This paper discusses the development and testing of a unique testbed consisting of a fleet of eight autonomous unmanned aerial vehicles (UAVs) that was designed as a platform for evaluating autonomous coordination and control algorithms. Future UAV teams will have to autonomously demonstrate cooperative behaviors in dynamic and uncertain environments, and this testbed can be used to compare various control approaches to accomplish these coordinated missions. A hierarchical configuration of task assignment, trajectory design, and low-level, waypoint following, are used in a receding horizon framework to control the UAV team. Numerous trajectory optimization and team coordination algorithms have recently been developed to execute these UAV missions. This paper highlights several of these algorithms and presents typical results for representative experiments. These demonstrations of the high-level planning algorithms on scaled vehicles operating in uncertain and dynamic environments represent key steps towards transitioning them to future UAV missions.
This paper introduces two unique testbeds that have recently been developed to demonstrate the cooperative control of teams of UAVs. The first testbed uses eight rovers and four blimps operated indoors to emulate a team of heterogeneous vehicles performing a combined reconnaissance and strike mission. The second testbed uses eight small aircraft that are flown autonomously using a commercially available autopilot. This combination of testbeds provides platforms for both advanced research and realistic demonstrations. Numerous trajectory optimization and team coordination algorithms have recently been developed to execute these UAV missions. This paper highlights several of these coordination and control algorithms and presents typical results for representative experiments. These demonstrations of the high-level planning algorithms on scaled vehicles operating in uncertain and dynamic environments represent key steps towards transitioning them to future UAV missions.
This paper introduces two unique testbeds that have recently been developed at MIT to demonstrate coordination and control algorithms for teams of autonomous UAVs. The first testbed uses eight rovers and four blimps operated indoors to emulate a heterogeneous fleet of vehicles that could be used to perform a search and rescue mission. The second testbed uses eight small aircraft that are flown autonomously using a commercially available autopilot. This combination of testbeds provides platforms for both advanced research and very realistic demonstrations. Numerous trajectory optimization and team coordination algorithms have recently been developed to perform these missions. This paper highlights several algorithmic pieces of the system and typical results are presented for representative experiments in each of the testbeds. Copyright c 2004 IFAC
Launched in December 2014 atop a Delta IV Heavy from the Kennedy Space Center, the Orion vehicle's Exploration Flight Test-1 (EFT-1) successfully completed the objective to stress the system by placing the un-crewed vehicle on a high-energy parabolic trajectory replicating conditions similar to those that would be experienced when returning from an asteroid or a lunar mission. Unique challenges associated with designing the navigation system for EFT-1 are presented with an emphasis on how redundancy and robustness influenced the architecture. Two Inertial Measurement Units (IMUs), one GPS receiver and three barometric altimeters (BALTs) comprise the navigation sensor suite. The sensor data is multiplexed using conventional integration techniques and the state estimate is refined by the GPS pseudorange and deltarange measurements in an Extended Kalman Filter (EKF) that employs UDU factorization. The performance of the navigation system during flight is presented to substantiate the design.
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