In heterogeneous battlefield teams, the balance between team and individual objectives forms the
basis for the internal topological structure of teams. The stability of team structure is studied by
presenting a graphical coalitional game (GCG) with Positional Advantage (PA). PA is Shapley
value strengthened by the Axioms of value. The notion of team and individual objectives is
studied by defining altruistic and competitive contribution made by an individual; altruistic and
competitive contributions made by an agent are components of its total or marginal contribution.
Moreover, the paper examines dynamic team effects by defining three online sequential decision
games based on marginal, competitive and altruistic contributions of the individuals towards
team. The stable graphs under these sequential decision games are studied and found to be
structurally connected, complete, or tree respectively.
The consensus problem in multi-agent systems often assumes that all agents are equally trustworthy to seek agreement.
But for multi-agent military applications - particularly those that deal with sensor fusion or multi-robot formation
control - this assumption may create the potential for compromised network security or poor cooperative performance.
As such, we present a trust-based solution for the discrete-time multi-agent consensus problem and prove its asymptotic
convergence in strongly connected digraphs. The novelty of the paper is a new trust algorithm called RoboTrust, which
is used to calculate trustworthiness in agents using observations and statistical inferences from various historical
perspectives. The performance of RoboTrust is evaluated within the trust-based consensus protocol under different
conditions of tolerance and confirmation.
We present a rigorous treatment of coalition formation based on trust interactions in multi-agent systems. Current
literature on trust in multi-agent systems primarily deals with trust models and protocols of interaction in noncooperative
scenarios. Here, we use cooperative game theory as the underlying mathematical framework to study the
trust dynamics between agents as a result of their trust synergy and trust liability in cooperative coalitions. We rigorously
justify the behaviors of agents for different classes of games, and discuss ways to exploit the formal properties of these
games for specific applications, such as unmanned cooperative control.
This paper first presents an overall view for dynamical decision-making in teams, both cooperative and competitive.
Strategies for team decision problems, including optimal control, zero-sum 2-player games (H-infinity control) and
so on are normally solved for off-line by solving associated matrix equations such as the Riccati equation.
However, using that approach, players cannot change their objectives online in real time without calling for a
completely new off-line solution for the new strategies. Therefore, in this paper we give a method for learning
optimal team strategies online in real time as team dynamical play unfolds. In the linear quadratic regulator case, for
instance, the method learns the Riccati equation solution online without ever solving the Riccati equation. This
allows for truly dynamical team decisions where objective functions can change in real time and the system dynamics can be time-varying.
Proc. SPIE. 7352, Intelligent Sensing, Situation Management, Impact Assessment, and Cyber-Sensing
KEYWORDS: Detection and tracking algorithms, Sensors, Control systems, Computer programming, Simulink, Adaptive control, Motion models, Control systems design, Systems modeling, Filtering (signal processing)
In this paper, the controllability of a Mecanum omnidirectional vehicle (ODV) is investigated. An adaptive drive
controller is developed that guides the ODV over irregular and unpredictable driving surfaces. Using sensor
fusion with appropriate filtering, the ODV gets an accurate perception of the conditions it encounters and then
adapts to them to robustly control its motion. Current applications of Mecanum ODVs are designed for use
on smooth, regular driving surfaces, and don't actively detect the characteristics of disturbances in the terrain.
The intention of this work is to take advantage of the mobility of ODVs in environments where they weren't
originally intended to be used. The methods proposed in this paper were implemented in hardware on an ODV.
Experimental results did not perform as designed due to incorrect assumptions and over-simplification of the
system model. Future work will concentrate on developing more robust control schemes to account for the
unknown nonlinear dynamics inherent in the system.
During mission execution in military applications, the TRADOC Pamphlet 525-66 Battle Command and Battle Space
Awareness capabilities prescribe expectations that networked teams will perform in a reliable manner under changing
mission requirements, varying resource availability and reliability, and resource faults. In this paper, a Command and
Control (C2) structure is presented that allows for computer-aided execution of the networked team decision-making
process, control of force resources, shared resource dispatching, and adaptability to change based on battlefield
conditions. A mathematically justified networked computing environment is provided called the Discrete Event Control
(DEC) Framework. DEC has the ability to provide the logical connectivity among all team participants including
mission planners, field commanders, war-fighters, and robotic platforms. The proposed data management tools are
developed and demonstrated on a simulation study and an implementation on a distributed wireless sensor network. The
results show that the tasks of multiple missions are correctly sequenced in real-time, and that shared resources are
suitably assigned to competing tasks under dynamically changing conditions without conflicts and bottlenecks.
An ultra-wideband (UWB) inter-radio ranging technology with measurement resolution of +/-0.5 ft and range up to
0.5 kilometer under certain FCC regulation was recently introduced. However, measurement data are extremely
erroneous due to stochastic variables in the device and multipath radio wave reflections. This paper presents fuzzy
logic tuned double tracking filters as a solution to remove misinformation in the data. The 1st tracker locates the
overall center of the data in the presence of the large sporadic noise. A fuzzy logic admits only neighborhood data
to a 2nd tracker which takes care of smaller deviation noise. The fuzzy neighborhood filter approach has been
successfully applied to clean up the UWB radio ranges. Experimental results are shown.
This paper details the development of a minimal set of locally distributed navigation beacons that can provide new waypoints in dense obstacle fields. The 'beacons' provide direction and magnitude inputs for the robot to use for its next waypoint. The beacons are placed in such a manner that all locations within a bounded playing field can reach a goal area in a desired number of steps. This guarantee of total coverage comes only with tuning the magnitudes and directions of each beacon (as well as their position in the field). Key to this approach is the underlying 'color map'. The color map assigns a color to regions of the playing field based on whether the region terminates at the goal ('green'); leaves the playing field and doesn't return ('red'); or doesn’t leave the playing field but does not terminate at the goal (within a fixed number of steps)-also know as 'stagnation' ('yellow'). Changes in the placement of the beacons and their associated parameters result in changes to the color map. A software tool has been developed to allow a user to see the instantaneous changes in the color map when changes are made to the beacons. This paper will also describe how the beacons are related to both Voronoi diagrams and nearest neighbor classifiers-thus generating the final name for the navigation beacons; Voronoi Classifiers. Future work is detailed including the development of color maps for other cost metrics (such as distance traveled, power consumed or terrain trafficabilty) and efforts in developing an algorithm to find the infimum solution (minimize the maximum steps, distance, etc.).
We present two methods for a localization system, defined as the "angle of arrival" scheme, which computes position and heading of an autonomous vehicle system (AVS) fusing both odometry data and the measurements of the relative azimuth angles of known landmarks (in this case, reflectors of a stabilized laser/reflector system). The first method involves a combination of a geometric transformation and a recursive least squares approach with forgetting factor. The second method presented is a direct approach using variants of the Unscented Kalman filter. Both methods are examined in simulation and the results presented.
The U.S. Army is seeking to develop autonomous off-road mobile robots to perform tasks in the field such as supply delivery and reconnaissance in dangerous territory. A key problem to be solved with these robots is off-road mobility, to ensure that the robots can accomplish their tasks without loss or damage. We have developed a computer model of one such concept robot, the small-scale "T-1" omnidirectional vehicle (ODV), to study the effects of different control strategies on the robot's mobility in off-road settings. We built the dynamic model in ADAMS/Car and the control system in Matlab/Simulink. This paper presents the template-based method used to construct the ADAMS model of the T-1 ODV. It discusses the strengths and weaknesses of ADAMS/Car software in such an application, and describes the benefits and challenges of the approach as a whole. The paper also addresses effective linking of ADAMS/Car and Matlab for complete control system development. Finally, this paper includes a section describing the extension of the T-1 templates to other similar ODV concepts for rapid development.
Proprioception is a sense of body position and movement that supports the control of many automatic motor functions such as posture and locomotion. This concept, normally relegated to the fields of neural physiology and kinesiology, is being utilized in the field of unmanned mobile robotics. This paper looks at developing proprioceptive behaviors for use in controlling an unmanned ground vehicle. First, we will discuss the field of behavioral control of mobile robots. Next, a discussion of proprioception and the development of proprioceptive sensors will be presented. We will then focus on the development of a unique neural-fuzzy architecture that will be used to incorporate the control behaviors coming directly from the proprioceptive sensors. Finally we will present a simulation experiment where a simple multi-sensor robot, utilizing both external and proprioceptive sensors, is presented with the task of navigating an unknown terrain to a known target position. Results of the mobile robot utilizing this unique fusion methodology will be discussed.