Increased vehicle autonomy, survivability and utility can provide an unprecedented impact on mission success and are
one of the most desirable improvements for modern autonomous vehicles. We propose a general architecture of
intelligent resource allocation, reconfigurable control and system restructuring for autonomous vehicles. The architecture
is based on fault-tolerant control and lifetime prediction principles, and it provides improved vehicle survivability,
extended service intervals, greater operational autonomy through lower rate of time-critical mission failures and lesser
dependence on supplies and maintenance. The architecture enables mission distribution, adaptation and execution
constrained on vehicle and payload faults and desirable lifetime. The proposed architecture will allow managing
missions more efficiently by weighing vehicle capabilities versus mission objectives and replacing the vehicle only when
it is necessary.
Unmanned Air Vehicles (UAVs) are expected to dramatically alter the way future battles are fought. Autonomous
collaborative operation of teams of UAVs is a key enabler for efficient and effective deployment of large numbers of
UAVs under the U. S. Army's vision for Force Transformation. Autonomous Collaborative Mission Systems (ACMS)
is an extensible architecture and collaborative behavior planning approach to achieve multi-UAV autonomous
collaborative capability. Under this architecture, a rich set of autonomous collaborative behaviors can be developed to
accomplish a wide range of missions. In this article, we present our simulation results in applying various autonomous
collaborative behaviors developed in the ACMS to an integrated convoy protection scenario using a heterogeneous team
UAVs are critical to the U. S. Army's Force Transformation. Large numbers of UAVs will be employed per Future
Combat System (FCS) Unit of Action (UoA). To relieve the burden of controlling and coordinating multiple UAVs in a
given UoA, UAVs must operate autonomously and collaboratively while engaging in RSTA and other missions.
Rockwell Scientific is developing Autonomous Collaborative Mission Systems (ACMS), an extensible architecture and
behavior planning/collaboration approach, to enable groups of UAVs to operate autonomously in a collaborative
environment. The architecture is modular, and the modules may be run in different locations/platforms to accommodate
the constraints of available hardware, processing resources and mission needs. The modules and uniform interfaces
provide a consistent and platform-independent baseline mission collaboration mechanism and signaling protocol across
different platforms. Further, the modular design allows for the flexible and convenient extension to new autonomous
collaborative behaviors to the ACMS. In this article, we first discuss our observations in implementing autonomous
collaborative behaviors in general and under ACMS. Second, we present the results of our implementation of two such
behaviors in the ACMS as examples.
We illustrate an approach for planning UAV sensing actions in urban or constrained domains. We plan and optimize a collection strategy for a target of interest using Design Sheet, a numeric/symbolic algebraic constraint propagation package. Once a set of sensing plans have been developed, we use a probabilistic roadmap planning algorithm to plan a route for a fixed wing UAV through urban terrain to collect that information. This planner has several novel features to improve performance for urban domains.
UAVs are a key element of the U. S. Army's vision for Force Transformation, and are expected to be employed in large numbers per FCS Unit of Action (UoA). This necessitates a multi-UAV level of autonomous collaboration behavior capability that meets RSTA and other mission needs of FCS UoAs. Autonomous Collaborative Mission Systems (ACMS) is an extensible architecture and behavior planning / collaborative approach to achieve this level of capability. The architecture is modular and the modules may be run in different locations/platforms to accommodate the constraints of available hardware, processing resources and mission needs. The modules and uniform interfaces provide a consistent and platform-independent baseline mission collaboration mechanism and signaling protocol across different platforms. Further, the modular design allows flexible and convenient extension to new autonomous collaborative behaviors to the ACMS through: adding new behavioral templates in the Mission Planner component; adding new components in appropriate ACMS modules to provide new mission specific functionality; adding or modifying constraints or parameters to the existing components, or any combination of these. We describe the ACMS architecture, its main features on extensibility, and updates on current spiral development status and future plans for simulations in this report.
UAVs are a key element of the Army’s vision for Force Transformation, and are expected to be employed in large numbers per FCS Unit of Action (UoA). This necessitates a multi-UAV level of autonomous collaboration behavior capability that meets RSTA and other mission needs of FCS UoAs. Autonomous Collaborative Mission Systems (ACMS) is a scalable architecture and behavior planning / collaborative approach to achieve this level of capability. The architecture is modular and the modules may be run in different locations/platforms to accommodate the constraints of available hardware, processing resources and mission needs. The Mission Management Module determines the role of member autonomous entities by employing collaboration mechanisms (e.g., market-based, etc.), the individual Entity Management Modules work with the Mission Manager in determining the role and task of the entity, the individual Entity Execution Modules monitor task execution and platform navigation and sensor control, and the World Model Module hosts local and global versions of the environment and the Common Operating Picture (COP). The modules and uniform interfaces provide a consistent and platform-independent baseline mission collaboration mechanism and signaling protocol across different platforms. Further, the modular design allows flexible and convenient addition of new autonomous collaborative behaviors to the ACMS through: adding new behavioral templates in the Mission Planner component, adding new components in appropriate ACMS modules to provide new mission specific functionality, adding or modifying constraints or parameters to the existing components, or any combination of these. We describe the ACMS architecture, its main features, current development status and future plans for simulations in this report.
Most technologies in use or proposed for use to detect landmines and unexploded ordnance (UXO) suffer from unacceptably high false-alarm rates, even at modest probabilities of detection. High false-alarm rates are a consequence of the inability to discriminate real UXO and landmines from man-made and naturally occurring clutter. The goal of the Defense Advanced Research Project Agency (DARPA)- sponsored Background Clutter Data Collection Experiment is to provide data which will support the development of techniques that are more adept at discriminating UXO from benign, man- made objects. During the fall of 1996, high areal density site surveys were completed using the following sensor types: magnetometer, infrared, electromagnetic induction, and ground- penetrating radar. Preliminary analysis of the data confirmed that a large number of anomalies in the sensor data are visually indistinguishable from anomalies that are a result of emplaced inert UXO or landmines. The Firing Point 20 site at Fort A. P. Hill exhibits the largest number of these ordnance- like anomalies. To determine the source of a subset of these sensor response anomalies, a 1-week excavation effort was conducted. This paper presents an analysis of the data to determine the candidate locations for, the procedures used during, and the results of the excavation.
Clutter is the largest factor contributing to the poor detection rates and high false-alarm rates for mine and unexploded ordnance (UXO) detection systems. The source of this clutter can be either naturally occurring or anthropic. Because the standard detector technologies are anomaly-based systems, few features within the sensor data permit mitigation of false alarms or provide an avenue to enhance detection rates. To achieve operational detection performance, a better understanding of clutter statistics is required at the single pixel level and at the feature level. This paper presents an in-depth assessment of the statistical properties of clutter and target signatures for a specific test site. This assessment uses data collected during the Defense Advanced Research Projects Agency (DARPA) Background Clutter Data Collection Experiment. Pixel-level statistics for electromagnetic induction detection systems are discussed. The resulting statistical distribution functions for clutter and targets exhibit poor separation. Improved separation of the distribution functions is achieved if features are employed. For example, by measuring the particular size and shape features of target signatures, the false-alarm rate can be reduced with minimal decrease in the detection rate. By using feature-level information, improved system performance can be achieved. This improved performance is dependent on the feature-level statistics of a specific site and is always limited by the overlap between the distribution functions of the clutter and target signatures. The resulting performance enhancement -- although significant -- is still far below the level required for very high detection rates and low false- alarm rates.
Mine and unexploded ordnance (UXO) detection systems must function in highly cluttered environments. Clutter leads to false alarms thereby hindering the detection and identification of targets of interest. Since the end user of the mine or UXO detection technology requires both a high detection rate and a low number of false alarms, technology demonstrations and system evaluations are designed to test these measures of performance. Detection rate and false- alarm rate are highly interdependent and must always be evaluated together. The relationship between the two rates directly affects the overall performance of the sensor in the field. Poor performance of a system in either detection rate or false-alarm rate causes a substantial increase in risk of undetected and thus unmarked or unremediated mines or UXO. A system with a low detection rate will leave many mines or UXO undetected. Performance can be traded between probability of detection and false-alarm rate by changing the system threshold. Raising or lowering the threshold will cause both the detections and false alarms to decrease or increase together. A system with a high false-alarm rate result in an increase in the time required to investigate potential targets. Therefore the rate of advance and rate of clearance decrease. With limited clearance resources, site coverage may become too time consuming or costly for operationally effective clearance, resulting in risk from undetected mines and UXO in areas that have not been searched. An assessment of the connection between detection rate nd false-alarm is presented. This relationship is discussed in the context of several government-sponsored in- field technology demonstrations of prototype and commercially available mine and UXO detection technologies, as well as real clearance operations. Implications of the results of these tests and the measures of performance are discussed in the context of real-world operations, including scenarios for clearance of miens in Bosnia and of UXO at DoD sites.
The two primary measures of detection performance are probability of detection (PD) and false alarm rate (FAR). Since these are statistical measures, the large statistical uncertainties associated with measurements on small data sets impose a severe limitation on the interpretation and extrapolation of the test results. The main difficulty with the data collected from many of the landmine detection experiments is that the number of mine targets is small, and the clutter data is measured on small areas. As a result of analyzing data from several detection tests for both UXO and mine detection technologies, the Institute for Defense Analyses (IDA) has generated a number of suggestions to improve the results obtained from future demonstrations and tests. In the absence of a rigorous error analysis, we can estimate the uncertainty in a probability of detection measurement with a simple model. If detections can be described as a binomial process weighted by the true probability of detection, then uncertainty equals (root)N, where N is the number of mines detected, and uncertainty in PD (percent) equals (root)N/N X 100 for large N. A similar statistical uncertainty is encountered in the measurement of probability of false alarm. An opportunity for a false alarm can be defined by the amount of ground covered by the projected area of the target plus an allowable miss distance, say 1 m2. Thus, for each 1 m2 of ground that it passes over, the system has one opportunity to declare a false alarm. A test field that is 1 m by 20 m offers only 20 m2 of area and thus only 20 samples for probability of false alarm measurement. A site that is 4 m wide and 4 km long covers 16,000 m2. Thus, there will be a substantial increase in the clutter environment samples by the sensor and a concomitant improvement in false alarm measurements. Of course, false alarm density will be highly site dependent, so extrapolation of the result is still uncertain. Unfortunately, this limitation is insurmountable in single test and at a single site.