning system  400  includes a displacement or step  502  (e.g., difference in elevation between work surface  106  and cut surface  250 ) that is detected by processor(s)  132  in laser beam  404 . As described above, the exact positions of the first sensor  130 A (e.g., the source), and the second sensor  130 B (e.g., the receiver) relative to the frame  202  of the machine  102 , as well as the angle  406  (e.g., an angular orientation of the first sensor  130 A and the second sensor  130 B relative to the frame  202 ) between the first sensor  130 A and the second sensor  130 B are known to a computing device  122  and/or remote system  114 . For instance, memory of the computing device  122  and/or remote system  114  can store the exact positions of the first sensor  130 A and the second sensor  130 B, and the angle  406 . Accordingly, the processors  132  of the computing device  122  and/or processors  132  of the remote system  114  can access the known positions from memory of the respective devices and use the known positions of the sensors  130  and the angle  406  between the sensors  130  to determine the position of the step  502  representing the location of the first edge  402 A (e.g., relative to second edge  402 B (e.g., edge currently being cut)), using triangulation. 
     In some examples, the first sensor  130 A (e.g., the source sensor), and the second sensor  130 B (e.g., the receiver), can, alone or with the assistance of controller  110  or off-board computing device  122 , measure the distance between the machine  102  and one or more of the work surface  106  and/or the cut surface  250 . For instance, the signals can be transformed into a Cartesian coordinate system projected onto work surface  106 . As illustrated,  FIG.  5    includes a displacement and/or step  502  (e.g., corresponding to a depth (e.g., difference in height) between work surface  106  and cut surface  250 ). In some examples, the step  502  is detected (e.g., by the first sensor  130 A and/or second sensor  130 B alone, or with the assistance of controller  110  or the off-board computing device  122 ), using mathematical gradient methods. In some examples, the position of previously cut edge  402 A relative to the side of cutting drum  220  that is proximate to the second edge  402 B is determined using the step  502 . In some examples, the position of the previously cut edge  402 A relative to the side of cutting drum  220  that is proximate to edge  402 B is used to determine a percentage of the width of drum  220  that is actually cutting work surface  106 . 
       FIG.  6    illustrates another perspective view of the laser profile scanning system  400  of  FIGS.  4  and  5   , as used to perform the triangulation process described above. As illustrated,  FIG.  6    shows a scanned edge of a partial-cut width of material behind a machine  102 . As illustrated, the laser profile scanning system  400  comprises a third sensor  130 D and a fourth sensor  130 D (not shown) (collectively referred to as sensor(s)  130 ). Sensor(s)  130  are configured to view cut surface  250  (e.g., ground behind the cutting drum  220 ). As illustrated, cut surface  250  includes an indication  608  of a first edge  402 A and a second edge  402 B. The indication  608  of the first edge  402 A comprises the previously cut edge by the machine  102 . The second edge  210 B comprises an edge currently being cut by the cutting drum  220  of the machine  102 . 
     In some examples, the third sensor  130 C and the fourth sensor  130 D (not shown) are one or more types of sensor(s)  130 , described above. In some examples, the exact positions of the third sensor  130 C and the fourth sensor  130 D relative to the frame  202  of the machine  102  are known and stored in memory of a computing device  122  and/or remote system  114 . For instance, as described above, sensor(s)  130 C and  130 D can be mounted to the frame  202  of the machine  102 . For instance, in some examples, the sensor(s)  130 C,  130 D are mounted at a fixed position on the frame  202  of the machine  102 , such that a field of view of the one or more sensor(s)  130 C,  130 D remains fixed relative to the frame  202  of the machine  102 . In some examples, one or more of the sensor(s)  130 C,  130 D are mounted to the frame  202  of the machine  102  based on the horizon. For instance, in some examples, the machine  102  operates on a work surface  106  that is angled (e.g., inclined or declined). In this example, one or more of the sensors  130 C,  130 D are mounted to the frame  202  of the machine  102  using a dynamic mount (e.g., a gimbal mount, or any other similar mount), such that the one or more of sensor(s)  130 C,  130 D present a fixed field of view relative to the horizon and/or an axis that is not governed by the angle (e.g., the incline or decline) of the machine  102 . 
     In some examples, the exact positions of the third sensor  130 C (e.g., the receiver), and the fourth sensor  130 D (e.g., the source) relative to the frame  202  of the machine  102 , as well as the angle (not shown) (e.g., an angular orientation of the third sensor  130 C and the fourth sensor  130 D relative to the frame  202 ) between the third sensor  130 C and the fourth sensor  130 D are known to a computing device  122  and/or remote system  114 . For instance, memory of the computing device  122  and/or remote system  114  can store the exact positions of the third sensor  130 C and the fourth sensor  130 D, and the angle. In some examples, a distance  612  between a position of the third sensor  130 C and/or fourth sensor  130 D (relative to the frame  202  of the machine  102  and/or the cut surface  250 ) and the cutting drum  220  (relative to the frame  202  of the machine  102  and/or the cut surface  250 ) is known and stored in memory. 
     In some examples, the third sensor  130 C and fourth sensor  130 D (not shown) comprise laser profile scanning devices suitable for determining a profile of a surface (e.g., cut surface  250 ) behind the cutting drum  220 . In some examples, a triangulation method is used to measure distances to objects. For instance, in some examples, the third sensor  130 C comprises a receiver and the fourth sensor  130 D (not shown) comprises a laser source. In some examples, the machine  102  is moving (e.g., advancing) along work surface  106  in a direction  408 . The third sensor  130 C (e.g., the receiver) receives reflected light  602  from a laser beam  606  that is projected by the fourth sensor  130 D downward onto the cut surface  250  behind the advancing cutting drum  220 . 
     As described above, the exact positions of the third sensor  130 C (e.g., the receiver), and the fourth sensor  130 D (e.g., the source sensor (not shown)) relative to the frame  202  of the machine  102 , and an angle (not shown) between sensors  130 C and  130 D are known to a computing device  122  and stored in memory. For instance, memory of the computing device  122  and/or memory of the remote system  114  can store the exact positions of the third sensor  130 C and the fourth sensor  130 D, and the angle. Accordingly, processor(s)  132  of a computing device  122  and/or remote system  114  can access the known positions from memory of the respective devices and use the known positions of the sensors  130 C and  130 D (not shown) and the angle between the sensors  130  to determine the position of the step  604 , using triangulation. In some examples, the step  604  comprises an actual cut depth associated with the second edge  402 B. 
     In some examples, the third sensor  130 C (e.g., the receiver sensor), and the fourth sensor  130 D (e.g., the source sensor), can, alone or with the assistance of controller  110  of the machine  102  or processor(s)  132  of an off-board computing device  122  and/or remote system  114 , measure the distance between the machine  102  and one or more of the work surface  106  and/or the cut surface  250 . For instance, the signals can be transformed into a Cartesian coordinate system projected onto work surface  106 . As illustrated, a second displacement and/or step  604  is detected. In some examples, the second step  604  is detected (e.g., by the third sensor  130 C and/or fourth sensor  130 D alone, or with the assistance of controller  110  of the machine, or processor(s)  132  of the off-board computing device  122  and/or the remote system  114 ), using mathematical gradient methods. 
     In some examples, the computing device  122  further stores one or more of the distance  612  between the third sensor  130 C and the cutting drum  220 , a determined cut width  610  (e.g., partial cut width and/or full cut width of the cutting drum  220 ) in front of the cutting drum  220 , and/or a first cut depth  614  associated with the first edge  402 A. In some examples, the first cut depth  614  comprises an actual cut depth associated with the first edge  402 A. Accordingly, the processor(s)  132  of the computing device  122  and/or remote system  114  generate one or more profiles (e.g., cut profiles) for the machine  102 , based at least in part on the sensor data received from sensor(s)  130  on the machine  102 . 
     Accordingly, the systems and methods described herein for utilize sensor(s)  130  on a machine  102  to track an environment around (e.g., in front of and behind) the machine  102  to generate actual cut profiles and determine actual cut volume. Such systems and methods are used to more accurately determine costs at the worksite  108 , as well assist with real-time control of cutting drum  220  height during operation of the machine  102 . 
       FIG.  7    is a flow chart depicting an example method  700  associated with the system  100  shown in  FIG.  1   . The process is illustrated as logical flow graphs, each operation of which represents a sequence of operations that may be implemented in hardware, software, or a combination thereof. In the context of software, the operations represent computer-executable instructions stored on one or more computer-readable storage media that, when executed by one or more processors, perform the recited operations. Generally, computer-executable instructions include routines, programs, objects, components, data structures, and the like that perform particular functions or implement particular abstract data types. The order in which the operations are described is not intended to be construed as a limitation, and any number of the described operations may be combined in any order and/or in parallel to implement the processes. Although any of the processes or other features described with respect to the method  700  may be performed by processor(s)  132  of a computing device  122 , for ease of description, the example method  700  will be described below as being performed by the processor(s)  132  and/or system controller  116  of the remote system  114  (e.g., back end server) unless otherwise noted. 
     As shown in  FIG.  7   , at  702 , one or more processor(s)  132  receive first information captured by first sensor(s)  130  carried by a machine  102  disposed at worksite  108 . In some examples, the processor(s)  132  are associated with computing device  122 . In some examples, the processor(s)  132  are associated with the remote system  114  (e.g., such as back-end servers). The processor(s)  132  receive the first information from a controller  110  of the machine  102  via a network  112 . In some examples, the first information comprises one or more of raw sensor data (e.g., unprocessed sensor data), image data (processed and/or unprocessed), and/or sensor data (e.g., processed sensor data) associated with the first sensor(s)  130 , a distance traveled by the machine  102 , a time (e.g., duration) the machine  102  has been operating, width of a distance between the sensor(s) and a cutting tool of the machine  102 , speed of the machine  102 , a width of the cutting tool of the machine  102 , images associated with the sensors, a partial cut width associated with the cutting tool of the machine  102 , and/or any other data detected by the sensor(s). 
     In some examples, the first sensor(s)  130  are disposed at a first location on the machine  102 . For instance, where the machine  102  comprises a cold planer  102 , the processor(s)  132  receive the first information from the first sensor(s)  130  included as part of the sensor array  246  described above. In this example, the first information comprises raw sensor data and/or sensor data associated with sensor(s)  130  positioned in front of a cutting drum  220  of the machine  102 . 
     At  704 , the processor(s)  132  generate, based at least partly on the first information, a first profile of a work surface in front of the machine  102 . In some examples, the processor(s)  132  process the raw sensor data to generate the sensor data (e.g., processed sensor data) and generate the first profile using the sensor data. In some examples, the first profile comprises a location associated with a front end of the machine  102 . For instance, where the first sensor(s)  130  are positioned in front of a cutting drum, the first profile comprises an area in front of the cutting drum  220 , across at least the width of the machine  102 , and/or extending in front of the machine  102 . In some examples, the first profile includes a substantially vertical cut edge defining a cut depth, and a substantially horizontal cut width, the substantially vertical cut edge and the substantially horizonal cut width being formed on the work surface  106  by the cutting tool (e.g., cutting drum  220 ) of the machine  102   
     At  706 , the processor(s)  132  determine, based at least partly on the first profile, an estimated volume of material removed by the machine  102  from the worksite  108 . As described above, the processor(s)  132  determine the expected volume of material removed from the worksite  108  by the machine  102  using data comprising (i) a depth measurement for depth do (equal to the difference in distances d 1  and d 2  for work surface  106  and cut surface  250 ), (ii) a width of the cutting drum  220  (e.g., full width and/or partial cut width), (iii) a travel distance measurement of machine  102 . In some examples, the depth measurement do is determined using a data from a cut depth sensor, such as a sensor  130  on sensor array  246  described above. 
     At  708 , the processor(s)  132  receive second information captured by one or more second sensor(s) carried by the machine  102 . The processor(s)  132  receive the second information from the controller  110  of the machine  102 . In some examples, the processor(s)  132  receive the second information via the network  112 . In some examples, the second information comprises one or more of raw sensor data (e.g., unprocessed sensor data) and/or sensor data (e.g., processed sensor data) associated with the second sensor(s), a distance traveled by the machine  102 , a time (e.g., duration) the machine  102  has been operating, width of a distance between the sensor(s) and a cutting tool of the machine  102 , speed of the machine  102 , a width of the cutting tool of the machine  102 , images associated with the second sensor(s), laser scans, a partial cut width associated with the cutting tool of the machine  102 , and/or any other data detected by the second sensor(s). In some examples, the second sensor(s) comprise one or more of sensor(s)  130  described above. 
     In some examples, the second sensor(s) are disposed at a second location on the machine  102 . In some examples, the second location is different from the first location. For instance, where the machine  102  comprises a cold planer  102 , the first sensor(s)  130  are included as part of the sensor array  246  described above and the second sensor(s) comprise sensor(s)  130  described above. As described above, sensor(s)  130  are located in one or more position(s) behind the cutting drum of the machine  102  and may be mounted to the machine  102  in various ways. In this example, the second information comprises raw sensor data, image data, and/or sensor data associated with sensor(s)  130  positioned behind the cutting drum  220  of the machine  102 . 
     At  710 , the processor(s)  132  generate, based at least partly on the second information, a second profile of a cut surface behind a cutting tool of the machine. In some examples, the processor(s)  132  process raw sensor data included in the second information to generate the sensor data (e.g., processed sensor data) and generate the second profile using the sensor data. In some examples, the second profile comprises a location associated with a back end of the machine  102 . In some examples, the second profile includes a substantially vertical cut edge defining a cut depth, and a substantially horizontal cut width, the substantially vertical cut edge and the substantially horizonal cut width being formed on the work surface  106  by the cutting tool (e.g., cutting drum  220 ) of the machine  102 . In some examples, the cutting tool comprises cutting drum  220  of the machine  102  and the second profile comprises an area behind the cutting drum  220 . In some examples, the second profile includes an area across at least the width of the machine  102 , and/or extending behind the machine  102 . 
     At  712 , the processor(s)  132  determine, based at least partly on the second profile, an actual cut depth associated with a cut edge of the machine. In some examples, the processor(s)  132  determine a second actual cut depth associated with a second cut edge of the machine. In some examples, the actual cut depth associated with the cut edge of the machine  102  comprises a depth  614  of the first edge  402 A described above. In some examples, the second actual cut depth comprises a second depth  604  of the second edge  402 B described above. 
     At  714 , the processor(s)  132  determine a difference between one or more of an expected cut depth associated with the first cut edge and the actual cut depth. In some examples, the processor(s) additionally, or alternatively determine a difference between a second expected cut depth associated with the second cut edge and the second actual cut depth. In some examples, one or more of the difference(s) are determined based on comparing the actual cut depth to an expected cut depth associated with the first cut edge of the machine and/or comparing the second actual cut depth to a second expected cut depth associated with the second cut edge of the machine. In some examples, the expected cut depth and/or the second expected cut depth can be stored in memory of the controller  110 , computing device  122 , and/or remote system  114 . In some examples, the expected cut depth and/or the second expected cut depth is a value set by a person (e.g., an operator of the machine  102 , a foreman, etc.) at the worksite  108 , prior to operation of the machine  102 . For instance, where the machine  102  comprises a cold planer, the expected cut depth associated with the first cut edge (e.g.,  402 A and/or right side of the cutting drum  220 ) and/or the second expected cut depth associated with the second cut edge (e.g.,  402 B and/or left side of the cutting drum  220 ) of the machine  102  of the machine  102  can be set to any depth (e.g., zero millimeters, 75 millimeters, etc.) and stored in the memory. In some examples, the expected cut depth and/or the second expected cut depth comprises a depth that the cutting drum  220  of the machine  102  is set at to cut the material  228  of the work surface  106 . In some examples, such as where the machine  102  comprises a grading machine, the expected cut depth and/or the second expected cut depth are defined based on a grade and/or a slope (e.g., an angle) of the machine. For instance, where a work surface is angled, the right side of the cutting drum may be lower than the center of the machine  102  compared to the left side of the cutting drum  220 . In this example, the right side of the cutting drum is set, such that an expected cut depth (e.g., such as 70 millimeters) and slope (e.g., such as 3%) are defined. In this example, the left side of the cutting drum  220  is set to have a second expected cut depth (e.g., such as 75 millimeters). In some examples, the left-hand side of the cutting drum  220  also defines a slope. 
     At  716 , the processor(s)  132  determine if a difference between the actual cut depth and the expected cut depth (and/or a difference between the second actual cut depth and the second expected cut depth) is greater than a threshold difference. In some examples, the threshold difference comprises a predetermined difference (e.g., 5 millimeters, 10 millimeters, etc.) set by the operator of the machine  102  and/or other worksite personnel at the worksite  108 . Although  716  is described with respect to comparing a difference between an actual cut depth and an expected cut depth to a threshold, it is understood that any metric can be used. In some examples, the metric comprises a difference between the actual volume removed and the expected volume removed. 
     Where the processor(s)  132  determine that a difference is greater than or equal to the threshold difference, the method proceeds to  718 . At  718 , the processor(s)  132  generate an instruction configured to cause the machine  102  to perform a desired operation. For instance, where the machine  102  comprises a cold planer, the processor(s)  132  determine a desired operation based on determining that the threshold difference is exceeded. In some examples, the desired operation comprises raising and/or lowering a cutting drum  220  of the machine  102 . For instance, where the machine&#39;s  102  expected cut depth is 75 millimeters and the actual cut depth is 81 millimeters, the processor(s)  132  compare the different (e.g., 6 millimeters) to a threshold difference. In this example, the threshold difference is 5 millimeters, accordingly, the processor(s)  132  determine the machine  102  is cutting above the threshold and generates an instruction to cause the controller  110  of the machine  102  to adjust the expected cut depth associated with the first cut edge. For instance, the instruction can indicate to the controller  110  to raise and/or lower the side of the cutting drum  220  associated with the first cut edge to be within the threshold difference. In some examples, the desired operation is associated with any controllable component of the machine  102  operably connected to a controller  110 . For instance, in some examples, the desired operation comprises an automated steering correction, automated acceleration of the machine  102 , an automated deceleration of the machine  102 , automated start of the machine  102 , automated stop of the machine  102 , among other things. 
     At  720 , the processor(s)  132  send the instruction to a controller  110  of the machine  102 . In some examples, the instruction is sent via the network  112 . In some examples, the controller  110  of the machine  102  is configured to execute the instruction and, based on executing the instruction, cause the machine  102  to perform the desired operation. In some examples, the processor(s)  132  send the instruction to the controller  110  to cause the controller  110  to execute the desired operation in near real-time. The processor(s)  132  continue to monitor the first sensor(s)  130  and/or second sensor(s)  130  as the machine  102  continues along a worksite  108 . 
     Accordingly, the systems and methods described herein for utilize sensor(s)  130  on a machine  102  to track an environment around (e.g., in front of and behind) the machine  102  to generate cut profiles. Such systems and methods are used to more effectively operate machine(s) at a worksite  108  and improve efficiency at the worksite  108 . For instance, by providing real-time feedback in the form of instructions to control an operation of the machine  102 , worksite safety and efficiency is improved. 
     Where the processor(s)  132  determine that the difference is less than the threshold difference, the method proceeds to  722 . In some examples, the method proceeds to  722  following execution of  720 . In some examples, the processor(s)  132  continue to monitor sensor(s)  130 . While  722  is described as occurring where the determination for  716  is a “NO” and/or after  720 , it is understood that  722  may be performed following  714  and/or simultaneously with at least  716 . 
     At  722 , the processor(s)  132  determine, based at least partly on the second profile, an actual volume of material  228  removed by the machine  102  from the worksite  108 . As described above, the actual volume of material  228  removed by the machine  102  from the worksite  108  it determined based on the second profile. For instance, the second profile comprises data including (i) a depth measurement for depth do (equal to the difference in distances d 1  and d 2  for work surface  106  and cut surface  250 ), (ii) a width measurement of the cutting drum  220  (e.g., full width and/or partial cut width), (iii) a travel distance measurement of machine  102 . In some examples, the depth measurement do is determined using a data from a cut depth sensor  130 , as described above. 
     In some examples, the second profile indicates that the machine  102  is removing a higher amount of volume than expected, the machine  102  is removing less volume than expected, and/or whether portions of the cut surface  250  include causes for concern (e.g., piece(s) of cutting drum  220  are broken, middle of the cut surface  250  is broken and/or not removed, etc.). For instance, where the worksite  108  comprises a roadway, the roadway comprises layer(s) of asphalt that is laid on top of concrete using a binder. In some examples, the binder is worn, such that when the machine  102  cuts the asphalt, some of the concrete that has not been cut by the cutting drum  220  is also removed. Accordingly, the second profile can indicate that extra material  228  has been removed from the worksite  108 , resulting in a higher volume of material  228  removed than expected. As the machine  102  continues to travel along a worksite  108 , the actual volume of material  228  removed is updated. In some examples, the actual volume of material  228  removed is updated in real-time. In some examples, the actual volume of material removed is updated intermittently (e.g., in intervals of time, based on a distance traveled by the machine, etc.). Accordingly, by using sensor(s)  130  positioned at the back end of the machine  102  to scan and create a second profile (e.g., actual cut profile) for an area behind the cutting drum  220 , the techniques described herein provide a more accurate determination of actual volume removed by the machine  102 . 
     At  724 , the processor(s)  132  generate one or more report(s). In some examples, the one or more report(s) are associated with billing information, machine trip information, coordination with other machine(s) at the worksite  108 , among other things. In some examples, the processor(s)  132  cause at least one of the one or more report(s) to be displayed on a display of a computing device  122 . In some examples, the computing device  122  is associated with a foreman at the worksite  108 . For instance, trip information comprises a trip meter indicating how much the machine  102  has traveled. Based on the trip meter, the one or more report(s) can indicate how much weight of material  228  has been offloaded onto a second machine  104  (e.g., such as a hauling machine). In this example, a foreman at the worksite can utilize the one or more report(s) to prevent machine(s) from being sent out overloaded and/or underloaded, thereby reducing costs and improving efficiency of the machine(s). In some examples, the one or more reports are generated at the remote system  114 . In some examples, the actual volume removed and/or the one or more reports are sent, via the network  112  to the remote system  114 . 
     Accordingly, the systems and methods described herein for utilize sensor(s)  130  on a machine  102  to track an environment around (e.g., in front of and behind) the machine  102  to generate cut profiles. 
     INDUSTRIAL APPLICABILITY 
     The present disclosure describes systems and methods for enabling automatic control of cut depth of a machine via a machine-controlled feedback loop and providing a manager of a worksite with machine profile(s) and actual volume of material removed by a machine  102 . Such systems and methods are used to more effectively operate machines, such as one or more of the machines  102 ,  104  described above, at a worksite. For example, such systems and methods enable a manager of the worksite to reduce personnel on the worksite, improve safety, and more effectively coordinate operations of other machines based on profile(s) and actual volume of material removed determinations by a machine  102 . 
     As a result, use of the systems and methods of the present disclosure reduces the computational resources, control infrastructure, and cost required to perform various operations at the worksite  108 , thereby improving the efficiency of the system  100 . In particular, the systems described herein can be implemented without using worksite personnel and corresponding resources. As a result, use of personnel, equipment, and/or other components typically associated with paving systems can be avoided. 
     While aspects of the present disclosure have been particularly shown and described with reference to the embodiments above, it will be understood by those skilled in the art that various additional embodiments may be contemplated by the modification of the disclosed machines, systems and methods without departing from the spirit and scope of what is disclosed. Such embodiments should be understood to fall within the scope of the present disclosure as determined based upon the claims and any equivalents thereof.