Abstract:
A system and method can provide a command and control paradigm for integrating robotic assets into human teams. By integrating sensor to detect human interaction, movement, physiology, and location, a net-centric system can permit command of a robotic platform without an OCU. By eliminating the OCU and maintaining the advantages of a robotic platform, a robot can be used in the place of a human without fatigue, being immune to physiological effects, capable of non-humanoid tactics, a longer potential of hours per day on-station, capable of rapid and structured information transfer, has a personality-free response, can operate in contaminated areas, and is line-replaceable with identical responses. A system for controlling a robotic platform can comprise at least one perceiver for collecting information from a human or the environment; a reasoner for processing the information from the at least one perceiver and providing a directive; and at least one behavior for executing the directive of the reasoner.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
       [0001]    This application is a continuation of U.S. patent application Ser. No. 11/646,451, filed Dec. 28, 2006, entitled “METHODS AND SYSTEMS FOR AN AUTONOMOUS ROBOTIC PLATFORM,” which is incorporated herein by reference in its entirety. 
     
    
     BACKGROUND OF THE INVENTION 
       [0002]    1. Field of the Invention 
         [0003]    The field of the invention relates to the command and control of robotic platforms. 
         [0004]    2. Description of the Related Art 
         [0005]    Conventional approaches to command and control (“C2”) of mobile robotic platforms, including unmanned ground, sea, or air vehicles, typically require constant human interaction or intervention. Generally, the current state of robotic C2 relies on either remote control, teleoperation, or map-based semi-autonomy. 
         [0006]    Remote control is conventionally implemented by having a remote operator directly control the robot. Typically, any and all actions executed by the robot are directly controlled by the operator, who is assumed to be in line-of-sight to the robot. The operator watches the robot and controls it through an operator control unit (“OCU”). The OCU is a remote device that can be tethered to the remote platform, but typically is not. The OCU typically has a joystick or other steering controller to control the movement and/or operation of the remote platform. The human operator must visually follow the unmanned vehicle to determine the next course of action and command the unmanned vehicle through the OCU to conduct that course of action. This operation is similar to operation of a remote-control toy car, where operation can be subject to visibility and distance limitations. 
         [0007]    In conventional teleoperation of a robotic platform, the OCU typically includes a video display and joystick for a human operator to control the robotic platform. Teleoperation is similar to remote control, but the line-of-sight restriction can be removed by utilizing sensors such as cameras (e.g., a camera on the vehicle viewed through a video display in the OCU) that give the operator a sense of the robot&#39;s environment and actions. An operator watches sensor output from the robot and controls the robot&#39;s actions with a joystick. In one example, the OCU can have a video display to monitor the actions of the robotic platform and/or the environment. The human operator uses the joystick on the OCU to operate the robot, making observations through the video display. 
         [0008]    In conventional semi-autonomous control of a robotic platform, the robot follows a sequence of GPS waypoints using sensors on board the robotic platform to detect and avoid any obstacles it may encounter. Using a conventional map-based OCU, robots are controlled by entering sequences of waypoints and tasks through the OCU. The robot then moves through the waypoints, carries out the tasks autonomously, and requires retasking upon completion or upon encountering circumstances that prohibit completion. For example, a human operator, based on location and limited information regarding the surroundings, designates waypoints on a map or overhead imagery, thereby commanding the robot to travel from a first coordinate to a second coordinate and so on to successive waypoints. The robot can be commanded to perform designated tasks at each waypoint, or along each path between waypoints. 
       SUMMARY OF THE INVENTION 
     Summary of the Problem 
       [0009]    There are a number of problems associated with conventional operator control of robotic platforms. Remote control of a robot is typically low-cost, but can only be operated in line-of-sight, and full-time operator attention is required. Additionally, although entities can be tracked with sensors and viewed by a human operator, this conventional method fails when an entity goes around a corner and cannot be tracked. Similarly, teleoperation of a robotic platform is also low-cost and full-time operator attention is also required, although teleoperation is not limited to line-of-sight control. The conventional, semi-autonomous map-based system is slow, requires training, is difficult to use to re-plan, and requires a sophisticated OCU, which is often heavy and cumbersome. Additionally, if there is an unforeseen event or circumstance, such as an obstacle or other situation that cannot be handled by its on-board programming, the robotic platform may require human intervention. These conventional systems can require significant and overt human direction of robot actions. As a result, these methods can break down when human operators are stressed or otherwise distracted. 
         [0010]    To make robots effective in supporting human teams, the human operator must not only visualize the location of the unmanned vehicle, but also understand the surrounding circumstances or environment. For example, referring to  FIGS. 1   a  and  1   b , two aerial views of squads in urban situations are illustrated showing soldier locations and the location of an unmanned vehicle. As shown in this example, it can be difficult for a remote operator to determine if the squad is in danger and the location of the threat. If the operator of the unmanned vehicle can only observe the squad via the aerial view of locations illustrated in  FIG. 1   b , the remote operator may not be able to discern whether the squad is taking a break or taking cover from enemy fire. Without understanding the situation, the remote operator may be unable to command the robot appropriately. 
         [0011]    If the operator is local, the stress of the situation can make it difficult for the operator to command the robot. For example, as shown in  FIG. 1   a , a squad of soldiers  100  can be moving down a street in an urban area with an unmanned vehicle carrying spare ammunition and supplies. In order to control the unmanned vehicle, a human operator with the squad or a remote operator through limited visibility must explicitly task the vehicle  120 . In the instance a sniper takes a shot at the squad or an IED explodes near the squad, the soldiers  100  may take cover behind a building or structure  110 , as shown in  FIG. 1   b . The unmanned vehicle  120  does not react appropriately and follow the soldiers because the human operator, who is concerned with his or her own life, takes cover rather than using an OCU to command the unmanned vehicle  120  to follow. As a result, the unmanned vehicle  120  may continue to follow its original route and traverse the street away from the squad. 
         [0012]    The use of unmanned vehicles or other robotic platforms in military operations can extend a team&#39;s area of influence, broaden its situational awareness and understanding, and increase its lethality and survivability, while reducing the physical and cognitive burden on individual team members. However, current unmanned vehicles can require near-constant human supervision and are difficult to retask when events change. As a result, unmanned vehicles are typically been relegated to operations that can be done slowly and deliberately, such as explosive ordinance disposal. 
         [0013]    Adding a second unmanned vehicle to a team can require additional equipment, and require a second team member to operate the OCU for that unmanned vehicle. As a result, the team can have one less soldier, rescue worker, or other type of team member for accomplishing a goal. 
         [0014]    Controlling an unmanned vehicle through an OCU can be cognitively demanding. In fact, many potential military applications for robots are considered unworkable because of the OCU requirement. As a result, unmanned vehicles may be excluded from high-intensity situations, including those in which the unmanned vehicles can be the most useful to the team. 
       Summary of the Solution 
       [0015]    One solution to these problems can be to enable asymmetric cognitive teams (“ACT”). For example, an ACT can be created by augmenting a mobile robot&#39;s sensors with instrumentation of other members of the team, and using this information in a cognitive model to enable the robot to understand the immediate situation and select appropriate behaviors. A robot so equipped would be able to “do the right thing” automatically, thereby eliminating the need for cumbersome OCUs; the robot literally acts like a member of the team, automatically adapting its actions to complement those of the other team members. The solution can reduce the cognitive burden on an operator by providing natural (i.e., human-like) interaction. In the example shown in  FIGS. 1   a  and  1   b , if the squad of soldiers move to a wall, an ACT-enabled robot can utilize the information about the change in formation along with data such as heart rate, blood pressure, and weapon status to determine whether the soldiers are in a combat situation or are taking a break. The robot can then automatically take the appropriate action, such as providing cover in the case of a combat situation or offering resupply if the team is taking a break. The system can enable robotic entities or unmanned vehicles to operate as effective team members without the need for constant human direction. As a result, each human team member can act according to his/her training, rather than requiring a team member to use an OCU to control a robot. 
         [0016]    The exemplary embodiments described herein can provide a command and control paradigm for integrating robotic assets into human teams. By integrating sensors to detect human movement, physiology, and location, and incorporating a cognitive model of situations and behaviors, an ACT can permit command of a robotic platform without an OCU. By eliminating the OCU and maintaining the advantages of a robotic platform, a robot can be used in the place of a human without fatigue, being immune to physiological effects, capable of non-humanoid tactics, a potential of about 24 hours per day on-station, capable of rapid and structured information transfer, has a personality-free response, can operate in contaminated areas, and is line-replaceable with identical responses. 
         [0017]    In one embodiment, a system for controlling a robotic platform comprises at least one instrumented external entity, at least one sensor on each team member, one perceiver for collecting information from the at least one instrumented external entity; a reasoner for processing the information from the at least one perceiver and providing a directive; and at least one behavior for executing the directive of the reasoner. 
         [0018]    In another embodiment, a method for controlling a robotic platform comprises the steps of developing tactical behaviors; determining a mission, situation, disposition, and/or human cognitive or emotional state; driving a cognitive model; inferring current state, goals, and intentions; and selecting an appropriate behavior. 
         [0019]    In yet another embodiment, a system for controlling a robotic platform comprises a team sensor system; a software system comprising a perception component for providing information from the sensors; a cognition component for estimate an intent from that information; a playbook action generator component for determining a course of action; a playbook executor component for executing the course of action complementary to the estimated intent; and an unmanned vehicle interface. 
         [0020]    In still yet another embodiment, a system for controlling a robotic platform comprises at least one sensor that detects a status; a software component that receives the status from the sensor; and the software component comprising a cognitive model; wherein the cognitive model directs the robot to perform an action. 
         [0021]    Additional features and advantages of the invention will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings. 
         [0022]    It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory and are intended to provide further explanation of the invention as claimed. 
     
    
     
       BRIEF DESCRIPTION OF THE FIGURES 
         [0023]    The present invention will be more clearly understood from a reading of the following description in conjunction with the accompanying exemplary figures wherein: 
           [0024]      FIGS. 1   a  and  1   b  show an aerial view of the location of the members in a squad; 
           [0025]      FIG. 2  shows states of a human and a robot according to an embodiment of the present invention; 
           [0026]      FIG. 3  shows a communication network between a team and a robot according to an embodiment of the present invention; 
           [0027]      FIG. 4  shows a system architecture according to an embodiment of the present invention; 
           [0028]      FIG. 5  shows a system architecture according to an embodiment of the present invention; 
           [0029]      FIG. 6  shows a system architecture according to an embodiment of the present invention; 
           [0030]      FIG. 7  shows components of a cooperative robotic weapon control system according to an embodiment of the present invention; 
           [0031]      FIG. 8  shows a system architecture according to an embodiment of the present invention; 
           [0032]      FIG. 9  shows an alternative system architecture according to an embodiment of the present invention; 
           [0033]      FIG. 10  shows a method of autonomous control according to an embodiment of the present invention; 
           [0034]      FIGS. 11   a  and  11   b  show a playbook and plays for a course of action in the playbook according to an embodiment of the present invention; 
           [0035]      FIG. 12  shows a system of robotic learning and training according to an embodiment of the present invention; 
           [0036]      FIGS. 13   a  to  13   h  show a team&#39;s ingress on an enemy force according to an embodiment of the present invention; 
           [0037]      FIGS. 14   a  to  14   g  show a team&#39;s ingress on a location according to an embodiment of the present invention; and 
           [0038]      FIGS. 15   a  to  15   f  show a team&#39;s positioning when a soldier is wounded according to an embodiment of the present invention. 
       
    
    
     DETAILED DESCRIPTION 
       [0039]    Reference will now be made in detail to the preferred embodiments of the present invention, examples of which are illustrated in the accompanying drawings. 
         [0040]    The systems and methods described herein can enable robotic platforms to perform appropriate behavior without overt human control. Control of robot platforms occurs without bulky and expensive OCUs. Robots can learn tactics and appropriate behavior from humans. Robots can continue to operate when all humans are distracted or cognitively overloaded. Robots can learn from training (much like humans do), and robots can fully interact with and support human teams. As compared to conventional systems that typically control an unmanned or robotic vehicle through an OCU, the systems and methods described herein can observe a human and/or team behavior and decide appropriate actions without direct tasking from humans. By understanding an individual&#39;s or a team&#39;s reaction to a situation, rather than understanding the entire environment and situation, the system can command the robot accordingly. Information can be provided about the human or the team by using sensors. A cognitive model on the unmanned vehicles can process the information, determine the appropriate action based on models of human activities, and then execute the action and monitor the result. In one exemplary action, the unmanned vehicle can move with the team as the team moves. If the team halts, the unmanned vehicles know to halt. If the team assumes a tactical formation, the unmanned vehicles know to move to and maintain an appropriate position in the formation. 
         [0041]    The architecture of the system uses hardware and software, as well as information flow to produce an approach to robotic command and control. The systems and methods can utilize net-centric information flow and an embedded cognitive model to build a working model of the current situation and a human&#39;s or team&#39;s intent. The humans and robotic platforms can interact through a communications network. Net-centric resources include sensors or instrumentation of humans and teams. The robotic platform then generates a short-term course of action for the robotic platform to pursue, without overt human control or direction by OCUs. Eliminating an OCU and/or direct control (e.g., RF control) can enable robots to be useful without physically or cognitively burdening human team members. This approach can also reduce training required for robotic platform control and can enable learning of tactics by the robotic platform. As a result, the robotic platforms can become useful members of human teams, while reducing the attention and effort on the part of the humans to direct the robotic platforms. One potential advantage can be to enable robotic platforms to be useful when humans are under stress or otherwise distracted. 
         [0042]    Referring to  FIG. 2 , coordination between a human and a robot can assist in replacing the burden of explicit control by a human. Coordination can be possible through mutual monitoring and mutual understanding. The system builds a cognitive state model of the human and uses it to determine an appropriate course of action. The human transitions between cognitive states, which can include, but are not limited to, at rest  200 , relaxed  210 , excited  220 , and frightened  230 . The system can make inferences based on known or detectable information, such as the mission, situational awareness and understanding, squad dynamics, posture, physiology, and logistics state. These inferences trigger a transition in the robot&#39;s behavioral state, which can include, but are not limited to, stopped  240 , unguarded motion  250 , protect  260 , hide  270 , and guarded motion  280 . 
         [0043]    The systems and methods described herein can integrate and control multiple unmanned platforms (i.e., mobile robots or vehicles) into human teams. The unmanned platforms can be interchangeably referred to herein as a robot, robotic platform, or unmanned vehicle. The systems and methods can be applied to any ACT-enabled platform including but not limited to ground, air, water surface, and underwater robotic platforms, which are also referred to herein as unmanned vehicles, including unmanned ground vehicles, unmanned aerial vehicles, unmanned surface vehicles, unmanned subsurface vehicles, or any other type of unmanned vehicle known in the art. The unmanned vehicle is not intended to be limited to only those vehicles that cannot be manned and includes all ground, air, sea, and undersea vehicles that can operate in an unmanned mode. Additionally, the unmanned mode of the unmanned vehicle is not intended to be limited to only when a human is not present on, in, or near the vehicle. 
         [0044]    The systems and methods can be domain-independent. For example, the robotic platform can be a supply carrier for a dismounted warfighter. In another example, the robotic platform can be a tank ammunition carrier that can move into position to provide ammunition. In yet another example, the robotic platform can be a surveillance air vehicle drone that stays in position over the battlefield to give situational awareness. The platform can also be a sensor platform and/or a weapons and/or a logistics platform. 
         [0045]    Although a soldier or a team member is described herein as a human, a soldier or team member can also include non-human members such as trained animals, robotic platforms, or other vehicles and equipment. In certain configurations, the human being is an individual soldier. In other configurations, the human is a member of a team. The soldier or team member is not intended to be limited to only those configurations where a human acts with other human beings. Additionally, the soldier or team member is not intended to be limited to a human conducting a military operation. 
         [0046]    A team or squad is not intended to be limited to a plurality of humans conducting a military operation. A team can include at least two team members, equipment, vehicles, or other entities that function together for a common purpose. 
         [0047]    The systems and methods can be used in any applications including, but not limited to, where tightly integrated, real-time cooperation between humans and robots can be required, such as warfare (e.g., support for dismounted infantry or mounted operations), control of non-combatants (e.g., crowd control, peacekeeping patrols), and high-intensity, time-critical operations (e.g., search and rescue after man-made or natural disasters). Additional opportunities for the systems and methods can include tactical military applications, such as a robotic point man, robotic logistics platform, force protection sentries, and similar applications. Other applications can include any application in which a robot can complement or augment human capabilities, such as a construction assistant. Additional opportunities can include site security and patrolling, human/robot First Responder teams, and any other situation where humans can be augmented by mobile robots. Additional military applications can include vehicles used in ground and sea convoys and swarming type attack vehicles (e.g., small boats, ATVs, and Navy Seal activity). Non-military applications can include ground and air vehicles for border patrols and harbor/facility/critical infrastructure patrol vehicles. 
         [0048]    As shown in  FIG. 3 , a team of soldiers  300  in an environment  310  can communicate with each other and a robot  330  through a wireless network  320 . Such communications can include position, pose, motion, physiological parameters, weapon status, and orientation command and directives. Additionally, robot sensors  360  can detect the environment  310  and the team  300  and report to the robot  330  with information including but not limited to a terrain map, target identification and tracking, range-finding, and designator detection. The robot  330  can control the robot sensors  360  and the robot weapons  340 . The status, behavior, and alerts of the robot  330  can be communicated to the team  300 . The robot  330  can compute formation, tactics, techniques, and procedures, as well as soldier or team cognitive state and goals. 
         [0049]    Improving human-robot coordination can be useful in replacing the burden of explicit control, such as through an OCU. Coordination can be possible through mutual monitoring and mutual understanding. The systems and methods herein can build a cognitive state model of the team member or team and can use it to determine the appropriate course of action. The system can use team location, physiology, and weapons status to estimate individual or team cognitive state and goals. That estimate can then be used with triggering events to transition the robotic state model. The robot then can execute tactically appropriate behaviors in response to the state of the individual or team. For example, the robot halts when the individual or team is relaxed. As another example, when the individual or team is frightened, the robot can maneuver between the team and an inferred threat. 
         [0050]    As compared to conventional systems, the systems and methods described herein can focus on situationally appropriate behaviors, rather than rote execution of predefined plans. For example, the system can use information from the humans in the form of location, looking and pointing directions, physiological data, and other information sources that may be available (e.g., machine readable high-level plans and situational information). This information is fed to a cognitive model that develops a model of each human in the team as well as the team as a whole in order to estimate the actions and intent of the humans. Once the robotic platform can estimate the intent, the system can then use that estimate of intent to define its own course of action. For instance, if a team of humans is moving through a town in a line formation, the robot will automatically get in line (either at the end or filling a gap) and move with the team—starting, stopping and changing speed with the team. 
         [0051]    Generally, the systems and methods can operate as follows. Referring to  FIG. 4 , a cognitive model  420  has at least one perceiver  425 ,  430 ,  435 ,  440  and a reasoner  445 . The perceivers can include, but are not limited to, a soldier perceiver  425 , weapon perceiver  430 , team perceiver  435 , and sensor perceiver  440 . The perceivers  425 ,  430 ,  435 ,  440  can be a software and/or a hardware component configured to produce tactical information with confidence metrics that drives the cognitive model  420  to estimate the current tactical state and condition of the team. For example, each perceiver  425 ,  430 ,  435 ,  440  can answer tactical questions about the current situation, such as: are the team members excited, are their weapons safe, are the majority of weapons pointed in the same direction, is anyone in a tactical pose? Information from soldiers  400 ,  405 ,  410 ,  415  can be communicated to the soldier perceiver  425 , the weapons perceiver  430 , and the team perceiver  435 . The perceivers can characterize the team member&#39;s state, such as location, posture, physiology, weapon status having the safety on/off, weapon pointing, and trigger pulls. The soldier perceiver  425  queries, for example, whether the soldiers are on alert or whether the soldiers are running or hiding. The weapon perceiver  430  queries, for example, whether the safety is in an “off” position, the weapon is low on ammunition, or if the weapon is pointing at a threat. The team perceiver  435  queries, for example, whether the team is in a formation and which formation, whether the formation is spreading or shrinking, whether the formation is changing, or where should the robot be in the formation. A sensor on a human or a robotic platform can be included in the perceiver or, alternatively, the sensor can be a separate component that provides information to the perceiver. The perceivers combine messages and sensor information from the sensors through the wireless communication system to the reasoner  445  to answer tactical questions. 
         [0052]    The reasoner  445  can consider trigger events, confidences, and soldier/squad state to determine appropriate action. The reasoner  445  can be a software and/or a hardware component configured to query the perceivers  400 ,  405 ,  410 ,  415  and uses the information to determine the situation. For example, the reasoner  445  can query the perceivers  400 ,  405 ,  410 ,  415  based on trigger events and state matching, including, for example, increase in heart rate and blood pressure, safeties in an “off” position, trigger pulls, or postures changing to cover postures. Alternatively, the reasoner  445  can respond to a verbal override. Based on the results, the reasoner  445  constructs a tactical picture. 
         [0053]    A plurality of behaviors can be used to execute the reasoner&#39;s  445  directives to perform tactical actions. Behaviors include, but are not limited to, rally  450  (e.g., move to a coordinate), patrol  455  (e.g., move in a pattern to follow a soldier or other robot), halt  460  (e.g., hold position), and weapon  465  (e.g., aiming and firing of a weapon). Examples of rally behaviors include the robot rally on a soldier, rally to a named waypoint, or rally to a designated position. Examples of patrol behaviors include point, follow soldier, formation move, patrol, and guard. Examples of weapon behaviors include cover fire, suppression fire, sector-free fire, and IFF protection. IFF is identification friend or foe and is a procedure to identify friendly entities. Behaviors are communicated to a reactive obstacle avoidance system  470 , which identifies any obstacles and commands the unmanned vehicle to go around them, and a weapon control  465 , which communicates with a vehicle control unit  475 . 
         [0054]    Generally, the system architecture can use on-board sensors to validate and localize information received from the team and to capture information that is not available net-centrically. Periodically or in real-time, the system can be provided with each team member&#39;s location, weapon state, and physiological state. In some configurations, the system can utilize the team member&#39;s reaction to events, which can be easier to understand than the events themselves. The cognition component fuses all incoming information into a tactical picture and develops an estimate of squad intent. The cognitive model consumes the perception estimate of higher-order team member behavior, which are observable states that indicate the internal state of the team member. This tactical state estimate enables the generation of an action in view of the current intent and short-term goals of the team members. 
         [0055]    With regards to the system architecture, referring to  FIGS. 5 and 6 , a system has C2 software  500 ,  600  a team sensor system (“TSS”)  505 ,  605  and an unmanned vehicle interface or integration kit  510 ,  610 . The system uses a cognitive framework, fuses perception and net-centric information from the TSS  505 ,  605  and other sources into a cohesive estimate of squad intent, generates a short-term robotic plan, then uses tactical behaviors to execute the plan. 
         [0056]    The C2 software  500 ,  600  provides functionality for perception, cognition, playbook action selection, executing and monitoring, and an interface to the unmanned platform. The C2 software  500  can support at least one unmanned vehicle that can, for example, maneuver with a team, execute behavioral roles, carry supplies, or resupply team members. The C2 software  500  can also enable unmanned vehicles to support coordinated team tactical maneuvering, behavioral roles for sensor coverage, and protection for weapon coverage. The C2 software  500  can provide network information management and dissemination functionality to enable efficient communications between the TSS  505  and the unmanned vehicles. 
         [0057]    The C2 software  500 ,  600  includes perception software  515 ,  615 , cognition software  520 ,  620 , playbook action-generation software  525 ,  625 , playbook action (or plan) execution  530 ,  630 , and playbook action feedback  535 ,  636 . Perception software  515 ,  615  can provide functionality to sense and analyze the environment for navigational and tactical purposes, such as obstacle detection, local terrain mapping, and tactical perception and symbolization (i.e., representation of the perceived entities in terms useful to the cognitive processing and action generation functions). The perception software  515 ,  615  uses net-centric information, as well as information from sensors. Perceivers  611 , such as those described above with respect to  FIG. 4 , can operate with a tactical module  612 , which can navigate  613  by using obstacle detection  614  and a terrain map  616 . 
         [0058]    Cognitive model software  520 ,  620  can provide the functionality to identify the current tactical state of team members, the team&#39;s tactical state as a whole, and the ability to share information between multiple cognitive models. The cognitive model  520 ,  620  can interact with other parts of the system to direct sensing and perceiving resources to help disambiguate the existing tactical situation. The cognitive model  520 ,  620  learns at different levels based on feedback from team members and chunking, or other short-term memory, to support human-based cues that identify when the system should learn a new situation. Cognitive software  520 ,  620  can identify the state/role of a team member  621  or a team  622  using semantic pattern recognition  623 , which obtains information from the perceptual synthesis  617  of the perceivers  611 . Semantic pattern recognition  623  can use patterns in memory to recognize an environment and can look for further patterns or clues to further distinguish the type of environment. 
         [0059]    Robotic playbook action-generation software  525 ,  625  can provide the functionality to generate robotic actions based on the perceived state of the team. The playbook action-generator  525 ,  625  can use the output of cognitive models to process tactical state information, draw upon stored databases of tactics, techniques, and procedures (“TTP”)  624 , training materials  626 , and act to identify appropriate tactical actions for unmanned vehicles. Along with training  626  and TTPs  624 , the playbook generator  627  can use current plans  628 , joint plans  629 , and information from the navigation module  613  of the perception software  515 ,  615 . This information, along with the tactical state discerned from the semantic pattern recognition  623 , can be provided to a play evaluator  632 . The system uses spatiotemporal reasoning  631  to understand a situation (e.g., a formation) in a time and space analysis and tries to figure out what the team members are doing and why. Spatiotemporal reasoning  631  submits tactical queries to the perceivers  611 . 
         [0060]    Playbook action execution software  530 ,  630  and feedback software  535 ,  635  can provide the functionality to control and sequence the execution of short-term robotic actions, monitor their execution, identify action failures, and identify when squads have abandoned, replaced, or modified behaviors. The play evaluator communicates with a plan or playbook action executor  633 , which also receives information from a play module  634 , to communicate with both a play monitor  636  and a plurality of behaviors  637  in the tactical behaviors module  638 . The feedback manager  535 ,  635  evaluates plan feedback in a relevance monitor  639  and communication monitor  641 . 
         [0061]    The C2 software  500 ,  600  uses TSS data and other information to estimate the squad intent and generate a plan. The TSS  505  can include non-intrusive sensor devices that provide information to the C2 software  500  and allows for feedback to the user. A TSS Feedback Manager  540  can manage a team member state  545 , weapon state  550 , state information and verbal command interface  555 , and alert devices  560 . The TSS feedback manager  540  can control the output (i.e., can decide to send information) from the sensors to the network when there is a change in a sensor&#39;s status. The TSS feedback manager  540  can monitor the status of the sensors and send information when there is a change that it deems to be significant. 
         [0062]    The team member can use verbal commands to inform the playbook action generator of the team member&#39;s state. The TSS  505  can include components and devices for team members to wear or carry that can provide sensor information to the system, as well as provide feedback from the system to the team member. The information can indicate team member location, physiological state, and weapon status information. The components can include, but are not limited to, COTS products such as sensors, worn or carried by the team members, that can provide weapon status information, location (e.g., via global positioning system (“GPS”)), a verbal command interface, and/or information on each team member&#39;s physiological state. GPS is global positioning satellite, a satellite navigation system that allows accurate determination of a location. Any discussion of GPS is not intended to be limited only to the global positioning satellite, but can include any position locating or tracking system, including, for example, global navigation satellite system (“GLONASS”) and Galileo. 
         [0063]    There can be numerous embodiments for the TSS components. For example, the TSS  505  can be a modification and/or addition to a rifleman&#39;s suite, which disseminates information over a wireless network. Haptics can serve as soundless, non-intrusive alert devices. A GPS chipset integrated into a microcontroller box and interfacing with a team member&#39;s personal role radio provides digital communications and team member location reporting. A small box with an inertial sensor (e.g., an orienting device) mounted on the weapon can provide weapon pointing information. The soldier&#39;s weapon can also be instrumented with safety, trigger, and auxiliary switches, as well as a laser rangefinder or designator. A chest-strap or instrumented t-shirt can provide physiological responses to a tactical situation, fatigue estimation, and estimates of cognitive load. A laser pointer (e.g., a IZLID 1000P laser pointer) mounted on the weapon can be used as a pointing device to select an object of interest (e.g., a possible IED). A non-intrusive “watch fob” display device can display status and imagery. It can be carried on the team member&#39;s belt and glanced at for situational awareness and understanding. A minor change to a team member&#39;s weapon can provide trigger-pull and safety status to the system. Solid state accelerometers at joints (e.g., back, thigh, ankle, knee, or hip) of the team members can enable deduction of posture, pose, position, or gait. A team member can wear a vest or bodysuit that has strain gauges or sensors to detect heart rate, respiration, and perspiration. The team member&#39;s canteen can also have a sensor to monitor the amount of water or fluids consumed from the canteen. The soldier&#39;s weapon can be instrumented for cooperative robotic weapon control. For example, as shown in  FIG. 7 , when a soldier  700  points and/or fires his weapon  710  in a certain direction, a robotic platform  720  can point a weapon  730  in a substantially similar direction. The robot&#39;s weapon  730  can be a surrogate automatic or semi-automatic weapon that can be mounted on a pan-tilt unit. Equipment for TSS may require the team member to carry extra weight, but eliminates bulky OCU equipment. 
         [0064]    The TSS  505 ,  605  receives task results at an information manager  642 , which transmits information to a verbal command module  643  and an alert manager  644 , which in turn communicates with alert devices  645 . The information manager  642  manages the network, captures messages, and decides which messages to listen to and which messages to send. For example, the information manager  642  can decide whether to send a message to a group or to one person and the best way of sending the message. The information manager  642  also identifies the team member&#39;s state  646 , such as geolocation, physiology, and weapon state. The information manager  642  communicates the team data and tasks or commands to the perceivers  611 . 
         [0065]    User feedback devices can enable alerts and information transfer back to team members without distracting the team member or obstructing his or her senses. User feedback management software can provide user feedback and manages the information flow to the team members, taking into account available bandwidth, information criticality, the tactical situation, and the estimated cognitive burden. 
         [0066]    Unmanned platform interface software  510 ,  610  can provide the functionality to provide connectivity from the C2 software  500 ,  600  to enable control of the unmanned vehicle. The unmanned vehicle interface kits  510 ,  610  can have devices, software, and hardware for integrating the C2 software  500 ,  600  with selected unmanned platforms. An unmanned vehicle common interface  510 ,  610  can use plug-in interfaces for the capability of controlling current and future unmanned vehicle platforms. The unmanned vehicle interface  510 ,  610  has an unmanned vehicle native platform controller  565 ,  665  that can control a common command interface  570 ,  670 , a common information interface  575 ,  675  and a common physical interface  580 ,  680 . The native platform controller  565 ,  665  takes information from common command interface  570 ,  670  and the common information interface  575 ,  675  and converts into the custom physical interface  580 ,  680 . The common command interface  570 ,  670  translates the commands from the plan execution  530 ,  630  in a common command language into a native controller language of the platform. The common information interface  575 ,  675  collects information and provides products such as video, pictures, audio, and the like, to the feedback manager  535 ,  635 . The custom physical interface  580 ,  680  can involve the interaction with hardware or mounting, such as determining how to get power or control pan tilt. 
         [0067]    Referring to  FIG. 8 , an architecture demonstrating the interface between an unmanned vehicle framework  800  and an unmanned vehicle interface  810  is shown. In the unmanned vehicle interface  810 , which can resemble the unmanned vehicle interface  510 ,  610  in  FIGS. 5 and 6 , an unmanned vehicle native platform controller  820  can control a common command interface  825 , a common information interface  830 , and a custom physical interface  835 . The common command interface  825  has modules such as a JAUS (Joint Architecture for Unmanned Systems) interface (e.g., to convert a command a send it to the platform), teleoperation interfaces (e.g., determine how to teleoperate and convert for the platform), and/or any well-defined interface, whether or not it is a standard. The common information interface  830  has modules such as a 2D (e.g., send pictures from a soldier or sensor, video frames, or streaming media, or a laser line scanner), 3D (e.g., radar, ladar, laser range information including distance), messages that go back and forth, and the like. The custom physical interface  835  has modules such as power, mechanical connections, network power, radio hookups, pan tilt, additional sensors, and/or other additional physical components. In one example of an interaction between the components of the unmanned vehicle interface  810 , if the common command interface  825  and the common information interface  830  require a plug-in for a new device, the custom physical interface  835  may respond by querying whether a new antenna is needed. 
         [0068]    The unmanned vehicle interface can convert the system information and control data into native control directives for the base platform. Each protocol can be supported by a plug-in that handles the translation. As a result, this approach can support new platforms and protocols. 
         [0069]    The unmanned vehicle framework  800 , which can represent software and/or hardware components shown in the TSS and C2 software shown in  FIG. 5 , can have tactical behaviors  840  (e.g., follow soldier or a verbal command) that are commanded to the common information interface  830 . The unmanned vehicle framework can also have a reasoning component  845 , which receives information from the custom physical interface  835  to provide to perceivers, such as a soldier perceiver or navigation perceiver. 
         [0070]    The architecture in this exemplary configuration can be applicable to any unmanned vehicle or robotic platform. The architecture can be specifically designed to utilize common interfaces that incorporate platform-specific drivers. The software components interact with these interfaces (e.g., physical, command, and information) and the interfaces utilize platform-specific drivers to accomplish their tasks. For example, the software components may instruct the robotic system to “go forward 10 meters.” This command is passed to the command interface, which translates it to machine instructions via a driver for JAUS, CanBUS, USB, or other similar platform protocols. JAUS is the joint architecture for unmanned systems. JAUS is formerly known as joint architecture for unmanned ground systems (“JAUGS”). CanBUS is a controller area network multicast-shared serial bus standard. 
         [0071]    Referring to  FIG. 9 , an exemplary architecture is shown for the system. Mounted nodes  910 , other information systems  915 , and dismounted nodes  905  can communicate with an information management component  920 . The mounted nodes  910  can offer information regarding targeting, plans, and detecting enemy soldiers. The other information systems  915  can be used to monitor and analyze current on-the-ground command and control. The dismounted nodes  905  can provide information from soldiers on a battlefield, including the platoon and company level, as well as those soldiers associated with a different unit. The information management  920  can provide both situational awareness  935  as well as information to the perceivers  930 . Sensors  925  can also provide information to the perceivers  930 . The sensors  925  can provide information to an annotated  3 D terrain map  965 , which can be used by the perceivers  930  rather than using raw sensor data. The perceivers  930  and the situational awareness  935  can be provided to the cognitive models  940 , which also learn from training  953 , case based learning  963 , and TTPs and mission plans  957 . The cognitive models  940  include intent of a squad  945  and soldier  950 , as described above with respect to the other architectural configurations. The cognitive model  940  has a reasoner  955  that can generate a plan, which is sent to the unmanned vehicle control  960  for execution. An executive  970  can direct the weapon control or vehicle control  985 , through tactical behaviors  975  and mobility behaviors  980 . 
         [0072]    Referring to  FIG. 10 , a method of autonomous control can proceed as follows. First tactical behaviors can be developed  1010 . Network information can be used to convey information from team members and/or sensors  1020 . A determination can be made as to the mission, situation, squad disposition, and soldier cognitive/emotional state  1030 . A cognitive model can be driven  1040 . The current state, goals, and intentions can be inferred  1050 . An appropriate behavior can be selected  1060 . Soldier/robot TTP and tactical behavior can be mapped  1070 . Optionally, feedback can be provided  1080 . This method is not intended to be limited to only these steps or the order thereof. 
         [0073]    The system can enable the robotic platform or unmanned vehicle to understand the tactical situation by observing the team members. The TSS can provide the robot with location, weapon state, posture, and physiology information. Each team member can serve as a sensor for detecting the environment and interpreting it for the unmanned vehicle. The C2 software on each unmanned vehicle can collect information from each team member as well as from the robotic platform&#39;s sensors. Using cognitive models, the C2 software reasons about how the team members have positioned themselves, how they are moving, their postures, whether they are pointing their weapons, whether the weapons safeties are off, and the state implied by each team members physiology. In view of this information, the system generates an estimate of squad intent, which is used to develop a short-term, simplistic plan known as the playbook action (“PA”). The system executes the PA and monitors the results. As long as the PA remains valid, the unmanned vehicle continues to execute it. If the system changes the estimate of intent or receives direct feedback from a team member, the system can modify or replace the current PA. 
         [0074]    The PA generator derives a short-term play for the platform that is appropriate for the situation, understood and expected by the team members, and consistent with the team&#39;s training with the platform. The PA generator evaluates the current tactical state provided by the cognitive model to determine which play to call from the playbook. Plays are short-term action plans, customized to fit the current situation. The playbook approach provides control at a high level of abstraction, but leaves the details of execution to the execution control and monitoring layer of the system architecture. In the C2 paradigm, all team members (human and robotic) share the same definition of a play (e.g., a battle drill) and understand the goals and acceptable behaviors for each member. 
         [0075]    The playbook is developed based on current training materials and TTPs. The system selects a play on the fly by a team member&#39;s command override or by a situation and intent analysis. Playbooks minimize the necessity for human interaction, while maximizing the capability of humans to interact and control the situation for optimal achievement of mission objectives. 
         [0076]    Referring to  FIG. 11   a , an exemplary playbook  1100  is shown. In this playbook  1100 , the platform can choose between ammunition resupply, corpsman 911, logistics carrier, formation move, rally, designator teleoperations, IED detection, and breach. In  FIG. 11   b , once IED detection is chosen as the course of action, for example, the PA generator has short plays for the current tactical situation, including, but not limited to, detect designated object, discover designated object, calculate heading and distance, laser designation, net-centric designation, move to object, follow path, avoid obstacle, employ IED sensor, deploy IED sensor, wait, and report results. 
         [0077]    The execution control and monitoring layer sequences the PA, monitors the intermediate results, and determines if the play is succeeding, failing, or being overtaken by events. The play executive ensures that the PA created by the PA generator is executable and executed. The play sequencer is the primary executive for the platform. The play sequencer has explicit knowledge about the system behaviors and the capabilities of the underlying platform. The play sequencer can be used to create platform and context specific executable robotic actions that will achieve the objectives of the play. Real-time monitoring detects exceptions in execution performance and exception handling provides repair actions for exceptions identified by action monitoring. 
         [0078]    The playbook monitor (“PM”) evaluates the status of the current PA, reasoning at the level of abstraction of the original play produced by the PA generator to determine what feedback, if any, to provide to the team member via the feedback manager. 
         [0079]    In order to construct a computational cognitive model, the system can use an existing cognitive framework, such as the Sandia Cognitive Framework, or build the cognitive model using languages such as ACT-R or SOAR. 
         [0080]    The system can adapt, learn, and train with the team in an effort to avoid obsolescence or being overtaken by events. Learning can be based upon many sources of information. Verbal commands, command overrides, and consent-by-taking-no-action provide feedback to the system on the quality of its understanding and action generation. TTPs, battle drills, x-files, and field manuals offer information on proper actions to take in tactical situations. Additionally, mission-recording and human-in-the-loop after action reviews can provide an environment in which situational understanding and action generation can be assessed and modified. 
         [0081]    Training and learning can occur on many levels. Tactical preferences can be minor modifications to the play. Team preferences for certain aspects are not defined in TTP or battle drills. The system can learn to adjust tactical timing (e.g., the time interval between team members crossing the street or a line of sight). The system understands roles, thereby operating at a higher level of abstraction. When a team leader calls plays, a predefined PA is prompted by the PA generator. The cognitive model can learn how to respond to a new situation and how to differentiate the new situation from the known situation (e.g., schema differencing). A team member guides a robot step-by-step through a new process, allowing the PA generator to build a new play. The new play can be associated with a new verbal command, extending the command vocabulary. Behavioral preferences are an extension of play recording. Data can be recorded during training and actual missions to provide adjustments to improve execution and coordination with a given team. 
         [0082]    Learning can be test-based on confidence metrics derived from semantic network situational understanding, case-based reasoning (e.g., comparing the current situation with historical cases), learning from training (e.g., parameterization of “playbook” actions, when playbook actions are appropriate, and responsibilities of different squad roles), or reinforcement learning (e.g., feedback from soldiers when inappropriate behavior is produced in the form of real-time verbal feedback or after-action review). The system can also learn from soldier interaction or response to events and objects. The system can learn from a squad-specific approach to tactical situations or soldier-specific physiological and behavioral response to threats. The system stores cases to guide real-time assessment. The system can also collect confirmatory information to validate a situational hypothesis. 
         [0083]    Learning of tactics and appropriate action can be enabled whenever a human gives corrective input to the human. This can be in the form of verbal override commands or “after action” analysis of the robot&#39;s performance. The human&#39;s corrective input can used to define and differentiate a situation where the new behavior is required and to enable the system to detect the appropriate situational markers (e.g., team positioning, team actions, changes in human physiology) that can be used in the future to trigger the new behavior. Referring to  FIG. 12 , feedback from both the current robot&#39;s behavior and corrections from the human can initiate learning in the robot resulting in modification to the state transition. A robot behavioral model  1210  can utilize inferences from perception  1220 , overt commands, or a human&#39;s actions to learn a new behavior. The robot&#39;s actions and results are sent as feedback to the human cognitive model  1200 . Both the soldier and the robot can analyze the situation in view of the mission plan, tactical picture, and squad disposition. 
         [0084]    For verbal commands, the system can include voice understanding, a fixed command set, command override, the ability to learn new verbal commands, and gesture commands with an instrumented glove. The command vocabulary can include, for example, point, flank, guard, and evac. 
         [0085]    The systems and methods can combine long-wave infrared images from an IR-sensitive camera (e.g., a FLIR A20) with corresponding images from other devices (e.g., two cameras in a stereo configuration, such as a PGR Bumblebee, a color camera, and a LADAR scanner) to detect humans. These sensors are integrated in the net-centric environment. 
         [0086]    Each soldier or team member can be outfitted with a PDA, wearable computer, or a computer that is implemented in one of their devices, such as a computer in the scope of the weapon. The information regarding the soldier can be transmitted through a wireless network from the computer to the unmanned vehicle or robotic platform. 
         [0087]    Tactical maneuvers can include following a team member, rally to a named point or team member, formation movement, maneuver to a fire position, wall hugging, low observability movement, and stealthy movement. The system is also capable of tactical understanding and role-based behavior, including safe operations with instrumented team members or other personnel, simpler roles such as “guard in place,” or more sophisticated roles such as “point man” or “rear guard.” 
         [0088]    In one example of this configuration, a team moves through a city with unmanned ground vehicles (e.g., a Talon) moving along with them to augment the team&#39;s capabilities in remote inspection, improvised explosive device (“IED”) detection, and ammunition resupply. The team sees an object and designates it as suspicious. The team verbally commands a Talon to inspect the suspicious object. The Talon employs IED sensors and reports back to the team. As a result, a team member does not have to perform continuous operator control of the Talon and can multitask while the Talon moves to and from the suspicious object. The Talon is put in harm&#39;s way, rather than a human. Other benefits can include a reduction in time to achieve mission goals and capabilities of those team members due to an automation of repetitive tasks. Additionally, the unmanned ground vehicles assume risks from the team members, which can increase team member survivability. 
         [0089]    In another example of this configuration, the team comes under fire during a routine patrol. In this example, an unmanned ground vehicle (e.g., a Gladiator) is in point position and an unmanned aerial vehicle (e.g., a Dragon Eye) is automatically maintaining position a few blocks in front of the squad. When the team gets excited and point safety-off weapons at a location, the Dragon Eye sweeps back to give an overhead situation awareness of the target area. The Gladiator moves towards the threat, drawing fire and moving its sensors into a better position for detection. The Gladiator monitors the street with its onboard sensor suite, alerting the team to new intruders. In this example, the team members do not have to perform any operator control of the unmanned ground/air vehicles or constantly monitor their progress. Additionally, these unmanned ground/air vehicles can augment the team&#39;s capabilities in scouting and reconnaissance without a team member on point, just in case an ambush occurs. 
         [0090]    In another example of this configuration, the team is on a movement to contact mission and aim their weapons at a threat. The team members verbally command the Gladiator for cover fire. The Gladiator realizes that the team members are stressed, their weapons are safety-off, and they are firing at a threat. The Gladiator triangulates the threat&#39;s location and positions itself to cover fire on command. When a team member is wounded, the Gladiator automatically provides cover for him and the corpsmen. On command from a corpsman, the Gladiator acts as a MEDEVAC when the wounded team member is stabilized. In this example, the unmanned ground vehicle can carry additional ammunition and medical supplies for a resupply, even under fire. The unmanned ground vehicle can even provide additional suppressive fire to enable the team members to maneuver. 
         [0091]    In another example, the team members designate a vehicle and verbally command the Talon to inspect it. The Talon autonomously approaches the vehicle and employs onboard sensors, reporting results back to the team. The Gladiator provides cover, ready to fire on command. The Gladiator monitors team weapon status to determine threat status and responds accordingly. The Dragon Eye performs reconnaissance several miles ahead, alerting the team of approaching vehicles. In this example, the team members are provided with a greater standoff from potential threats and are provided with an early warning of approaching threats. 
         [0092]    In one example of using this configuration, a soldier walks forward and a robot takes point in front of the soldier providing cover in case of a surprise attack. The soldier switches the safety to “off” on the weapon and assumes a tactical posture. The robot tacks back and forth in a general direction to flush out hidden enemies, maintain a view of the soldier to analyze the body pose and hand signals, position itself to provide cover for the crouching soldier, and retreat if necessary when the soldier starts retreating by providing rear guard and cover for the soldier. 
         [0093]    Key events and cooperative behaviors can include, but are not limited to, team movement, formation change, verbal override, providing cover fire, ammunition resupply, and protecting a downed (e.g., injured or wounded) soldier. For team movement, the robot can move appropriately into a formation, including line, wedge, or column. For formation change, the robot can detect change in formation and respond accordingly. For verbal override, the robot can change position from default in response to verbal directive. For providing cover fire, the robot can detect soldier/team pose, elevated physiology, weapon orientation and status (e.g., safety “off” or firing), and estimates location of threat to provide cover fire for the team. For ammunition resupply, the robot can detect soldier ammunition as low and move to resupply the soldier with additional ammunition. For protecting a downed soldier, the robot can detect a wounded soldier through pose and physiological status, maneuvering between the wounded soldier and the estimated threat. 
         [0094]    In another example, referring to  FIG. 13   a , a fire team ingresses an area of building A using a satchel charge to enable an assault on an enemy force T. In  FIGS. 13   b  and  13   c , the fire team continues to ingress the area and approaches using bounding overwatch. In  FIG. 13   d , the soldiers begin firing on the target. In  FIG. 13   e , robot R 1  provides cover fire. In  FIG. 13   f , robot R 2  runs gauntlet with the satchel charge and drops it at a wall of building A. In  FIG. 13   g , robot R 2  moves outside the explosive range and the soldiers detonate the charge. In  FIG. 13   h , robots R 1  and R 2  provide cover fire as the soldiers storm the breach. 
         [0095]    In yet another example, referring to  FIG. 14   a , a plurality of soldiers execute a “through the door” TTP to breach a doorway and enter a room having assaulting enemy forces therein. Referring to  FIG. 14   b , soldier  3  breaches the door. Referring to  FIG. 14   c , soldier  3  retreats and soldier  1  goes through the door and to the left. Referring to  FIG. 14   d , soldier  2  goes through the door to the right. Referring to  FIG. 14   e , soldier  3  goes through the door to the left. Referring to  FIG. 14   f , soldier  4  does through the door to the right. 
         [0096]    In an alternative configuration, robots can assume randomly selected positions or roles in the action. For example, referring to  FIG. 14   g , robot R 1  takes the place of soldier  3  and robot R 2  takes the place of soldier  2 . 
         [0097]    In still yet another example, a soldier is wounded during an assault and robots work as a team to protect and evacuate the downed soldier. Referring to  FIG. 15   a , a fire team is assaulting a defended enemy position. Robots R 1  and R 2  provide rear security. Referring to  FIG. 15   b , soldier  2  is wounded. Soldier  2 &#39;s impact sensor senses the wound and reports via the network. Referring to  FIG. 15   c , robot R 1  provides physical cover and robot R 2  provides wide-area cover fire. Referring to  FIG. 15   d , soldier  4  assists to attach soldier  2  to robot R 1 . Referring to  FIG. 15   e , robot R 2  moves to provide physical cover. Referring to  FIG. 15   f , soldier  4  moves back to cover, robot R 1  drags soldier  2  out of the area, and robot R 2  provides physical cover and cover fire. 
         [0098]    When the system is utilized, a robot can perform cooperative, tactically correct behavior without human interaction or cognitive burden. In a dismounted mode, the robot operates as an integrated and trained member of a team, understands team mission and tactics, needs no human intervention during short-term high-intensity conflict, has situational awareness and understanding by discovery and harvesting the net-centric information streams. In a mounted mode, a robotic “wingman” can automatically support and protect a manned vehicle; can understand machine readable mission plans, situational awareness and understanding, and targeting streams; can provide automated net-centric fire platform; and the automatic tactical behavior reduces the need for robotic controllers. 
         [0099]    Referring to  FIG. 10 , in one configuration, the system is integrated in a small-unit unmanned ground vehicle for high-stress operations, such as military operations on urban terrain (“MOUT”) scenarios, without using an OCU. The system will use tactical behaviors; use netcentric information from instrumented sources to determine the mission, situation, squad disposition, and soldier cognitive/emotional state; monitor the soldiers&#39; positions and poses to detect changes in a tactical state; utilize a cognitive model of the soldier and the squad to infer the current state, goals, and intentions; based on the inference, select the appropriate behavior for the unmanned ground vehicle to support the squad in the current situation; map the soldier/robot TTP and tactical behavior into the current situation and terrain; and provide non-computer, non-RF feedback to the soldier from the robot (e.g., pointing at a suspected enemy location). The system can be constantly updated with information from external sources. 
         [0100]    The embodiments described above are intended to be exemplary. One skilled in the art recognizes that numerous alternative components and embodiments that may be substituted for the particular examples described herein and still fall within the scope of the invention.