The Boeing Company

Massachusetts Institute of Technology

Jonathan Patrick How

John Lyle Vian

Matthew Aaron Vavrina

Brett M Bethke

Joshua Redding

James J Troy

Bernard J Michini

Thomas Edward Pilutti

Tuna Toksoz

Charles A Erignac

Emad William Saad

Ali Reza Mansouri

Stefan Richard Bieniawski

Gregory John Clark

Paul Murray

Khaled Abdel-Motagaly

Paul E R Pigg

Ronald Carl Provine

Matthew A Michini

Mario J Valenti

Georges Aoude

Vishnu Rajeswar Desaraju

Matthew Michini

Heba Abdullatif Kurdi

Zachary Dydek

A mechanism for dynamically allocating tasks among multiple UAVs operating autonomously during a mission is discussed. Task assignment is adjusted by each UAV dynamically during the mission based on criteria related to the individual UAV's operational status and/or mission parameters. Task allocation is determined independently without group communication between the UAVs actively taking part in the mission and without direct communication to a ground-based controller. A communication UAV provides a shared memory space that may be utilized by each UAV in determining its own task allocation.

Dynamic task allocation in an autonomous multi-UAV mission

A system and method for predicting whether a vehicle will come to a stop at an intersection is provided. Generally, the system contains a memory; and a processor configured by the memory to perform the steps of: generating a prediction of whether the vehicle will or will not stop at the intersection before a first time based on vehicle data measured during a first time window; and at a second time, the second time being before the first time and approximately equal to a time at which the time window ends, providing an indication that the vehicle will not stop at the intersection before the first time based upon the prediction, wherein generating the prediction comprises using a classification model, the classification model configured to indicate whether the vehicle will or will not stop at the intersection before the first time based on a plurality of input parameters, and wherein the plurality of input parameters are selected from the group consisting of speed, acceleration, and distance to the intersection.

System and method for providing driver behavior classification at intersections and validation on large naturalistic data sets

A system for monitoring a vehicle having systems, components, and sensors has a nominal controller and an adaptive controller. A vehicle health function module is coupled to an output of the adaptive controller and generates a vehicle health output signal indicating a prognosis of at least one of the systems or components degradation of the vehicle.

Vehicle capability monitoring and adaptation system and method therefor

A vehicle base station comprises a platform on which a vehicle may be positioned, a first battery bay located on a first side of the platform, a battery replacement assembly to remove a battery from the vehicle and to replace the battery with a new battery, and a power source adapted to provide power to the vehicle while the vehicle is positioned on the platform.

Vehicle base station

Systems and methods for development testing of vehicles and components are disclosed. In one embodiment, a system includes a position reference system and a command and control architecture. The position reference system is configured to repetitively measure one or more position and motion characteristics of one or more vehicles operating within a control volume. The command and control architecture is configured to receive the repetitively measured characteristics from the position reference system, and to determine corresponding control signals based thereon. The control signals are then transmitted to the one or more vehicles to control at least one of position, movement, and stabilization of the one or more vehicles in a closed-loop feedback manner. The system may further include a health monitoring component configured to monitor health conditions of the one or more vehicles, the control signals being determined at least in part on the health conditions.

Autonomous vehicle rapid development testbed systems and methods

Growing community of inventors

Arlington, MA, United States of America

Jonathan Patrick How