Abstract: An autonomous vehicle comprises one or more sensors configured to obtain data regarding conditions which affect movement of the autonomous vehicle; a speed planner coupled to the one or more sensors and configured to calculate a desired speed based, at least in part, on the data obtained from the one or more sensors; and one or more actuators responsive to signals from the speed planner and configured to adjust the speed of the autonomous vehicle based on the desired speed calculated by the speed planner.

Abstract: An autonomous vehicle comprises one or more sensors configured to obtain data regarding conditions which affect movement of the autonomous vehicle; a speed planner coupled to the one or more sensors and configured to calculate a desired speed based, at least in part, on the data obtained from the one or more sensors; and one or more actuators responsive to signals from the speed planner and configured to adjust the speed of the autonomous vehicle based on the desired speed calculated by the speed planner.

Abstract: Method and systems of traversing through a domain is provided. One method comprises getting a set of widely spaced waypoints, assigning the next waypoint to be the goal, then using a Laplacian path planner to construct a desired finely detailed path towards the goal, through the domain that avoids boundaries and objects in the domain. Assigning a potential value of v(r)=0 for r on boundaries and obstacles. Assigning a potential value of v(r)=?1 for r on a goal region, wherein the goal is a point on a planned path. Obtaining a numerical solution to the desired path with a Laplace's equation by gridding up the domain with a multi-sized cell grid, wherein the cells near an object are denser then the cells away from the objects. Iteratively setting a potential at each interior point equal to the average of its nearest neighbors and following the numerical solution provided by the Laplace's equation to the goal region.

Abstract: The accuracy of flight management systems, based on mathematical prediction models calculated from aircraft specific data, are improved by adding engine sensor data to the calculations, checking sensor and pilot entered data, and comparing data measured from redundant sensors. A thrust estimate, calculated from available engine sensors, is added to the thrust-minus-drag aircraft model allowing prediction parameters to be accurately calculated even in a cruise condition. Sensor data and pilot entered data used in calculating predication parameters are checked to improve accuracy. Redundant sensor data is compared to determine the level of agreement. Redundant sensor data is also compared with a valid data range to find the sensor with the most accurate data.

Abstract: A flight plan is modeled by a trajectory with a set of segments having finite lateral and altitude extents between nominal way points. A terrain database stores hierarchical patches with maximum and minimum altitudes and pointers or altitudes for subpatches. Linear-programming inequality constraints match segments with patches. A search finds any terrain locations that impinge upon the trajectory tube, indicating an error condition for the plan. Moving hazards are modeled with segmented trajectories. Conflict with a moving hazard occurs when moving bubbles within both of the trajectories overlap in both space and time.