Autonomy & Flight Control

One of the strongest areas of activity at the Aurora RDC is in vehicle autonomy and control. Aurora is working on autonomy for both individual unmanned vehicles and coordination of multiple unmanned systems (surface, underwater, ground, and air).
In addition to autonomy, Aurora’s RDC has both the expertise and the simulation capability to conduct sophisticated analysis of air vehicle dynamics and control, as well as research into vision-based guidance and other systems for urban and GPS-denied flight.

MAV Guidance

Aurora is conducting groundbreaking ‘bio inspired’ research into guidance sensors and control systems that will allow Micro Air Vehicles (MAVs) to fly in dense urban environments without the aid of GPS. The technologies are being developed by studying the vision systems of insects and the use of sonar by bats.
This will allow the MAVs to avoid large obstacles without sizeable image-processing computational requirements and avoid small obstacles by integrating echolocation sensors.

Multi-Vehicle Cooperative Control for Air and Sea Vehicles in Littoral Operations (UAV/USV)

Aurora is applying algorithms for cooperative tasking of multiple unmanned vehicles, which have been significantly matured in UAVs, to a variety of USV littoral search, inspection, and force protection missions. Because of our mature starting point, the focus is to address real-world issues such as distributed implementation over intermittent communication networks; dynamic, stochastic environments; and management of vehicle loss and other multi-vehicle health management issues. Aurora's existing collaboration with MIT researchers is being expanded toward transitioning technologies from the UAV realm to the USV realm.
Cooperative USV/UAV/UUV teams are being considered, in which some vehicles play the role of communication relays.
Existing multi-vehicle real-time simulations with communication emulation are already available for these studies, and the MIT-developed RDTA algorithms can be used to optimize planning for flexible, diverse unmanned teams with diverse sensor sets.
Extensions to incorporate recent results in multi-vehicle health management and human interfaces to reduce operator workload are also being incorporated. The effort will culminate with in-water testing of multiple USVs, together with real or simulated UAVs in a cooperative mission.

On-board Planning System for UAVs Supporting Expeditionary Reconnaissance
and Surveillance (OPS-USERS)

Aurora and MIT are working on a program for ONR in which we are developing the architecture, core algorithms, and human interface concepts for a multi-platform, distributed UAV-USV team that responds to requests from field operators for intelligence support. Aurora is building on existing robust distributed tasking algorithms that have been demonstrated to work in intermittent communications environments, tailoring them to address multiple-operator issues, rules of engagement constraints, and the necessity to ensure performance of time-critical tasks.
Capabilities are being incorporated into a collaborative decision-making process flow that specifically accounts for human supervisory control issues, including interfaces, cognitive roles, and situational awareness. This system will be implemented in an onboard planning module, already in development, that can be incorporated into low cost UAVs, giving them higher levels of autonomy and making it possible for them to coordinate their activities as a team over a real-world communication network.

Flare Planning

Aurora is working with NASA to develop and demonstrate a method for generating robust autonomous flare maneuvers for manned and unmanned vehicles. The goal of a flare maneuver is to safely transition an aircraft from final approach to touchdown, decelerating the vehicle and setting up a safe landing attitude.
During this transition, a complex series of dynamic events can occur, and the pilot or autopilot must address uncertainty in aerodynamics, disturbances such as cross-winds and, in severe circumstances, aircraft impairment. Aurora’s Flare Planner takes advantage of recent advances in control theory which allow for fast ‘on-the-fly’ determination of appropriate control inputs for complex dynamic situations – these methods efficiently generate flight paths that simultaneously satisfy vehicle performance limits, constraints, and touchdown criteria.

Distributed Sensor Fusion

In the expanding arena of Net-Centric Warfare (NCW) there is still a capability gap with tactical UAV employment: incorporation of local UAV data into the intelligence datastream is still limited, and more importantly, coordination of data gathering platforms (especially UAVs) is not automated or optimized. As the number of UAVs in the battlespace increases, the potential for fast and accurate localization, identification, designation, and prosecution of time-sensitive targets will only be realized if mechanisms for coordinating assets are developed. This includes coordination of multiple local, mixed assets, as well as combined local and stand-off assets. The focus here is on managing UAV resources by appropriately tasking them to perform ‘coincident collection’ of ISR data, either with other UAVs – placing multiple UAV sensors on a target to mitigate the sensor limitations of single vehicles – or with stand-off assets – providing low-altitude data collection on targets identified by stand-off sensors (GMTI, SAR) and thus increase the fidelity of identification or provide target designation.
Under a Phase II SBIR sponsored by the Air Force Research Lab, Aurora is exploring advanced algorithms to optimize the autonomous management of multiple UAVs to both make UAV data collection more relevant to overall battlefield situational awareness, as well as optimizing the coordination of multiple UAVs and their sensors to ensure effective, timely, and persistent ISR information.