Abstract:
An operating system is provided for controlling an unmanned vehicle. The system includes a stratified plurality of instruction layers, a behavior axiom block and a set of operation parameters. The instruction layers are substantially arranged in descending priority order. Each layer provides an information signal to either an adjacent descending layer or an operation device on board the unmanned vehicle. The behavior axiom block provides an independent protocol signal to a first instruction layer in said stratified plurality. The operation parameters provide an environmental condition that neighbors the unmanned vehicle to a second instruction layer. Preferably, the behavior axiom block includes prioritization adjustment to an instruction layer for overriding the information signal from an adjacently ascendant layer, such as by an interrupt signal.

Description:
STATEMENT OF GOVERNMENT INTEREST 
     The invention described was made in the performance of official duties by one or more employees of the Department of the Navy, and thus, the invention herein may be manufactured, used or licensed by or for the Government of the United States of America for governmental purposes without the payment of any royalties thereon or therefor. 
    
    
     BACKGROUND 
     The invention relates generally to flexible command and control of an autonomous surface vehicle. In particular, the invention provides a stratified structure of instructions to achieve a mission objective operating within constraint protocols. 
     Conventional operational methods employ remote control signal devices provided by a human that views sensor information from the unmanned vehicle or from other sources to send steering commands to the vehicle. Some limited autonomy is available for situations without obstacles, traffic, or enemy in which waypoints are issued to the surface vehicle follow with a simple autopilot on board. The vehicle uses Global Positioning System or a similar system to hold the boat on bearing to the next waypoint. Commercial boat autopilots are available for this purpose for commercial and recreational boating applications to reduce human workload. For these systems, human monitoring remains necessary in the event of traffic or obstacles. For such situations or when the weather obscures visibility or interferes with stability, direct human control of the vehicle steering is required to ensure operational safety and achievement of the vehicle&#39;s mission. 
     An example of where direct human intervention is required is the case of steering relative to an oncoming wave to prevent rollover. Operations such as docking or rendezvous with a command platform all require direct human control of the ship steering. In a situation where the USV supports combat operations, there can be traffic present (including both friendly and hostile) which require human intervention to direct the activities of the vehicle by remote control. In addition, complex missions such as intercepting a potentially hostile incoming boat would require direct human control via a remote link. 
     Some autonomy is available in missile and aircraft autopilot and missile guidance systems. Aircraft and missile autopilots deal with narrowly defined missions such as stabilization of the aircraft or execution of a commanded turn to a new heading. These automated functions are fairly limited in nature and are designed to work in a rather scripted process. 
     The greatest amount of autonomy in aircraft systems occurs in the microburst recovery systems for commercial aircraft. Because the limited time required to respond stresses the human reaction time, consensus is developing of the utility to provide limited autonomy to the system to fly the vehicle out of the microburst. This represents a very scripted and optimized flight procedure. Trajectory guidance for an autonomous land attack cruise missile follows a scripted mission without significant flexibility. This limits autonomous operation to a fire-and-forget weapon such as the Tomahawk cruise missile, rather than an unscripted reconnaissance platform such as the Global Hawk aircraft that requires real time flexibility. 
     Current methods for controlling an unmanned surface vehicle require increased manning requirements for the command platform operating that surface vehicle. Additionally, the current approach to remote control operation of unmanned vessels exhibits decreased functionality during certain periods because the human operators degrade by fatigue or lack of trained personnel. There are also limitations on the mission because of the limited human capabilities. Advanced automatic systems are anticipated be able to pilot the ship in inclement weather conditions better than human operated systems. This arises from the ability to design the system to use sensor input rather than organic feedback to a human operator such as Doppler measurement of water speed and from the faster response time of automated systems. 
     SUMMARY 
     Conventional unmanned vehicle control systems yield disadvantages addressed by various exemplary embodiments of the present invention. In particular, various exemplary embodiments provide an architecture for the Command and Control (C 2 ) of an autonomous unmanned ship or surface vehicle with a minimum of human intervention. The Stratified Horizon Control herein provides an architecture for creating an algorithm that interprets the highest level of commander&#39;s orders in a linguistic format as might be given to a human operating an equivalent vehicle and autonomously interprets the commanders orders and develops the various levels of controls to ultimately steer and control the speed of the surface vehicle to achieve its commanded mission. This algorithm provides safe, reliable, and effective execution of the commander&#39;s intent without increased manning requirements. 
     Various exemplary embodiments provide an operating system for controlling an unmanned vehicle. The system includes a stratified plurality of instruction layers, a behavior axiom block and a set of operation parameters. The instruction layers are substantially arranged in descending priority order. Each layer provides an information signal to either an adjacent descending layer or an operation device on board the unmanned vehicle. 
     The behavior axiom block provides an independent protocol signal to a first instruction layer in said stratified plurality. The operation parameters provide an environmental condition that neighbors the unmanned vehicle to a second instruction layer. In various exemplary embodiments, the behavior axiom block includes prioritization adjustment to an instruction layer for overriding the information signal from an adjacently ascendant layer, such as by an interrupt signal. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       These and various other features and aspects of various exemplary embodiments will be readily understood with reference to the following detailed description taken in conjunction with the accompanying drawings, in which like or similar numbers are used throughout, and in which: 
         FIG. 1  is a block diagram view of a strategic horizon control system; 
         FIG. 2  is a plan view of an operation under a mission design layer; 
         FIG. 3  is a block diagram view of operational parameter reception; 
         FIG. 4  is a block diagram view of operational parameter integration; 
         FIG. 5  is a plan view of a navigation grid pattern; 
         FIG. 6  is a plan view of a horizon network in the  FIG. 2  operation; 
         FIG. 7  is a first plan view of an operational mission route; 
         FIG. 8  is a second view of the operational mission route; 
         FIG. 9  is a plan view of a first navigation guidance scenario; 
         FIG. 10  is a plan view of a second navigation guidance scenario; 
         FIG. 11  is a plan view of a third navigation guidance scenario; 
         FIG. 12  is a plan view of a fourth navigation guidance scenario; 
         FIG. 13  is a block diagram view of a route planning circuit; 
         FIG. 14  is a plan view of a composite navigation scenario; 
         FIG. 15  is a graphical view of optimizer selected time; 
         FIG. 16  is a tabular view of a complex object map; 
         FIG. 17  is a plan view of an object avoidance mission; 
         FIG. 18  is a tabular view of a selection matrix for obstacle avoidance; 
         FIG. 19  is a graphical view of down and cross-range distances for collision avoidance during a docking mission; 
         FIG. 20  is a block diagram view of a PID controller; and 
         FIG. 21  is a graphical view of a yaw rate response. 
     
    
    
     DETAILED DESCRIPTION 
     In the following detailed description of exemplary embodiments of the invention, reference is made to the accompanying drawings that form a part hereof, and in which is shown by way of illustration specific exemplary embodiments in which the invention may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the invention. Other embodiments may be utilized, and logical, mechanical, and other changes may be made without departing from the spirit or scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined only by the appended claims. 
       FIG. 1  shows an exploded view of a plan for Stratified Horizon Control (SHC), which represents an important aspect in various exemplary embodiments in controlling an unmanned vehicle. Behavior axioms are contained within a behavior axiom block (BAB)  110 , operating parameters  120 , preemptory prioritization adjustor  125  as adrenaline parameter with interrupt control. The BAB  110  includes safety protocols. The operating procedures  120  include group operating pictures (GOP) and individual operating pictures (IOP). 
     The plan  100  includes a series of levels for Stratified Horizon Control (SHC)  130 . The levels from strategic and long-term for decision to specific and immediate begin with commander&#39;s intent interpretation layer  140  that yields global quantitative goals and constraints  145 . Next, mission design  150  that yields horizons for lower controls, path constraints and horizon goals  155 , and environmental assessment  160  at the same level of detail as the mission design  150  but subject to information received from the operating procedures  120 . The assessment  160  yields navigation constraints  165 . 
     Next, route planning layer  170  that yields optimized navigation parameters  175 . Next, navigation layer  180  that yields heading rate or heading schedule commands  185  to achieve goal objectives and avoid collisions. Finally, control layer  190  that provides steering and throttle commands  195 . Time constraints for integrated decisions and command implementation range from interpretation  140  involving hundreds of minutes or several hours, mission design  150  involving tens of minutes, route planning  170  involving minutes, and navigation  180  involving tens of seconds, down to control commands  190  that involve milliseconds to seconds. 
       FIG. 2  shows a plan view of a mission implementation using the SHC  130  as an exemplary mission planning layer  200  that includes constraints from the mission design  150  and environment assessment  160 . A mission goal  210  can be identified by the human overseer and is disposed beyond a land mass  220  demarcated by a coast littoral  225 . Waypoints  230  together with their directional bearing arrows denote paths marked at horizons  240  that slide along the route planning layer  170 . Goals  155  from the mission design layer  150  include the horizons  240 . Obstacles denoted by individual points within the dash oval  250  along with a larger, more complex obstacle  260  represent entities to be avoided while navigating towards the goal  210 . The navigation constraints  165  include a global survey of objects and hazards from on-board and off-board sensors. The navigation parameters  175  include optimized guidance parameters. The heading commands  185  include acceleration commands to achieve goal and avoid collisions that control the steering and throttle commands  195 . 
     The unmanned surface vehicles must operate relative to a parent command vessel and with other peer surface vehicles.  FIG. 3  shows a block diagram  300  for implementing the SHC  130  relative to multi-vehicle operation within the operating procedures  120  for an unmanned surface vehicle (USV). Individual USV C 2  architecture  310  includes vehicle management, and sensors that communicate with the IOP, which interface with the SHC  130 . GOP  320  communicate with the parent or mother combat system (MCS)  330  that hosts the launch platform, and also associates with the SHC  130 . Off Board Assets  340 , Sensor Information  350  and Sensing Requests  360  supply information or instructions to the MCS  330 . Optimal Sensor Allocation  370  provides supplemental information to the architecture  310 . 
       FIG. 4  shows an expanded block diagram  400  for the operation of an individual vehicle using SHC  130 . The USV platform  410  employs the C 2  architecture  310 , including the IOP  420 . Together with the GOP  320 , the IOP  420  provides information to the SHC  130  for guidance to the platform  410 . A sensor suite  430  provides environmental and operational information to the platform  410 , such as radar, infrared camera, inertial measurement unit (IMU), global positioning system (GPS), etc. A vehicle management system  440  provides status on power, system health and communication. 
     Commands  450 , provided by the human operator and in conjunction with the SHC  130 , operate to provide group controls  455  to the vehicle management  440 . Other communications include sensing requests  460  from the IOP  420  to the suite  430 , returning with sensor measurements  465 , such as presence of local entities with tracks and classification, wave measurements, wind speed, etc. Information  470  exchanges between the operating procedures  120  and the vehicle management  440 . Information  475  also exchanges between the operating procedures  120  to the SHC  130 . Sensing requests  480  can issue from the vehicle management  440  to the suite  430 . The vehicle management  440  provides status updates  485  to the SHC  130 , which can pass requests  490  to the suite  430  and throttle/steering commands  495  to the platform  410 . 
     The Stratified Horizon Control (SHC)  130  represents an algorithm that divides the control processes from interpretation of the commander&#39;s intention through the actual commands to the steering and speed control into a set of instruction strata. The highest stratum involves the broadest decision making and interpretation and is called the commander&#39;s intent interpretation layer  140 . This layer deals with the more strategic aspects of the command-and-control mission 
     The interpretation of the commander&#39;s intent involves the definition of the mission, constraints, and acceptable levels of risk. The decision making at this level has the longest (time) horizon for consideration. Having to deal with entire missions, decision-making may extend to the hundreds of minutes. The lowest level of the SHC  130  provides the control outputs to the actuators on the USV platform  410 . Typically these are concerned with the desired rudder angle and the desired throttle control though alternate actuator arrangements may be accommodated in this approach. This lowest level of control deals with such inputs as heading or heading angle rate to produce the desired actuator command. The time horizon on this level is very short and deals with decisions affecting hundreds of milliseconds into the future. This architecture allows the implementation of existing off-the-shelf components such as the use of existing autopilots for the lowest level. 
     Between this uppermost layer and the lowest, the stratification of the control may assume many forms. Descending through these layers of the exemplary architecture illustrated in  FIG. 1 , the planning becomes more detailed and quantitative and with shorter time horizons. The entire system is designed for adaptability, and thus the horizons for each layer can be determined by the SHC  130  process. The level for setting the horizons can typically be performed in the mission design layer  150 . 
     The functions of each of these layers are discussed in detail in the following sections. These layers have set horizons that are updated at periodically at an interval much less than their above horizons (typically one-tenth of the immediately above horizon). At each update the solution can be completely redesigned with the current states as the initial conditions. Thus, the mission designed at the start of a mission may change considerably by the time of mission completion. Given sufficient environmental information and no surprises, the SHC  130  provides a stable mission definition but the redefinition can be established for adaptability upon receipt of new information. 
     Another important aspect of the SHC  130  is the Behavior Axioms Block (BAB)  110 , which exists outside of the stratified layer and is updated at a very high rate, typically on the order of the control layer  190 . That horizon can be relatively brief and confront issues where any of the BAB  110  may be violated. The behavior axioms deal with the survivability of the vessel and the safety of friendly and non-combatant ships and individuals. The BAB  110  conducts its own calculations over its horizon to determine whether any behavior axioms may be violated. 
     The BAB  110  has the ability to interrupt any layer currently in process by issuances of interrupt message. This interrupt message instills an adrenaline factor in each control factor which can affect its actions. This factor orients the control layer  190  away from mission success to satisfying the BAB  110 , which also dictates when the axioms are not in danger of violation and allow resumption of normal operations for the SHC  130 . At this point, the commander&#39;s intent interpretation layer  140  reinitializes the SHC  130  again from the current vehicle states and environmental conditions. 
     The commander&#39;s intent interpretation level  140  is designed to take linguistic based commands and translates these into mission design parameters. The interpretation of the Commander&#39;s Intent involves the definition of the mission, constraints, and acceptable levels of risk. The decision making at this level has the longest (time) horizon for consideration. Having to analyze entire missions, the interpretation level  140  may produce decisions out to the hundreds of minutes. The outputs of this level include the definition of the type of mission such as a patrol of specified region in use of particular sensor suite. Once the type of mission is specified then the mission parameters can be defined. 
     For example, for a patrol using a specific sensor suite, this level would provide the boundaries of the region, spacing and interlacing of vehicle sweeps, completion time for the patrol, decision node and reporting points, and the acceptable mission risk of the mission. This last parameter deals with the level of urgency associated with the mission and is important for regulating the adrenaline factor used in the lower levels. This factor is used to trade-off risk for probability of mission success in the mission design parameters and for the threshold for when the BAB  110  overrules the planned mission design  150 . An example of such a trade-off might be a very high priority mission that follows a path that hits waves at an approach angle that would be avoided in a routine mission. This level depends on the use of embedded knowledge of naval operations to interpret the linguistic commands from the commander. This level employs expert systems to allow access to the level of knowledge that an expert user of the particular boat and experienced sailor might possess of boat and navy operations. 
     The waypoints  230  between which the platform  410  operates can be described by nodal geometry.  FIG. 5  shows a plan view diagram of a navigation grid  500 . Nodes  510  are defined by grid position and approach direction and can be arranged in a rectangular pattern separated by a cell width  520  having length d in orthogonal directions. The platform  410  can be directed to follow a directed cycle  530  forming an octagonal ring of inward arcs. Outgoing arcs  540  correspond to approach direction ±45° directed away from the cycle  530 . 
       FIG. 6  shows expanded plan view  600  from  FIG. 2  of mission planning layer  150 . A sliding window “world view”  610  provides an observation region from which a moving platform  410  can operate from waypoints  230  along concatenated horizons  240  towards the objective  210 . Expansion  620  of an exemplary horizon  240  shows a detail horizon  630  with the corresponding waypoint  230  disposed at a horizon boundary  640 . To detour obstacles  650  and escape a hostile vessel  660 , the platform  410  travels along a navigation line  670  towards a horizon goal at bearing  680 . 
     The mission design layer  150  receives the output of the commander&#39;s intent interpretation layer  140  and converts these into quantified mission parameters. The outputs of this layer may typically include: contemporary (and estimates of future values) horizons (time values) for each of the layers and the desired position and heading  230  of the vessel platform  410  at the next horizon  240 . Planning in this layer occurs at length and time scales where the dynamics of the vessel are negligible. For the analysis at this level, the platform  410 , launch platform  330  may be considered to turn instantaneously and paths consist of straight line segments between nodes  510 . In this layer the nodes  510  are used to specify the path  530  or  540 . 
     The selection of nodes  510  includes the avoidance of known large obstacles  650  such as coasts or reefs. Smaller obstacles such as an isolated ship or rock are not considered at this level. The discrimination between which obstacles are included at this level of analysis is made based on maximum dimension of the object. For this determination, groups of closely spaced objects  250  can be combined into a single larger object through clustering algorithms  1700 . Consequently, the composite larger object or regions are avoided by mission design  150 . 
     The spacing of nodes that demarcate setpoints on the horizons that are applied to the lower levels and are determined by the type of mission interpreted from the commander&#39;s intent interpretation level  140 . As an example, if the commanding officer desires to leave port, transit the Atlantic, and then dock at another port, the nodes can be spaced close together in the port regions and relatively far apart in the open ocean areas. Correspondingly, the horizons  240  applied to the deeper layers would be smaller in the ports and larger in the open ocean areas.  FIG. 2  for the mission design layer  150  shows the development of horizons for the route planning layer  170  and the selection of node points by the mission design layer  150 . The extent of horizon of the route planning layer  170  represents the horizon for the mission design layer  150  which the latter self-selects based on the goal. 
     The mission design layer  150  develops the horizons for the lower levels and develops the “local world view” for the GOP  320  and IOP  420 . The interrelationship of these layers is shown in  FIG. 6  for interactions of the SHC  130 . The purpose of the route planning layer (RPL)  170  is to compute the nominal parameters used in a navigation algorithm, as in the navigation layer  180 . The selection of these parameters is based on optimizing a selected performance function over the horizon of the RPL  170 . This timeframe elapses typically on the order of tens of seconds. The specific horizon  630  of the RPL  170  in operation is selected by the mission design layer  150 . The RPL  170  represents a simulation based algorithm that uses a simulation of the vehicle to determine the navigation algorithm parameters. The navigation parameters  175  are based on minimizing the time to the desired position and heading at the current RPL horizon. The desired position and heading on the RPL horizon are provided by the mission design layer  150 . 
       FIG. 7  shows a plan view of a navigation grid  700  for an example reconnaissance mission as an exemplary embodiment for the mission planning design layer  150 . An initial waypoint  710  provides a start position for the platform  410 , which is assigned to reconnoiter a patrol area  720  containing obstacles  650 , while avoiding keep-out zones  730 . The platform  410  begins from the initial waypoint  710  along an ingress path  740  to the patrol area  720 . Upon arrival, the platform  410  proceeds to maintain station in a loiter cycle  750  within the patrol area  720  to survey an object  760  under observation. Upon completion of this task, the platform  410  returns to its initial waypoint  710  by an egress path  770  while again avoiding obstacles  650  and the keep-out zones  730 . 
       FIG. 8  shows an expanded plan view of the navigation grid  800 . The ingress and egress paths  740 ,  770  include demarcation horizons  810  that mark nodes  510  and course change positions. At the entry of the patrol area  720 , the path  740 ,  770  includes goal horizons  820  to indicate time-and-position goals. 
       FIG. 9  shows a plan view  900  for a first guidance level competency for the navigation layer  180  with guidance to commanded time, position and heading at the next horizon  240 . The platform  410  is disposed at an initial horizon boundary  910  corresponding to interval i and travels along a path  920  towards an adjacent horizon  930  at the next interval i+1, denoted by cross-hair oval, while avoiding obstacles  650 . An exemplary north-east-down-local (NEDL) frame  940  references global compass directions oriented on first-quadrant Cartesian directions north  950  and east  960 . Alternative frames using different directions and/or for other quadrants can be employed. 
       FIG. 10  shows a plan view  1000  for a second guidance level competency for the navigation layer  180  guidance to intercept a target with offset rendezvous. The platform  410  is disposed at an initial horizon boundary  1010  corresponding to interval i towards a target vessel  1020  to be intercepted. An adjacent horizon  1030  at the next interval i+1 corresponds to a position not directly along the intercept path  1040 . A predicted rendezvous position  1050  and direction  1060  can run parallel to the target vessel  1020  that travels along a target path  1070  and corresponding direction  1080 . 
       FIG. 11  shows plan view  1100  for third guidance level competency for blocking a craft from reaching an intended position. The platform  410  is disposed at an initial horizon boundary  1110  corresponding to interval i along an intercept path  1120  for arrival at an intercept position  1130  in a specified travel direction  1135 . At the intercept position  1130 , the platform  410  can detect and deter objects within a lethal range radius  1140 . An adjacent horizon  1150  at the next interval i+1 corresponds to a position not directly along the intercept path  1120 . The intercept position  1130  is disposed between a defended asset  1160  and the craft  1170  to be blocked. By traveling in a direction  1180  towards the defended asset  1160 , but reaches the radius  1140  at a range position  1190  for deterrence by the platform  410 . 
       FIG. 12  shows plan view  1200  for fourth guidance level competency for search and patrol. The platform  410  is disposed at an initial horizon boundary  1210  corresponding to interval i along an intercept path  1220 . An adjacent horizon  1230  at the next interval i+1 corresponds to a position not directly along the intercept path  1120 , which meanders around obstacles  650  and through regions  1240  for investigation. 
       FIG. 13  shows block diagram view of a circuit  1300  for the Route Planning Layer  170  for Guidance. An optimizer  1310  initiates along guide parameters path  1320 , such as gain constants K 1 , K 2 , K 3 , t c  and ψ to an on-board simulator  1330  and cycle along an iteration return path  1340 . The circuit exhibits directionality  1350  upon optimization to be submitted to the navigation layer  180 . The cycle  1300  optimizes parameters to maintain a minimum closest point of approach (CPA) relative to an obstacle in the local horizon, as well as to minimize time. The simulator  1330  integrates over the control horizon using an assumed guidance law with parameters, such as minimum CPA to any object during trajectory, time-to-horizon and final states at horizon. 
       FIG. 14  shows plan view  1400  of a guidance layer for composite navigation along a trajectory with an NEDL frame  940 . The platform  410  is disposed at an initial bearing position  1410  and travels along a path  1420  towards an end goal  1430 , while avoiding obstacles  650 . The path  1420  incorporates composite navigation for avoidance, homing and shaping using optimizer-selected weighting factors. From the initial bearing position  1410  travels in a straight initial bearing until a course correction horizon  1440  that initiates a direction change set at a selected time. 
     Control law solutions for terminal conditions in vector form include all dimensions such as throttle acceleration to be used for target intercept (i.e., offset rendezvous). The optimal control u can be expressed as: 
                     u   =         V   2     R     ⁡     [         K   1     ⁡     (       r   ^     -     v   ^       )       +       K   2     ⁡     (         v   ^     f     -     v   ^       )         ]         ,           (   1   )               
where V is missile speed, R is range-to-go to the next waypoint or target point, K 1  and K 2  are respective guidance gains, {circumflex over (r)} is the unit vector for the line-of-sight to the target point, {circumflex over (v)} is the unit vector along the current velocity and {circumflex over (v)} f  is the unit vector for the desired final velocity orientation. The commanded accelerations can be expressed without throttle control.
 
     Bearing guidance includes an initial bias phase, such as in a P frame, expressed as: 
                       a   _     bearing   P     =     {         0             a   •             0         }             (   2   )               
where the vector array includes the middle term such that:
 
 a*=[k (ψ*−ψ)],  (3)
 
where k is a user selected gain, ψ* and ψ are current bearing angles of the vehicle, and an obstacle avoidance component, also in the P frame, expressed as:
 
                         a   _     avoid   P     =     {         0             a   avoid             0         }       ,           (   4   )               
where
 
               a   _     avoid   P         
is a vector of acceleration commands necessary to avoid obstacles.
 
     An optimization or rule-based set algorithm can be used to determine the parameters that minimize the time to the goal point, maintains minimal separation (i.e., the CPA) around known obstacles, and satisfies any heading constraints along the path. Such constraints represent known heading angle constraints due to wave motion in certain regions. One goal includes avoiding the preplan of the route to improve safe maneuver.  FIG. 13  for route planning shows the concept for the RPL  170 . 
     The navigation layer  180  is responsible for generating commands for the heading, heading rate, or acceleration to the control layer  190 . The navigation layer  180  uses the navigation algorithm parameters  175  selected by the RPL  170  in computing these heading rates or heading schedule.  FIG. 14  shows the overview of the procedure for the navigation layer  180 , including an initial bearing phase  1410  from which the vehicle  410  turns to a bearing and holds until reaching position  1440  at a designated time t c  from the RPL  170 . 
     Composite navigation provides for obstacle avoidance and path shaping using the parameters from the RPL  170 . Depending on the implementation on the USV platform  410 , the navigation law can be cast as a commanded acceleration as shown: 
                       a   _     NEDL     =         T     P   ⁢           ⁢   1   ⁢   NEDL       ⁢       K   ~     1     ⁢     T     P   ⁢           ⁢   1   ⁢   NEDL     T     ⁢       a   _     goal   NEDL       +       T     P   ⁢           ⁢   2   ⁢   NEDL       ⁢       K   ~     2     ⁢       a   _     bearing   P       +                         ⁢     T     P   ⁢           ⁢   3   ⁢   NEDL       ⁢       K   ~     3     ⁢       a   _     avoid   P       ,                 (   4   )               
where T and T T  are matrices and their respective transforms.
 
     The commanded accelerations are developed in the reference frame defined by the NEDL frame  940 . The three terms provide three, different aspects of the guidance law are blended by a set of three gains contained in the three gain tensors: {tilde over (K)} 1 , {tilde over (K)} 2 , {tilde over (K)} 3 . These gain tensors are formed by: {tilde over (K)} i =K i [I] where [I] is the identity tensor and K i  is a blending weighting scalar for each component of the composite law. The weightings are set by the RPL  170 . For generality, the gain may be scheduled in time t such that:
 
 K   i =ƒ( t ),  (5)
 
where time-varying function ƒ is provides rate of gain change.
 
     The three elements of the composite guidance include: ā goal  which is a goal and orientation control algorithm designed to reach goal points on the local horizon, ā bearing  is the acceleration required to follow the bearing, and ā avoid  is the acceleration to avoid objects and environmental conditions, such as the need to approach large waves at a specified angle. The specifics of how these commanded accelerations are computed may be developed in many different substations. The latter two commands are naturally developed in the P frame which has its x axis aligned with the current velocity vector and its z axis aligned with the local gravity vector. T P2NEDL  is the transformation from the P frame to the NEDL frame. 
     Typically, the values for bearing guidance and the avoidance guidance would be terminated at some point in the local horizon. For instance, the bearing guidance computation can be set to zero in response to the specified time of the RPL  170  to follow the bearing has been exceeded. Similarly, the acceleration to avoid objects or environmental conditions would reach a zero value once the time-to-go to the obstacle or environmental conditions has gone negative. This implies that the object is behind the USV platform  410  in terms of the current direction of travel. 
     The guidance or navigation layer  180  generates commands for the heading, heading rate and acceleration to the control layer  190 . Guidance parameters are obtained from the latest route planning update, and measurements from the IMU and GPS are processed using guidance equations to compute acceleration or heading rate. The navigation law can be expressed as a heading rate {dot over (ψ)} for in-flight composite guidance: 
                             ψ   .     =       ⁢     V   ⁢            a   _     NEDL                        =       ⁢     V   ⁢              T     P   ⁢           ⁢   2   ⁢   NEDL       ⁢       K   ~     1     ⁢     T     P   ⁢           ⁢   2   ⁢   NEDL     T     ⁢       a   _     goal   NEDL       +               T     P   ⁢           ⁢   2   ⁢   NEDL       ⁢       K   ~     2     ⁢       a   _     bearing   P       +             ⁢                                 ⁢       T     P   ⁢           ⁢   2   ⁢   NEDL       ⁢       K   ~     3     ⁢       a   _     avoid   P            ,           ⁢                   (   6   )               
where V is current speed of the vessel. The first term represents terminal conditions control; the second term determines initial bearing phase and the third term provides obstacle avoidance. The gain tensor is represented by the form:
 
                         K   ~     n     =     [           K   n         0       0           0         K   n         0           0       0       0         ]       ,           (   7   )               
where n is integer 1, 2 or 3. The sign of the commanded heading rate can be computed from the vector relationship on the right hand side of eqn (7). This approach has the benefit of interfacing with existing autopilots for vessel operations.
 
       FIG. 15  shows a plan view of object cluster process  1500  using fuzzy logic to cluster closely spaced objects. This process can be used to augment obstacle avoidance. A first complex object  1510  includes track items A, B, C, D. A second complex object  1520  includes track items F, G, H, and J. A third complex object  1530  includes track item E. A fourth complex object  1540  travels along a moving vector  1545 . Obstacle tracks can be used as vertices for avoidance logic. 
       FIG. 16  shows tabular view of Complex Object Map  1600  to Obstacle Tracks. The first column  1610  identifies the complex object from the cluster process  1500 . The second column  1620  identifies the corresponding object tracks. The third column  1630  lists the corresponding vertex for avoidance. The fourth column  1640  concludes the closest point of approach (CPA) buffer for each object. 
       FIG. 17  shows a plan view of goal seeking object avoidance  1700 . During operations, the divert angle to each object (port L or starboard R) can be computed. The resulting miss metric (MM) or of all other objects can be computed for each maneuver. A platform  1710  travels in an initial direction  1715  with a divergence direction  1720  for avoidance acceleration. A stage goal  1730  is disposed at the horizon edge  1560  (representing terminal conditions) traveling in the direction  1565 . 
     Obstacles K, L and M lie disposed between the traveling platform  1710  and the goal  1730 . No avoidance is required for the K obstacle. However, the L obstacle can be circumvented by dash-line divert directions to port, and the M obstacle can be circumvented by dash-line divert direction to starboard. Upon passing the M obstacle through the divert maneuver, a goal angle  1740  directs the platform  1710  to the goal  1730 . A plot  1750  illustrates the relation between the abscissa  1760  as the closest point of approach and the ordinate  1770  as the miss metric. Diversion to expand approach distance around an obstacle is maintained to equal the miss metric reaches fifty-feet in this example. 
       FIG. 18  shows a tabular view of a selection matrix  1800  for goal-seeking object avoidance. The columns include lists of obstacle objects K, L and M, the divert direction (port and starboard), the miss metric to the respective obstacles, the average and the angle to target. The diversion from L to port is selected with the corresponding angle from target circled. 
       FIG. 19  shows graphical view of a plot  1900  for optimizer navigation substantiation. An exemplary docking mission involves a jet-ski craft (starting at the origin) to a pier with a single waypoint and an obstacle blocking a straight path. The abscissa  1910  represents downrange distance from the origin and the ordinate  1920  represents cross-range distance. An obstacle  1930  is disposed between the origin and the pier  1940  that represents the goal. A known-obstacle path  1960  enables avoidance of the obstacle  1930  assuming initial availability of this information to ensure an imposed 50-foot clearance. A pop-up path  1970  provides an alternate route limited to ⅓g (one-third gravitational acceleration) maneuverability limit assuming a delay in obstacle detection. 
       FIG. 20  shows a block diagram  2000  of an adaptive proportional-integral-differential (PID) controller for boat lateral command. Desired dynamics  2010  are provided to a predicted response algorithm  2015  to generate a response fed to a first sum operator  2020  for receipt to a rule-set  2025  for adjusting gains for receipt into a controller  2030 . A signaler  2035  provides inputs, such as yaw, yaw rate, heading angle, speed and environmental conditions at the previous instrumentation sample to the algorithm  2015  through a first gate  2040 . A periodic impulse rudder input  2045  is provided to the algorithm  2015  through a second gate  2050 . 
     The controller  2030  provides an exemplary implementation of the control layer  190  that includes proportional (P), integral (I) and differential (D) functions, receiving through a second sum operator  2055  error correction from feedback (negative) from the output and commanded heading rate (positive) from the signaler  2035  as the setpoint. Outputs from the functions are summed in a third sum operator  2060  as boat true-dynamics output  2080 , such as rudder command in degrees. The summation output represents achieved yaw, yaw rate and heading rate for the boat. This output  2080  is returned as feedback through a third gate  2090  to the first sum operator  2020 , as well as to the second sum operator  2055 . 
       FIG. 21  shows a graphical view of a response plot  2100  for an adaptive PID controller used in boat lateral command. The abscissa  2110  represents time and the ordinate  2120  represents yaw rate. A first damped sinusoidal curve  2140  represents an initial unadjusted response to a kick with high peak and rapid attenuation as shown by lower envelope decay  2145 . A second damped sinusoidal curve  2150  represents an adjusted simulation response with upper envelope decay  2155 . The second curve  2150  corresponds to a time to first zero-axis crossing  2160  occurring at about 3.6 seconds and a desired peak response  2170 . PID gains K p , K i  and K d  (for proportional, integral and differential, respectively) can be adjusted to achieve these conditions. 
     Various exemplary embodiments provide the USV platform  410  with the ability to autonomously conduct a broad spectrum of missions equivalent to those which a commander might expect of a human operated surface vehicle. The ability to do this autonomously provides the commander with an expanded combat and operations capabilities without the attendant growth in manpower on the ship. The first generation of USV requires an additional complement of five-to-ten people to conduct operations of the platform  410 . 
     Various exemplary embodiments decrease manning requirements and augment combat and operational capability for the command platform (mother ship)  330  operating a USV platform  410 , increased continuous functionality because human intervention becomes optional such that and the systems do not degrade by fatigue from human operation, and increased functionality because the automatic system are able to perform missions that humans cannot accomplish. 
     Various exemplary embodiments provide the use of layers of automation with each layer dealing with the total problem at different levels of abstraction and time horizon. This process enables the mimicry of the human capability for strategic decision making as compared to the tactical decision making. By parsing out lower-level considerations for the higher strategic levels, the amount of computational time is greatly reduced. This approach thereby reduces complexity of the software. The minimum number of logic paths operating at each layer reduces testing time necessary to identify and correct the logic errors for ensuring safe and efficient operation, as compared to branch-and-tree approaches. 
     Conventional alternatives of exemplary embodiments include high personal attention for continued use of manned remote control operations of the USV platform  410 . This has the attendant problems of increased manning requirements on the command platform  330 , lower performance caused by operator fatigue, and lower total performance from communication lags due to fleet bandwidth limitations and human limitations. An alternate approach to automation is to use the branch-tree method as seen in applications such as chess programs. This approach is very computational intensive despite the game-playing characteristics, thus necessitating only a very limited need for situational awareness as compared to real world operations. 
     While certain features of the embodiments of the invention have been illustrated as described herein, many modifications, substitutions, changes and equivalents will now occur to those skilled in the art. It is, therefore, to be understood that the appended claims are intended to cover all such modifications and changes as fall within the true spirit of the embodiments.