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Implementation and flight testing of an Autonomous Formation Flying
System (AFFS)
James C. Neidhoefer, Jason C. Ryan Aerotonomy, Inc. Lithia Springs, GA 30122
Eric S. Johnson
Georgia Institute of Technology, Atlanta, GA, 30332-0844
A significant obstacle to the implementation of autonomously-controlled aircraft into the national airspace system is the tendency of autonomous systems to operate in non-deterministic manners. While autonomous aircraft may have the capability to safely maneuver in shared airspace, they often do not operate under the same flight guidelines as a trained human pilot, leading to potential misinterpretation of actions and flight paths. The Autonomous Formation Flying System (AFFS) provides both autonomous control for formation flight as well as a deterministic solution for changes in trajectory. The AFFS gives rotorcraft the capability to autonomously avoid multiple static or moving obstacles, including pop-up threats, while flying in formation. It functions in dynamic three-dimensional situations with both small and large heterogeneous formations, facilitating safe and efficient entries into and exits from a formation while also allowing seamless, real-time changes in the formation structure and following the standard “rules of the road” adhered to by trained human pilots. The testing of this innovative system included a series of manned and unmanned multi-ship simulations as well as flight-test experiments in which Georgia Tech’s GTMax rotorcraft UAV was flown in formation with a manned high-fidelity UH-60 simulation. Both the simulations and flight-tests effectively demonstrated the autonomous formation flying and collision avoidance capabilities of the AFFS.
I. Introduction
This paper presents work towards the development of an autonomous formation-flying control system that not only controls the aircraft, but does so in a manner such that a human pilot in shared airspace may trust that the actions of the autonomous vehicles will be similar to those of a trained human pilot. The reliability of a deterministic system such as this represents an important step in the integration of autonomous vehicles into both the National Airspace System and into military tactical operations around the globe. The following issues, if not properly addressed, have been determined to be significant potential barriers to fielding a new autonomous formation flying and collision avoidance system on manned aircraft:
1) Pilot acceptance: Pilot interviews indicate that pilot acceptance hinges on the system’s behavior being intuitive and deterministic; the pilot must know what to expect from his own as well as other vehicles in a given situation. This constraint significantly affected early AFFS design choices since typical implementations of many standard trajectory optimization and collision avoidance techniques (such as potential field and optimal control-based methods) can often exhibit highly non-deterministic and, from a pilot’s perspective, seemingly chaotic behaviors. One of the primary strengths of the AFFS developed in this project is its ability to react to situations deterministically and in a manner similar to the way a trained human pilot would react.
2) Certification for use in the National Airspace System (NAS) outside of special use airspace: Only a few UAV operations in civil airspace have been allowed in recent years, with each mission evaluated and accepted on a case-by-case basis and tightly controlled by the FAA. Several significant efforts such as:
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• the DoD/FAA/Northrop Grumman collaboration on the Global Hawk’s FAA National Certificate of Authorization (CoA) and recent military airworthiness certification [3]
have been directed towards institutionalizing the safe and legal operation of unmanned and autonomous air vehicles in the NAS. One thing made clear by these efforts is that the FAA will expect unmanned and autonomous manned air vehicles operating in the NAS to follow the same stringent levels of rules and regulations that are followed by manned piloted aircraft. Based on this information, the AFFS was designed from the start to comply with the rules and regulations followed by manned aircraft (even when operating autonomously), guaranteeing behaviors that are predictable and consonant with NAS operating procedures in order to facilitate future FAA certification issues.
II. The AFFS Guidance System
The AFFS Guidance System consists of the Baseline Trajectory Generator (BTG) and the Proximity Limit Violation Protection System (PLVPS). The inputs to the BTG can be either waypoints and the type of trajectory desired at each waypoint, such as stop, e-turn, cut, hover, etc., or, when in a formation flying mode, the location and orientation of the formation axis (usually associated with a lead aircraft’s state in leader/follower formations) and the desired location within the formation (usually defined as an offset vector from the origin of the formation axis). These waypoints or the desired formation state come to the BTG from the Formation Executive. Based on these inputs, the BTG generates a smooth, realizable trajectory that tracks the waypoints or the desired formation state. The trajectories are delivered in the form of commanded position, velocity, and acceleration, which are fed to the outer loop reference model in the Neural Adaptive Controller (NAC), and commanded attitude and angular velocity, which are fed to the inner loop reference model in the NAC.
At the heart of the AFFS is the Proximity Limit Violation Protection System (PLVPS), used to predict and prevent proximity limit violations (i.e. one aircraft getting too close to another). This system works by automatically modifying control commands to follow trajectories that are both safe and efficient. The PLVPS consists of:
1) the Proximity Limit Violation Predictor, which uses an estimate of relative proximity dynamics to predict limit violations before they occur.
2) the Safe Response Generator, which uses an adaptive tangent-ellipsoid approach to generate smooth commanded trajectories that continually track the desired formation state while safely avoiding collisions.
3) the Trajectory Correction Generator, which uses a guaranteed stable functional estimate of local plant dynamics, adapted in real-time with update laws derived using Lyapunov synthesis, to ensure that the resulting commanded trajectories are both realizable and efficient. This allows the AFFS to takefull advantage of the vehicle’s available operational envelope even in cases where limiting conditions such as actuator saturation are present.
While the PLVPS is new and innovative, it is based on a significant amount of prior research [4-7] and several actual flight test demonstrations [8, 9] of the underlying technology. When the PLVPS is used in conjunction with the Baseline Trajectory Generator (BTG), the Formation Executive (FE), and a rotorcraft tracking controller, the resulting hierarchical AFFS approaches the formation flying and collision avoidance problems in a manner very similar to a trained human pilot.
3 B A B A B Corrected “SAFE” Trajectory A Baseline Trajectory Baseline Trajectory Baseline Trajectory Case 3 Case 2 Case 1 1 1 1 1 2 2 2 B B A A B B A A B B Corrected “SAFE” Trajectory A Baseline Trajectory Baseline Trajectory Baseline Trajectory Baseline Trajectory Baseline Trajectory Baseline Trajectory Case 3 Case 2 Case 1 1 1 1 1 2 2 2
Figure 1. The PLVPS provides corrections to the baseline trajectory generated by the BTG
If the aircraft is operating in a safe region not too close to other aircraft, such as Case 1 in Figure 1, the output of the AFFS Guidance System is equivalent to the baseline trajectory provided by the unmodified BTG. However, if the aircraft moves too close to another aircraft, either due to external disturbances or because the initial trajectory coming from the BTG is commanding the aircraft to move too close to one of its neighbors, the PLVPS will predict a proximity limit violation and automatically generate trajectory modifications. The new trajectory will continue to guide the aircraft towards its goal (either the desired location in the formation axis, which could be moving, or the next waypoint) while autonomously maneuvering to avoid the limit violation, i.e. the other aircraft. In Case 2, Aircraft 1 starts at Waypoint A, the Formation Executive has specified a desired location at Waypoint B, and the BTG has specified a straight line desired trajectory between A and B. However, Aircraft 2 is hovering directly on the commanded trajectory. In Case 3 in Figure 1, Aircraft 1 has begun to move along the desired trajectory towards Waypoint B. In doing so, it approaches Aircraft 2 until the PLVPS predicts a proximity limit violation based on the relative position and velocity of the two aircraft. When the limit violation is predicted, the PLVPS generates smooth, realizable trajectory corrections that allow Aircraft 1 to maneuver around Aircraft 2, avoiding the limit violation, and continue on to its goal at Waypoint B. (It should be noted that if Aircraft 2 were utilizing an AFFS, it would also move to avoid the other approaching aircraft.)
Thus, when the PLVPS is combined with the BTG, desired locations within a formation can be freely commanded by the Formation Executive without having to explicitly account for the locations of other aircraft in the formation. In effect, the Formation Executive just has to specify a goal for the
4 To enter a pre-existing formation, the Formation Executive simply commands the desired location in the formation, and all of the aircraft in the formation will automatically settle into their correct positions. The AFFS Guidance System also facilitates flying in different types of formations, autonomously maneuvering within the formation, and leaving the formation in both planned and emergency situations. Finally, given appropriately sensed data, the AFFS could easily be enhanced for use in Nap of the Earth (NOE) flying.
III. The Proximity Limit Violation Predictor
The Proximity Limit Violation Predictor is used to predict proximity limit violations before they occur. The system currently uses the relative position and velocity between the commanded state of its aircraft, which we denote as Aircraft 1, and an encroaching aircraft (Aircraft 2) or obstacle, which we denote by the subscript 2. Future versions of the system will also make use of the relative acceleration. This system assumes the use of the adaptive Trajectory Correction Generator, which guarantees that the commanded trajectories output by the AFFS are realizable, thus ensuring that tracking error remains small.
Figure 2 shows data from a three-aircraft, line-abreast formation that illustrates the use of the Proximity Limit Violation Predictor in a case with multiple moving obstacles. In Figure 2 Part A, the left-most aircraft (the GTMax) is commanded to shift position to the right side of the formation. In Part B, a proximity violation is predicted and the AFFS enables the GTMax to autonomously maneuver to avoid the first obstacle (the nearest neighboring aircraft in the formation). While maneuvering to avoid the first aircraft, a proximity violation is predicted with the second aircraft (Part C). The GTMax then maneuvers to avoid the second aircraft (Part D) and smoothly converges to its new desired location within the formation (Part E).
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A B
C
E
D
Figure 2. Proximity limit violation prediction with multiple moving obstacles
IV. The Safe Response Generator
The commanded trajectory output by the Baseline Trajectory Generator (BTG) is called the “Un-Modified Trajectory” (or UMT). When a proximity limit violation is predicted, the Safe Response Generator (SRG) uses a three dimensional, multi-modal, adaptive geometry-based approach to generate a smooth “Modified Trajectory” (or MT) that continues to track the Un-Modified Trajectory while safely avoiding collisions. An advantage of the SRG is that by varying a small number of SRG parameters, a wide variety of highly deterministic response behaviors can be generated.
The SRG was intended from the start to be used with two additional AFFS subsystems: the Trajectory Correction Generator, which ensures that the resulting AFFS commanded trajectories are
6 way a trained human pilot would react.
The SRG has three basic modes: the approach mode, the limit avoidance and convergence mode, and the failsafe mode. Each of these modes has different capabilities, and by changing a small number of parameters, the system can exhibit a wide variety of complex yet highly deterministic behaviors. At the heart of each of these modes is a governing set of well-behaved three-dimensional vector equations. At each time step, these equations are solved to calculate the desired position of the aircraft at the following time step.
UMT(t-1) UMT(t)
UMT(t-1) UMT(t)
Figure 3. Geometry and vector definition of the SRG Approach Mode
V. The Trajectory Correction Generator
The Trajectory Correction Generator (TCG), which uses a guaranteed stable functional estimate of local plant dynamics, adapted in real-time with update laws derived using Lyapunov synthesis [6, 10], ensures that the resulting commanded trajectories are both realizable and efficient. This allows the AFFS to take full advantage of the vehicle’s available operational envelope even in cases where limiting conditions such as actuator saturation are present.
As shown in Figure 4, most real closed loop systems suffer from lag. If a closed loop system is commanded to be at a specific location one time step in the future, it will usually (assuming it has good tracking performance) make it almost but not quite to the commanded location (as in Figure 4). However, if we had a function (like that shown at the top of Figure 4) with inputs of commanded position, velocity, and acceleration, and outputs that closely match the actual position, velocity, and acceleration, then this function can be used to adapt a set of pseudo-commands in such a way that when the pseudo-commands
7 are applied as inputs to the closed loop system, the actual output of the closed loop system matches the original desired output (as shown in Figure 5).
Model
Pc(t) Vc(t) ac(t) P(t+dt) V(t+dt) a(t+dt) P(t-dt) P(t) Pc(t+dt) P(t+dt)Predicted ACTUAL position @ t+dt
Close, but usually ≠Pc(t+dt) Commanded position @ t+dt
ACTUAL position @ t ACTUAL position
@ previous time
step ACTUAL path will differ from
desired path because of system dynamics Commanded path
At time = t, need to command an acceleration to get to desired
position @ next time step = Predicted position error @ t+dt
Model
Pc(t) Vc(t) ac(t) P(t+dt) V(t+dt) a(t+dt)Model
Pc(t) Vc(t) ac(t) P(t+dt) V(t+dt) a(t+dt) P(t-dt) P(t) Pc(t+dt) P(t+dt)Predicted ACTUAL position @ t+dt
Close, but usually ≠Pc(t+dt) Commanded position @ t+dt
ACTUAL position @ t ACTUAL position
@ previous time
step ACTUAL path will differ from
desired path because of system dynamics Commanded path
At time = t, need to command an acceleration to get to desired
position @ next time step = Predicted position error @ t+dt
Figure 4. Most real non-predictive systems have lag
P(t-dt) P(t) Pc, orig(t+dt) Porig(t+dt) ORIGINAL Predicted ACTUAL position @ t+dt ORIGINAL Commanded position @ t+dt ACTUAL position @ t ACTUAL position @
previous time step
Original Commanded path
Final ACTUAL path Pseudo
Command FINAL Commanded position adjusted for system dynamics
Final Commanded path
Achieve
P(t+dt) = P
c, orig(t+dt)
P(t-dt) P(t) Pc, orig(t+dt) Porig(t+dt) ORIGINAL Predicted ACTUAL position @ t+dt ORIGINAL Commanded position @ t+dt ACTUAL position @ t ACTUAL position @previous time step
Original Commanded path
Final ACTUAL path Pseudo
Command FINAL Commanded position adjusted for system dynamics
Final Commanded path
Achieve
P(t+dt) = P
c, orig(t+dt)
Figure 5. The adaptive estimate of local plant dynamics can be used to predict future performance and compensate for lag
VI. Flight Testing
The flight test experiments were designed to demonstrate the feasibility of the formation flying and collision avoidance methodology and to evaluate the overall system performance in a real world environment. Specific goals of the flight test demonstrations were to:
8 to real-time changes in the formation structure or the aircraft’s position assignment within the formation.
3. Demonstrate that the PLVPS can provide safe, efficient trajectory corrections to baseline trajectories that would otherwise result in collision, even when the obstacle is in motion.
4. Demonstrate operation of the AFFS system with heterogeneous rotorcraft formations.
5. Demonstrate the robustness of the communication system and protocol in a real-world environment.
The primary objective of Test Plan #1 was to demonstrate the formation flying capabilities of the AFFS. For this test, the formation structure was set up as a line-abreast formation with 20 ft separation, and a trajectory-fixed formation reference axis system was used. Upon engagement of the AFFS, the GTMax UAV moved into position 20 ft to the left side of the hovering simulated leader aircraft. The primary objective of test plan #2 was to demonstrate the collision avoidance capabilities of the AFFS. In this experiment, a scenario was created in which the commanded baseline trajectory for the follower would cause a collision with the simulated leader aircraft. In this case, the PLVPS had to predict the collision, and generate a safe, efficient modified trajectory to avoid the collision and still achieve the waypoint goal. The primary objective of test plan #3 was to demonstrate simultaneous formation flying and collision avoidance capability. This test was similar to test plan #2 in that a scenario was created in which the commanded baseline (unmodified) trajectory for the follower would cause a collision with the simulated leader aircraft. However, in this experiment, the simulated leader was commanded to fly at a positive forward speed, and the actual GTMax UAV was required to autonomously change position relative to the leader, switching from one side to the other side of the leader, while maintaining the formation. The primary objective of test plan #4 was to demonstrate that even during collision avoidance, the Safe Response Generator is aware of the desired formation state and reacts accordingly. In this experiment, the formation structure was set up as a line-abreast formation with 75 ft separation, and a trajectory-fixed formation reference axis system was used. Upon engagement of the AFFS, the GTMax UAV moved into position 75 ft to the side of the hovering simulated leader aircraft. The primary objective of test plan #5 was to demonstrate the AFFS-equipped GTMax UAV flying in formation with a piloted UH-60 leader aircraft. While the first four flight test plans used a simulated GTMax flying autonomously as the formation leader, this experiment was conducted using the piloted UH-60 RIPTIDE simulation as the formation leader. This computer was connected to GTMax ground station computer #1 through an ethernet LAN, and communicated with the GTMax through the spread spectrum data link.
All objectives for each of these test plans were completed successfully.
VII. Conclusion
The AFFS system has demonstrated the capability to function in dynamic situations, allowing for obstacle-avoidance and formation flight of a group of rotorcraft in a completely deterministic manner. The AFFS incorporates these deterministic flight path solutions with an obstacle avoidance package, formation execution, and an innovative trajectory correction package to remove the errors induced by system lag. Utilizing all of these features, The AFFS delivers a reliably repeatable and thus predictable level of performance for unmanned systems, an important step forward as solutions are sought to merge the currently segregated realms of manned and unmanned flight systems. While the system still requires a great deal of refinement, the research performed here lays the groundwork for the implementation of such autonomous systems in the future. Methods such as the ones used here, which can provide deterministic solutions for not only one, but multiple aircraft, are potentially the form of control system required to finally bridge the trust gap between manned and unmanned systems.
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VIII. Acknowledgements
This work was performed under contract number #W911NF-05-C-0092. Aerotonomy, Inc. would like to acknowledge Chris Gibson and Paul Mays for their contributions to this research.
IX. References
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[2] The Access 5 Program, http://www.access5.aero/.
[3] HALE ROA in the NAS, NASA Dryden Research Center,
[4] Unnikrishnan, S., Prasad, J., ”Reactionary Automatic Envelope Protection for Autonomous Unmanned Aerial Vehicles”, AIAA Paper #AIAA-2004-4819, AIAA Atmospheric Flight Mechanics Conference and Exhibit, Providence, Rhode Island, August 16-19, 2004
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[6] Yavrucuk, I., Prasad, J., and Calise, A., “Carefree Maneuvering Using Adaptive Neural Networks", In proceedings of the AIAA Atmospheric Flight Mechanics Conference, Aug. 2002, Monterey,CA. [4] Yavrucuk, I., Prasad, J., “Automatic Limit Detection and Avoidance for Unmanned Helicopters", In
American Helicopter Society 57th Annual Forum Proceedings, May 2001, Washington D.C.
[8] Yavrucuk, I., Unnikrishnan, S., Prasad, J., “Envelope Protection in Autonomous Unmanned Aerial Vehicles," In proceedings of the American Helicopter Society 59th Annual Forum, May 2003, Phoenix AZ.
[9] Johnson, E., Schrage, D., Prasad, J., Vachtsevanos, G., “UAV Flight Test Programs at Georgia Tech”, Proceedings of the AIAA Unmanned Unlimited Technical Conference, Workshop, and Exhibit, 2004.
http://www.nasa.gov/centers/dryden/research/HALE_ROA/.
[10] Johnson, E., Kannan, S., “Adaptive Trajectory Control for Autonomous Helicopters”, AIAA Journal of Guidance, Control, and Dynamics, Vol. 28, No. 3, 2005.