Patent Publication Number: US-2023138671-A1

Title: Method for Using Exteroceptive Sensor Data Based on Vehicle State or Mission State

Description:
BACKGROUND 
     Autonomous vehicles and semi-autonomous vehicles are becoming more widely used. These vehicles can include a number of sensors of different types that may be more or less useful for detecting obstacles depending on the state of the autonomous vehicle. 
     SUMMARY 
     An autonomous vehicle is disclosed. The autonomous vehicle may include a sensor array; an engine output control system; a braking control system; and a controller. The controller may be communicatively coupled with the sensor array, the engine output control system, and the braking control system. The controller may be configured to: sense an environment with the sensor array to produce sensor data; receiving autonomous vehicle state data; determining whether the autonomous vehicle state data is above a threshold state value; in the event the autonomous vehicle state data is above a threshold state value, not using the sensor data to operate the autonomous vehicle; and in the event the autonomous vehicle state data is not above a threshold state value, using the sensor data to operate the autonomous vehicle. 
     The various embodiments described in the summary and this document are provided not to limit or define the disclosure or the scope of the claims. 
    
    
     
       BRIEF DESCRIPTION OF THE FIGURES 
         FIG.  1    is a block diagram of an example communication and control system for an autonomous vehicle. 
         FIG.  2    is a flowchart of an example process for determining whether to use exteroceptive sensor data based on a vehicle state or mission state. 
         FIG.  3    is a flowchart of an example process for determining whether to use exteroceptive sensor data based on a vehicle state or mission state. 
         FIG.  4    is a block diagram of an example computational system that can be used to with or to perform some embodiments described in this document. 
     
    
    
     DETAILED DESCRIPTION 
     Methods and systems for filtering or removing sensor data based on the state of an autonomous vehicle are disclosed. Some sensor data, for example, may or may not be appropriate or useful during all operational states of an autonomous vehicle. For example, some sensor data may not be valuable during low speed operation of the autonomous vehicle. Others, for example, may not be useful during high speeds of the autonomous vehicle. And yet other sensor data is not useful during dusty or stormy conditions. In many conditions, such sensor data may result in false positive identification of obstacles. Various sensor data may be filtered, adjusted, or not used based on the state of the autonomous vehicle. 
     Radar data, for example, may be useful in determining obstacles while an autonomous vehicle is operating at high speeds (e.g., speeds greater than a threshold speed of 2 m/s). But because radar may provide low resolution data, it may present false positive identification of obstacles. Thus, radar data may be used at high speeds for obstacle detection. If an object is detected based on radar data, the autonomous vehicle may begin to slow down to avoid the obstacle. Once the autonomous vehicle&#39;s speed is below a threshold speed (e.g., below 2 m/s), lidar sensor data may be used to further identify and/or characterize the obstacle detected by the radar. 
       FIG.  1    is a block diagram of an example communication and control system  100  for an autonomous vehicle. Portions of the communication and control system  100 , for example, may include a vehicle control system which may be mounted on an autonomous vehicle  110 . The autonomous vehicle  110 , for example, may include an automobile, a truck, a van, an electric vehicle, a combustion vehicle, a loader, a wheel loader, a track loader, a dump truck, a digger, a backhoe, a forklift, a dump truck, a mower, a sprayer, etc. The communication and control system  100 , for example, may include any or all components of computational system  400  shown in  FIG.  4   . 
     The autonomous vehicle  110 , for example, may include a steering control system  144  that may control a direction of movement of the autonomous vehicle  110 . The steering control system  144 , for example, may include any or all components of computational system  400  shown in  FIG.  4   . 
     The autonomous vehicle  110 , for example, may include a speed control system  146  that controls a speed of the autonomous vehicle  110 . The autonomous vehicle  110 , for example, may include an implement control system  148  that may control operation of an implement coupled with or towed by the autonomous vehicle  110  or integrated within the autonomous vehicle  110 . The implement control system  148 , for example, may include any type of implement such as, for example, a bucket, a shovel, a blade, a thumb, a dump bed, a plow, an auger, a trencher, a scraper, a broom, a hammer, a grapple, forks, boom, spears, a cutter, a wrist, a tiller, a rake, etc. The speed control system  146 , for example, may include any or all components of computational system  400  shown in  FIG.  4   . 
     The control system  140 , for example, may include a controller  150  communicatively coupled to the steering control system  144 , to the speed control system  146 , and the implement control system  148 . The control system  140 , for example, may be integrated into a single control system. The control system  140 , for example, may include a plurality of distinct control systems. The control system  140 , for example, may include any or all the components show in  FIG.  4   . 
     The controller  150 , for example, may receive signals relative to many parameters of interest including, but not limited to: vehicle position, vehicle speed, vehicle heading, desired path location, off-path normal error, desired off-path normal error, heading error, vehicle state vector information, curvature state vector information, turning radius limits, steering angle, steering angle limits, steering rate limits, curvature, curvature rate, rate of curvature limits, roll, pitch, rotational rates, acceleration, and the like, or any combination thereof. 
     The controller  150 , for example, may be an electronic controller with electrical circuitry configured to process data from the various components of the autonomous vehicle  110 . The controller  150  may include a processor, such as the processor  154 , and a memory device  156 . The controller  150  may also include one or more storage devices and/or other suitable components (not shown). The processor  154  may be used to execute software, such as software for calculating drivable path plans. Moreover, the processor  154  may include multiple microprocessors, one or more “general-purpose” microprocessors, one or more special-purpose microprocessors, and/or one or more application specific integrated circuits (ASICS), or any combination thereof. For example, the processor  154  may include one or more reduced instruction set (RISC) processors. The controller  150 , for example, may include any or all the components show in  FIG.  4   . 
     The controller  150  may be in communication with a spatial locating device  142  such as, for example, a GPS device. The spatial locating device  142  may provide geolocation data to the controller  150 . 
     The memory device  156 , for example, may include a volatile memory, such as random access memory (RAM), and/or a nonvolatile memory, such as ROM. The memory device  156  may store a variety of information and may be used for various purposes. For example, the memory device  156  may store processor-executable instructions (e.g., firmware or software) for the processor  154  to execute, such as instructions for calculating drivable path plan, and/or controlling the autonomous vehicle  110 . The memory device  156  may include flash memory, one or more hard drives, or any other suitable optical, magnetic, or solid-state storage medium, or a combination thereof. The memory device  156  may store data such as field maps, maps of desired paths, vehicle characteristics, software or firmware instructions and/or any other suitable data. 
     The steering control system  144 , for example, may include a curvature rate control system  160 , a differential braking system  162 , a steering mechanism, and a torque vectoring system  164  that may be used to steer the autonomous vehicle  110 . The curvature rate control system  160 , for example, may control a direction of an autonomous vehicle  110  by controlling a steering control system of the autonomous vehicle  110  with a curvature rate, such as an Ackerman style autonomous loader,  110  or articulating loader. The curvature rate control system  160 , for example, may automatically rotate one or more wheels or tracks of the autonomous vehicle  110  via hydraulic or electric actuators to steer the autonomous vehicle  110 . By way of example, the curvature rate control system  160  may rotate front wheels/tracks, rear wheels/tracks, and/or intermediate wheels/tracks of the autonomous vehicle  110  or articulate the frame of the loader, either individually or in groups. The differential braking system  162  may independently vary the braking force on each lateral side of the autonomous vehicle  110  to direct the autonomous vehicle  110 . Similarly, the torque vectoring system  164  may differentially apply torque from the engine to the wheels and/or tracks on each lateral side of the autonomous vehicle  110 . While the steering control system  144  includes the curvature rate control system  160 , the differential braking system  162 , and/or the torque vectoring system  164 . A steering control system  144 , for example, may include other and/or additional systems to facilitate turning the autonomous vehicle  110  such as an articulated steering control system, a differential drive system, and the like. 
     The speed control system  146 , for example, may include an engine output control system  166 , a transmission control system  168 , and a braking control system  170 . The engine output control system  166  may vary the output of the engine to control the speed of the autonomous vehicle  110 . For example, the engine output control system  166  may vary a throttle setting of the engine, a fuel/air mixture of the engine, a timing of the engine, and/or other suitable engine parameters to control engine output. In addition, the transmission control system  168  may adjust gear selection within a transmission to control the speed of the autonomous vehicle  110 . Furthermore, the braking control system  170  may adjust braking force to control the speed of the autonomous vehicle  110 . While the illustrated speed control system  146  includes the engine output control system  166 , the transmission control system  168 , and/or the braking control system  170 . A speed control system  146 , for example, having other and/or additional systems to facilitate adjusting the speed of the autonomous vehicle  110  may be included. 
     Alternatively or additionally, the autonomous vehicle may comprise an electric vehicle with an electric motor and batteries. An electric vehicle may or may not include a transmission control system  168  and/or the engine output control system  166  may be coupled with the electric motor. 
     The implement control system  148 , for example, may control various parameters of the implement towed by and/or integrated within the autonomous vehicle  110 . For example, the implement control system  148  may instruct an implement controller via a communication link, such as a CAN bus, ISOBUS, Ethernet, wireless communications, and/or Broad R Reach type 
     Automotive Ethernet, etc. 
     The implement control system  148 , for example, may instruct an implement controller to adjust a penetration depth of at least one ground engaging tool of an agricultural implement, which may reduce the draft load on the autonomous vehicle  110 . 
     The implement control system  148 , as another example, may instruct the implement controller to transition an agricultural implement between a working position and a transport portion, to adjust a flow rate of product from the agricultural implement, to adjust a position of a header of the agricultural implement (e.g., a harvester, etc.), among other operations, etc. 
     The implement control system  148 , as another example, may instruct the implement controller to adjust a shovel height, a shovel angle, a shovel position, etc. 
     The implement control system  148 , as another example, may instruct the implement controller to adjust a shovel height, a shovel angle, a shovel position, etc. 
     The controller  150 , for example, may be coupled with a sensor array  179 . The sensor array  179 , for example, may facilitate determination of condition(s) of the autonomous vehicle  110  and/or the work area. For example, the sensor array  179  may include one or more sensors (e.g., infrared sensors, ultrasonic sensor, magnetic sensors, radar sensors, Lidar sensors, terahertz sensors, sonar sensors, cameras, etc.) that monitor a rotation rate of a respective wheel or track and/or a ground speed of the autonomous vehicle  110 . In a specific example, the sensor array may include two sensors: a lidar sensor and a radar sensor or lidar sensor and an ultrasonic sensor. The sensors may also monitor operating levels (e.g., temperature, fuel level, etc.) of the autonomous vehicle  110 . Furthermore, the sensors may monitor conditions in and around the work area, such as temperature, weather, wind speed, humidity, and other conditions. The sensors, for example, may detect physical objects in the work area, such as the parking stall, the material stall, accessories, other vehicles, other obstacles, or other object(s) that may in the area surrounding the autonomous vehicle  110 . Further, the sensor array  179  may be utilized by the first obstacle avoidance system, the second obstacle avoidance system, or both. 
     The operator interface  152 , for example, may be communicatively coupled to the controller  150  and configured to present data from the autonomous vehicle  110  via a display  172 . Display data may include data associated with operation of the autonomous vehicle  110 , data associated with operation of an implement, a position of the autonomous vehicle  110 , a speed of the autonomous vehicle  110 , a desired path, a drivable path plan, a target position, a current position, etc. The operator interface  152  may enable an operator to control certain functions of the autonomous vehicle  110  such as starting and stopping the autonomous vehicle  110 , inputting a desired path, etc. The operator interface  152 , for example, may enable the operator to input parameters that cause the controller  150  to adjust the drivable path plan. For example, the operator may provide an input requesting that the desired path be acquired as quickly as possible, that an off-path normal error be minimized, that a speed of the autonomous vehicle  110  remain within certain limits, that a lateral acceleration experienced by the autonomous vehicle  110  remain within certain limits, etc. In addition, the operator interface  152  (e.g., via the display  172 , or via an audio system (not shown), etc.) may alert an operator if the desired path cannot be achieved, for example. 
     The control system  140 , for example, may include a base station  174  having a base station controller  176  located remotely from the autonomous vehicle  110 . For example, control functions of the control system  140  may be distributed between the controller  150  of the control system  140  and the base station controller  176 . The base station controller  176 , for example, may perform a substantial portion of the control functions of the control system  140 . For example, a first transceiver  178  positioned on the autonomous vehicle  110  may output signals indicative of vehicle characteristics (e.g., position, speed, heading, curvature rate, curvature rate limits, maximum turning rate, minimum turning radius, steering angle, roll, pitch, rotational rates, acceleration, etc.) to a second transceiver  180  at the base station  174 . The base station controller  176 , for example, may calculate drivable path plans and/or output control signals to control the curvature rate control system  160 , the speed control system  146 , and/or the implement control system  148  to direct the autonomous vehicle  110  toward the desired path, for example. The base station controller  176  may include a processor  182  and memory device  184  having similar features and/or capabilities as the processor  154  and the memory device  156  discussed previously. Likewise, the base station  174  may include an operator interface  186  having a display  188 , which may have similar features and/or capabilities as the operator interface  152  and the display  172  discussed previously. 
       FIG.  2    is a flowchart of an example process  200  for determining whether to use exteroceptive sensor data based on a vehicle state or mission state. Process  200  may include any number of additional blocks between, before, or after the blocks shown in process  200 . The blocks in process  200  may occur in any order. And any block in process  200  may be removed and/or replaced. 
     At block  210  sensor data may be received. The sensor data may include any sensor data from sensor array  179 . The sensor data may include infrared sensor data, ultrasonic infrared sensor data, magnetic infrared sensor data, radar infrared sensor data, Lidar infrared sensor data, terahertz infrared sensor data, sonar infrared sensor data, and/or camera data, etc. The sensor data may be received at the control system  140 . 
     At block  215  state data may be received. The state data may include autonomous vehicle speed data, autonomous vehicle velocity data, autonomous vehicle location data, autonomous vehicle direction data, map data, implement activity data, weather data, dust conditions, etc. The state data may include mission state data or autonomous vehicle state data. The sensor data may be received at the control system  140 . 
     At block  220  the state data may be analyzed to determine whether a condition has been met. If the condition has been met, then process  200  proceeds to block  230  and the sensor data is not used. If the condition has not been met, then process  200  proceeds to block  235  and the sensor data is used. After block  225  or block  230  the process  200  returns to block  210 . A pause for a period of time may be included between block  225  or block  230  and prior to block  210 . 
     At block  225  the sensor data may be sent to the system such as, for example, to other processes or algorithms within the control system  140 . At block  230  no sensor data may be sent, or a null value may be sent to the system such as, for example, to other processes or algorithms within the control system  140 . For example, at block  225  the sensor data is used to operate the autonomous vehicle and at block  230  the sensor data is not used to operate the sensor data. 
     For example, the condition may be whether the autonomous vehicle speed is greater than, less than, or equal to a condition speed value. At block  220 , the speed of the autonomous vehicle (the state data) may be analyzed to determine whether it is greater than, less than, or equal to the condition speed value. If it is, then process  200  may proceed to block  230  and specific sensor data may not be used at block  230 . 
     As another example, at block  210  ultrasonic sensor data may be received at control system  140  from an ultrasonic sensor. At block  215  velocity data may be received. At block  220 , the control system  140  may determine whether the velocity of the autonomous vehicle is less than a predetermined velocity value (e.g., 2.0, 1.5, 1, 0.5, etc. m/s) in the forward direction of the autonomous vehicle. If, for example, the velocity of the autonomous vehicle is less than the predetermined velocity value, then process  200  proceeds to block  230  and the ultrasonic sensor data is not used. If, for example, the velocity of the autonomous vehicle is greater than the predetermined velocity value, then process  200  proceeds to block  225  and the ultrasonic sensor data is used. 
     As another example, at block  210  lidar sensor data may be received at control system  140  from a LIDAR sensor. At block  215  speed data may be received. At block  220 , the control system  140  may determine whether the speed of the autonomous vehicle is less than a predetermined speed value such as, for example, a predetermined speed value that may be dependent on the deceleration rate of the autonomous vehicle and/or the range of the LIDAR sensor (e.g., less than about 2 m/s, 1.5 m/s, 2 m/s, 0.5 m/s 0.25 m/s, etc. m/s). If, for example, the speed of the autonomous vehicle is less than the predetermined speed value, then process  200  proceeds to block  225  and the LIDAR sensor data is used. If, for example, the speed of the autonomous vehicle is greater than the predetermined speed value, then process  200  proceeds to block  230  and the LIDAR sensor data is not used. 
     As another example, at block  210  lidar sensor data (and/or other sensor data) may be received at control system  140  from an LIDAR sensor. At block  215  an implement state may be determined whether the implement is in a dusty state. A dusty state may include, for example, whether a bucket on an autonomous loader is being raised or has been raised, whether a bucket has been dumped, whether another vehicle has passed the autonomous vehicle or is about to pass the autonomous vehicle, or a plow or shovel on an autonomous plow is engaged, a shovel on an autonomous digger is engaged, etc. If the implement state is determined to be a dusty state at block  220 , then process  200  proceeds to block  230  and lidar sensor data (and/or other sensor data) is not used. 
     As another example, at block  210  lidar sensor data (and/or other sensor data) may be received at control system  140  from an LIDAR sensor. At block  215  weather state data may be received. A weather state may include, for example, whether there is rain, snow, hail, fog, high wind, sunny weather, time of day, sun position, temperature, etc. If the weather state is determined to include rain, snow, hail, or high wind at block  220 , then process  200  proceeds to block  230  and lidar sensor data (and/or other sensor data) is not used. 
     As another example, the condition may be whether the sensor data includes data from within a map area previously defined as being restricted. At block  220 , the location of the sensor data (or portions of the sensor data) may be analyzed to determine whether it is within the map area. If it is, then process  200  may proceed to block  230  and the sensor data (or portions of the sensor data) may not be used at block  230 . 
       FIG.  3    is a flowchart of an example process  300  for determining whether to use exteroceptive sensor data based on a vehicle state or mission state. Process  300  may include any number of additional blocks between, before, or after the blocks shown in process  300 . The blocks in process  300  may occur in any order. And any block in process  300  may be removed and/or replaced. 
     Process  300  includes blocks  210 ,  215 ,  229 , and  225  from process  200 . Block  230  from process  200 , however, is replaced by block  330 . At block  330 , the sensor data may be filtered or adjusted. Filtered data may include any type of mathematical filter such as, for example, a geometry filter, classification filter, machine-learned classification filter, deep-learned classification filter, etc. An adjustment may include adjustments such as, for example, adjusting the contrast, adjusting the sensor sensitivity, adjusting the algorithm sensitivity, adjusting the magnitude, adjusting the weighing of sensor data, etc. As another example, an adjustment may include adjusting algorithm parameters based on the vehicle state or mission state. 
     For example, if the condition is based on a map area, the sensor data that falls within the map area may be filtered or removed from the sensor data. As another example, if the environmental state is dusty or there is precipitation the contrast on some sensor data may be increased. As another example, if the speed of the autonomous vehicle is above or below a specific value, then some sensor data may be filtered or adjusted. 
     The computational system  400 , shown in  FIG.  4    can be used to perform any of the examples described in this document. For example, one or more computational systems  400  or components thereof can be used to execute process  200  and/or process  300 . As another example, computational system  400  can perform any calculation, identification and/or determination described here. Computational system  400  includes hardware elements that can be electrically coupled via a bus  405  (or may otherwise be in communication, as appropriate). The hardware elements can include one or more processors  410 , including without limitation one or more general-purpose processors and/or one or more special-purpose processors (such as digital signal processing chips, graphics acceleration chips, and/or the like); one or more input devices  415 , which can include without limitation a mouse, a keyboard and/or the like; and one or more output devices  420 , which can include without limitation a display device, a printer and/or the like. 
     The computational system  400  may further include (and/or be in communication with) one or more storage devices  425 , which can include, without limitation, local and/or network accessible storage and/or can include, without limitation, a disk drive, a drive array, an optical storage device, a solid-state storage device, such as a random access memory (“RAM”) and/or a read-only memory (“ROM”), which can be programmable, flash-updateable and/or the like. The computational system  400  might also include a communications subsystem  430 , which can include without limitation a modem, a network card (wireless or wired), an infrared communication device, a wireless communication device and/or chipset (such as a Bluetooth device, an 802.6 device, a Wi-Fi device, a WiMax device, cellular communication facilities, etc.), and/or the like. The communications subsystem  430  may permit data to be exchanged with a network (such as the network described below, to name one example), and/or any other devices described in this document. In many embodiments, the computational system  400  will further include a working memory  435 , which can include a RAM or ROM device, as described above. 
     The computational system  400  also can include software elements, shown as being currently located within the working memory  435 , including an operating system  440  and/or other code, such as one or more application programs  445 , which may include computer programs of the invention, and/or may be designed to implement methods of the invention and/or configure systems of the invention, as described herein. For example, one or more procedures described with respect to the method(s) discussed above might be implemented as code and/or instructions executable by a computer (and/or a processor within a computer). A set of these instructions and/or codes might be stored on a computer-readable storage medium, such as the storage device(s)  425  described above. 
     In some cases, the storage medium might be incorporated within the computational system  400  or in communication with the computational system  400 . In other embodiments, the storage medium might be separate from a computational system  400  (e.g., a removable medium, such as a compact disc, etc.), and/or provided in an installation package, such that the storage medium can be used to program a general-purpose computer with the instructions/code stored thereon. These instructions might take the form of executable code, which is executable by the computational system  400  and/or might take the form of source and/or installable code, which, upon compilation and/or installation on the computational system  400  (e.g., using any of a variety of generally available compilers, installation programs, compression/decompression utilities, etc.) then takes the form of executable code. 
     Unless otherwise specified, the term “substantially” means within 5% or 10% of the value referred to or within manufacturing tolerances. Unless otherwise specified, the term “about” means within 5% or 10% of the value referred to or within manufacturing tolerances. 
     The conjunction “or” is inclusive. 
     The terms “first”, “second”, “third”, etc. are used to distinguish respective elements and are not used to denote a particular order of those elements unless otherwise specified or order is explicitly described or required. 
     Numerous specific details are set forth to provide a thorough understanding of the claimed subject matter. However, those skilled in the art will understand that the claimed subject matter may be practiced without these specific details. In other instances, methods, apparatuses or systems that would be known by one of ordinary skill have not been described in detail so as not to obscure claimed subject matter. 
     Some portions are presented in terms of algorithms or symbolic representations of operations on data bits or binary digital signals stored within a computing system memory, such as a computer memory. These algorithmic descriptions or representations are examples of techniques used by those of ordinary skill in the data processing arts to convey the substance of their work to others skilled in the art. An algorithm is a self-consistent sequence of operations or similar processing leading to a desired result. In this context, operations or processing involves physical manipulation of physical quantities. Typically, although not necessarily, such quantities may take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared or otherwise manipulated. It has proven convenient at times, principally for reasons of common usage, to refer to such signals as bits, data, values, elements, symbols, characters, terms, numbers, numerals or the like. It should be understood, however, that all of these and similar terms are to be associated with appropriate physical quantities and are merely convenient labels. Unless specifically stated otherwise, it is appreciated that throughout this specification discussions utilizing terms such as “processing,” “computing,” “calculating,” “determining,” and “identifying” or the like refer to actions or processes of a computing device, such as one or more computers or a similar electronic computing device or devices, that manipulate or transform data represented as physical electronic or magnetic quantities within memories, registers, or other information storage devices, transmission devices, or display devices of the computing platform. 
     The system or systems discussed are not limited to any particular hardware architecture or configuration. A computing device can include any suitable arrangement of components that provides a result conditioned on one or more inputs. Suitable computing devices include multipurpose microprocessor-based computer systems accessing stored software that programs or configures the computing system from a general-purpose computing apparatus to a specialized computing apparatus implementing one or more embodiments of the present subject matter. Any suitable programming, scripting, or other type of language or combinations of languages may be used to implement the teachings contained in software to be used in programming or configuring a computing device. 
     Embodiments of the methods disclosed may be performed in the operation of such computing devices. The order of the blocks presented in the examples above can be varied—for example, blocks can be re-ordered, combined, and/or broken into sub-blocks. Certain blocks or processes can be performed in parallel. 
     The use of “adapted to” or “configured to” is meant as open and inclusive language that does not foreclose devices adapted to or configured to perform additional tasks or steps. Additionally, the use of “based on” is meant to be open and inclusive, in that a process, step, calculation, or other action “based on” one or more recited conditions or values may, in practice, be based on additional conditions or values beyond those recited. Headings, lists, and numbering included are for ease of explanation only and are not meant to be limiting. 
     While the present subject matter has been described in detail with respect to specific embodiments thereof, it will be appreciated that those skilled in the art, upon attaining an understanding of the foregoing, may readily produce alterations to, variations of, and equivalents to such embodiments. Accordingly, it should be understood that the present disclosure has been presented for purposes of example rather than limitation, and does not preclude inclusion of such modifications, variations and/or additions to the present subject matter as would be readily apparent to one of ordinary skill in the art.