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  Robust uninhabited air vehicle active missionsUSPTO Application #: 20070061116Title: Robust uninhabited air vehicle active missions Abstract: A command sequence for an autonomous UAV mission is optimized by simulating the performance of a mission in a model environment. Using a genetic algorithm, neural net, or other suitable technique this command sequence is then optimized, to improve the outcome of the mission. A factor in selecting an optimal command sequence will be its compressability. A set of one or more optimal command sequences is compiled. Each optimal command sequence is encoded into an algorithmic active packet of minimum size for uploaded to the UAV, which then executes the mission. To track the UAV in its performance of the mission without compromising its location, the active packets are executed in the simulated environment. The simulated environment is continually updated with the most current available information. The simulation results are an approximation of the current state of the UAV. (end of abstract)
Agent: Paul J. Esatto, Jr. Scully, Scott, Murphy & Presser - Garden City, NY, US Inventor: Stephen Francis Bush USPTO Applicaton #: 20070061116 - Class: 703008000 (USPTO) Related Patent Categories: Data Processing: Structural Design, Modeling, Simulation, And Emulation, Simulating Nonelectrical Device Or System, Mechanical, Vehicle The Patent Description & Claims data below is from USPTO Patent Application 20070061116. Brief Patent Description - Full Patent Description - Patent Application Claims
Background of the Invention [0001] 1. Field of Invention [0002] The invention relates generally to the field of Uninhabited Air Vehicles (UAVs), and more particularly, it relates to a method of training and monitoring a UAV for a specific mission. [0003] 2. Description of Related Art [0004] Autonomous unmanned air vehicles (UAV) have great potential for military and civilian use. Clearly, intelligent unmanned vehicles can readily be sent into hostile situations without fear of casualties. In addition, because the aircraft is intelligent, communication with the vehicle is unnecessary thus increasing its undetected surveillance capability. [0005] Current UAVs have not met the degree of safety and reliability required for autonomous operation over populated areas or in airspace shared with commercial aircraft. Autonomy technologies that can provide reflexive responses and rapid adaptation (as exhibited by a pilot) to compensate for a vehicle's structural, perceptual and control limitations are lacking. This is particularly evident when UAV mishap rates are compared to those of piloted systems. [0006] Compared to piloted aircraft systems, current UAVs are designed to be very low cost, use smaller low-power commercial off-the-shelf components and have very limited redundancy. Unfortunately, the lower requirement for reliability has led to higher failure rates. The higher failure rate is seen as somewhat acceptable because it does not mean the loss of human life, except when the vehicle flies over populated areas. It is desirable, however, for a UAV to be able to safely fly over populated areas, to safely share airspace with other piloted vehicles, and to generally improve the mission success rate. For these reasons, the UAV control systems must be capable of rigorously analyzing and predicting component failures and their effects to determine the appropriate response to faults much as a pilot does prior to or as a result of system failure. BRIEF SUMMARY OF THE INVENTION [0007] The present invention includes providing a simulation of the environment the UAV is to operate in, and simulating the performance of a mission by the UAV. This simulation takes into account environmental stimuli and mission objectives, and outputs some mission outcome. The command sequence is then optimized using a genetic algorithm, neural net, or other suitable technique, to improve the outcome of the mission. A set of one or more optimal command sequences to achieve the mission is compiled, and each optimal command sequence is encoded into an algorithmic active packet of minimum size. An active packet is the object communicated in an active network. Active networks are a recent development in computer science and networking technology. The application of active networking to the present invention will be elaborated, infra. These active packets are uploaded to the UAV, which then executes the mission. [0008] To track the UAV in its performance of the mission without compromising its location, the active packets are executed in the simulated environment. The simulated environment is continually updated with the most current available information. The simulation results are an approximation of the current state of the UAV. BRIEF DESCRIPTION OF THE DRAWINGS [0009] These and other features, aspects and advantages of the present invention will be apparent from the following drawings, description and appended claims, where: [0010] FIGS. 1A and 1B, bridged by connector A, represent a flow chart of an exemplary embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION [0011] It is desirable for a UAV operating over hostile territory to be undetectable. Towards that end, limiting or eliminating radio transmissions to and from the UAV decreases the likelihood of detection. Therefore, a UAV capable of operating autonomously without the need to report its status to a remote control system and receive commands from it is less detectable. Further, an autonomous UAV is not vulnerable to having its commands overridden by an outside source. [0012] In order to achieve this goal of autonomy, a UAV must incorporate all decision making into the vehicle while executing a mission. One question that arises is how to best communicate the mission to the UAV. The mission may be represented by static waypoints and commands. However, it can be more efficient to represent the mission in a programmatic or algorithmic manner. [0013] The co-pending application "Optimistic Distributed Simulation for a UAV Flight Control System", Ser. No. ______ (unassigned, attorney docket no. 14874), hereby incorporated by reference, is directed toward active network control of a UAV. Active network control includes state objects that comprise executable code to process the control model. The active missions of the present invention define the executable code for a given UAV mission. [0014] Referring now to FIG. 1A, in an exemplary embodiment, the method of the present invention, generally 100, begins 102 by preparing a simulation 104 of the environment the UAV is to operate in. The simulated environment could include topographical terrain information, known weather conditions and their predicted movements, and/or known enemy locations. [0015] Additionally in preparation, the mission objectives must be defined 106. In one illustration, a reconnaissance mission has the objectives to pass through a given waypoint, take a photograph, and return to base. [0016] A simplistic model of this mission would be a set of intermediate waypoints associated with commands to be executed at those waypoints. The waypoints trace the course of the mission, and the commands specify the actions the UAV will take to achieve the mission at each waypoint. For example, the instruction at an intermediate waypoint may be a null, i.e., an instruction to take no action. The instruction at the target waypoint could be to take a picture. [0017] A randomized, though feasible, command sequence is initially generated 108. A feasible command sequence is one that can achieve the mission goals, and is within the capabilities of the UAV. For example, a next waypoint that cannot be reached by the UAV, either because of a turn radius that is impossible to achieve or because it is beyond the operating range of the UAV, is unfeasible. The initial command sequence is simulated 110, and the outcome is evaluated 112, for example against a fitness function. [0018] When using a genetic algorithm as part of the optimization according to the present method, a fitness function is defined, in a manner known in the art. In this case, the fitness function measures the outcome of the UAV simulation of the command sequence. The fitness function consists of measurable objectives towards achieving the mission goal. An example fitness function for this sample mission might include the following elements: TABLE-US-00001 TABLE Fitness Function Elements Measurable damage to the UAV, with emphasis on the flight capability and whether the camera remains in an operational state (minimize damage) The minimum distance ultimately reached by the UAV from the target to be photographed (minimize target error) The minimum distance of the UAV from base after the target has been photographed and begins the return flight (minimize return error) Estimated complexity of the command sequences generated based upon Minimum Data Length (MDL) theory (minimize complexity) [0019] The evaluation of the outcome is compared against some threshold value 114, to determine if more modification 116 is necessary. Care must be taken to avoid converging on a local, rather than global, minimum or maximum value of the fitness function. Through iterative simulation, an optimal command sequence to achieve the mission is developed. [0020] Continuing with example of the genetic algorithm procedure, parent selection, mating and mutation are then performed to optimize the outcome according to the fitness function. Again, this genetic algorithm technique is known in the art, and need not be discussed further. See Schatten, A., Genetic Algorithm Short Tutorial, http://www.ifs.tuwien.ac.at/.about.aschatt/info/ga/genetic.html, which is hereby incorporated by reference. Continue reading... Full patent description for Robust uninhabited air vehicle active missions Brief Patent Description - Full Patent Description - Patent Application Claims Click on the above for other options relating to this Robust uninhabited air vehicle active missions patent application. ###  How KEYWORD MONITOR works... a FREE service from FreshPatents1. Sign up (takes 30 seconds). 2. Fill in the keywords to be monitored. 3. Each week you receive an email with patent applications related to your keywords. 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