Abstract: A method and system for generating an optimal trajectory path tasking for an unmanned aerial vehicle (UAV) for collection of data on one or more collection targets by a sensor on the UAV.

Abstract: The invention relates to route planning and optimization. In some embodiments, the invention includes obtaining vertex data describing target attributes, including at least one target location, and process dynamics data describing system constraints of a process; defining vertex functionals, describing attribute requirements, and edge functionals, which use the system constraints to determine cost requirements, for cost objectives; and using the data, the functionals, and the cost objectives to construct a T-graph that includes a label space representing the target attributes. Next, the T-graph is solved to construct a label space trajectory by determining waypoints such that the process (1) travels through all target locations and (2) satisfies the attribute requirements, dynamically calculating travel cost by applying the cost requirements to each intervening edge, and minimizing the travel cost according to the cost objectives.

Abstract: A method and system for generating an optimal trajectory path tasking for an unmanned aerial vehicle (UAV) for collection of data on one or more collection targets by a sensor on the UAV.

Abstract: The disclosure provides a closed-loop controller for a controlled system comprising a comparison element generating an error e, a compensator generating a control uN value based on the error e, and a control allocator determining a manipulated parameter uM value based on the control uN. The control allocator typically utilizes a control effectiveness function and determines uM value by selecting one or more specific system x0 signals from the system state xi or system input yj values or system parameters values pk reported, defining a plurality of distributed xD around each specific system x0 signal, and minimizing an error function E(zi), where the error function E(zi) is based on errors which arise from use of the plurality of distributed xD in the control effectiveness function rather than one or more specific system x0 signals.

Abstract: The disclosure provides a robotic arm controller which determines a control parameter for at least one actuator comprising the robotic arm using a control equation having the general form Ax=b, where A is a transformation matrix A based on the geometry and Jacobian of the robotic arm, x is the control parameter x such as a torque vector at a specific joint, and b is the end effector parameter b which specifies a desired corrective state of the end effector. The methodology, by way of constructing and solving an unscented optimization problem, provides a solution to the Ax=b problem by perturbing at least one joint angle appearing in the Jacobian to generate a plurality of distributed joint angles, determining a control parameter x which minimizes an error function.

Abstract: The disclosure provides a method and apparatus for determination of a control policy for a rigid body system, where the rigid body system comprises a sensor and a plurality of actuators designed to maneuver the rigid body system and orient the sensor toward a plurality of defined vertices, such as geographic points on the earth surface. A processor receives input data describing an initial state of the rigid body system and further receives a plurality of candidate vertices for potential targeting by the sensor. The processor additionally receives an intrinsic value for each vertex, reflecting the relative desirability of respective vertices in the plurality of vertices. The processor determines an appropriate control policy based on the vertices, the intrinsic values, and the rigid body system through a formulation of the determination process as an optimization problem which actively considers various constraints during the optimization, such as maneuvering and observation constraints.

Abstract: Unscented optimal control combines the unscented transform with optimal control to produce a new approach to directly manage navigational and other uncertainties in an open-loop framework. The sigma points of the unscented transform are treated as controllable particles that are all driven by the same open-loop controller. A system-of-systems dynamical model is constructed where each system is a copy of the other. Pseudo-spectral optimal control techniques may be applied to this system-of-systems to produce a computationally viable approach for solving the large-scale optimal control problem.