A method for selecting an altitude changing phase route for an aircraft is presented. The method comprises receiving sequences of multivariate flight data from at least one prior flight, receiving a set of flight parameters for the aircraft including at least a total altitude change and a takeoff weight, and receiving a set of candidate altitude changing phase routes having candidate step profiles. For each candidate altitude changing phase route, a sequence of fuel burn quantities is predicted for the respective candidate step profile based on at least the sequences of multivariate flight data and the set of flight parameters. The fuel burn quantities are summed over the candidate altitude changing phase route to obtain an estimated fuel burn. A preferred candidate altitude changing phase route having a lowest estimated fuel burn is indicated.

An aircraft control system includes a longitudinal control module, an engine torque control module, and an actuator control system. The longitudinal control module is configured to generate a desired torque value and a desired elevator position value for an aircraft based on a desired airspeed value, a desired altitude value, an actual airspeed value, and an actual altitude value. The engine torque control module is configured to generate a desired power lever position value based on the desired torque value and a measured engine torque value that indicates a measured engine torque in the aircraft. The actuator control system is configured to generate a power lever position command and an elevator position command for the aircraft based on the desired power lever position value and the desired elevator position value.

A method, apparatus and system for controlling an attitude of a spacecraft, the spacecraft including an attitude control system operatively associated with a ground-based spacecraft control system. According to an exemplary embodiment, the spacecraft attitude control system uses a B-spline interpolator for commanding the spacecraft. The methods and systems disclosed herein can be implemented in, for example, executable machine code and/or integrated circuit hardware.

An aerial vehicle equipped with a first range sensor oriented to capture range data above the aerial vehicle and a second range sensor oriented to capture range data below the aerial vehicle is programmed with global map of an indoor space, including an upper global map representing distance data for upper surfaces of the indoor space and a lower global map representing distance data for lower surfaces of the indoor space. An offset to an altitude is calculated based on a comparison between range data captured by the first range sensor and the upper global map, and range data captured by the second range sensor and the lower global map. Additionally, global maps may be updated based on returns captured by the range sensors, where such data indicates the presence of a previously undetected object.

An aerial vehicle equipped with a first range sensor oriented to capture range data above the aerial vehicle and a second range sensor oriented to capture range data below the aerial vehicle is programmed with global map of an indoor space, including an upper global map representing distance data for upper surfaces of the indoor space and a lower global map representing distance data for lower surfaces of the indoor space. An offset to an altitude is calculated based on a comparison between range data captured by the first range sensor and the upper global map, and range data captured by the second range sensor and the lower global map. Additionally, global maps may be updated based on returns captured by the range sensors, where such data indicates the presence of a previously undetected object.

A system for UAV collaborative coverage path planning based on a Q-learning adaptive ant colony algorithm including a memory, an image collection device, and a plurality of UAVs loaded with a path planning module configured to: construct a 3D model in a collaborative coverage environment, by performing a cell division on the 3D model based on a scanning range of an airborne radar of each UAV, obtain one or more sub-regions; by establishing constraints of the UAV and the environment based on the determined 3D model of the region to be searched, establish a problem total cost model; perform a plurality of rounds of iterations, calculate a reward value of each ant colony and determine whether a maximum iteration count is reached, if the maximum iteration count is reached, enter a new round of iteration, otherwise, output a path corresponding to a current round of iteration as a final path.

A system for UAV collaborative coverage path planning based on a Q-learning adaptive ant colony algorithm including a memory, an image collection device, and a plurality of UAVs loaded with a path planning module configured to: construct a 3D model in a collaborative coverage environment, by performing a cell division on the 3D model based on a scanning range of an airborne radar of each UAV, obtain one or more sub-regions; by establishing constraints of the UAV and the environment based on the determined 3D model of the region to be searched, establish a problem total cost model; perform a plurality of rounds of iterations, calculate a reward value of each ant colony and determine whether a maximum iteration count is reached, if the maximum iteration count is reached, enter a new round of iteration, otherwise, output a path corresponding to a current round of iteration as a final path.

An unmanned ocean vehicle apparatus having a hull, solar array, hydrofoil, and electric thruster is operable on a surface of a body of water to travel from one point to another with long range capabilities. The unmanned ocean vehicle can further be placed in a more compact configuration, incorporating a mechanism to alternate between operational mode and collapsed storage mode to facilitate physical storage or shipment of the vehicle. Methods are described for optimizing travel and determining favorable movements of the unmanned ocean vehicle by calculating current and predicted conditions at certain waypoints using a combination of a sensor, a computer, and a control system. The physical support of hydrofoils, a drag reducing system, capsize-prevention technique, and a three-dimensional arrangement of solar panels provides efficient power management, optimal route strategy, and sustainability of the vehicle's high speeds in wildly varying ocean conditions.

An unmanned ocean vehicle apparatus having a hull, solar array, hydrofoil, and electric thruster is operable on a surface of a body of water to travel from one point to another with long range capabilities. The unmanned ocean vehicle can further be placed in a more compact configuration, incorporating a mechanism to alternate between operational mode and collapsed storage mode to facilitate physical storage or shipment of the vehicle. Methods are described for optimizing travel and determining favorable movements of the unmanned ocean vehicle by calculating current and predicted conditions at certain waypoints using a combination of a sensor, a computer, and a control system. The physical support of hydrofoils, a drag reducing system, capsize-prevention technique, and a three-dimensional arrangement of solar panels provides efficient power management, optimal route strategy, and sustainability of the vehicle's high speeds in wildly varying ocean conditions.

A vegetation management system includes a computing system including a processor having an associated memory that is configured for implementing a vegetative modeler including an image analyzer and at least one 3-dimensional (3D) vegetation growth model. The vegetative modeler is for analyzing images of vegetation that is growing around electrical power lines of an electric utility including identifying locations of the vegetation relative to locations of the electrical power lines and to identify specific types of the vegetation. The 3D vegetation growth model utilizes at least the locations of the vegetation relative to the locations of the electrical power lines and the specific types of the vegetation to generate a predicted timing of encroachment of the electrical power lines by the vegetation to identify future encroachment areas. A scheduler is for scheduling vegetative maintenance of the vegetation for the future encroachment areas.