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 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.
The thndamental goal ofniine detection is to achieve a high detection rate along with a low false alarm rate. While many mine detectors achieve the first of these goals, it is often at the cost of a prohibitively large false alarm rate. In this paper, a Bayesian decision-theoretic approach to the detection ofmines, which incorporates the physical properties of the target response to an electromagnetic induction (EMI) device, is presented. This approach merges physical modeling ofthe evoked target response with a probabilistic description that represents uncertainty in the ground surface, composition of the mine, and its placement in the surrounding environment. This approach provides both an optimal detection scheme, and performance evaluation measures in the form ofprobability of detection (Pd) and false alarm rate (Pfa). We present results in which the model-based, Bayesian approach significantly outperforms (lower Pfa for the same Pd) the energy detector and matched filter detectors on data obtained from the DARPA Backgrounds ClutterData Collection Experiment1. In addition, the model-based, Bayesian approach is also shown to outperform a detector which estimates the eddy-current decay rate from the data. Results are also presented to illustrate the amount of sensitivity ofthe matched filter detector for a known environment to incorrect prior knowledge ofuncertain parameters in the demining scenario, as well as the robustness of performance and performance bounds realizable by the optimum detection algorithm that properly accounts for uncertainty within a Bayesian framework.The underlying principle of an EMI sensor is that a time-varying current is passed through a coil, producing a timevarying magnetic field which elicits an inductive response from a nearby object. This induced response is detected by sensing coils, generating the received signal. The form ofthe received waveform has a specific time relationship to the transmitted pulses and the system response. For conducting targets, the response of a target, s(t) , to the transmitted pulse can be approximated by a single resonant response,where A is the initial value of the response and Co r is the resonant frequency of the target. For induction systems with a low operating frequency, the ' r is purely imaginary. Thus, the resulting response has the form of a damped exponential, 5(t) = Ae_at(2)The assumption that the response in a low frequency EMI system can be modeled as a decaying exponential has been 1. The Backgrounds Clutter Data Collection Experiment sponsored by the Defense Advanced Research Projects Agency, Chemical Detection Program. Data is available by request. Vivian George, Walcoff and Associates, vgeorgewalcoff.com. 716 SP1E Vol. 3079 • 0277-786X/97/$10.00 Downloaded From: http://proceedings.spiedigitallibrary.org/ on 06/17/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx
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