Optimal Collision Avoidance Trajectories for Unmanned/Remotely Piloted Aircraft

The post-911 environment has punctuated the force-multiplying capabilities that Remotely Piloted Aircraft (RPA) provides combatant commanders at all echelons on the battlefield. Not only have unmanned aircraft systems made near-revolutionary impacts on the battlefield, their utility and proliferation in law enforcement, homeland security, humanitarian operations, and commercial applications have likewise increased at a rapid rate. As such, under the Federal Aviation Administration (FAA) Modernization and Reform Act of 2012, the United States Congress tasked the FAA to provide for the safe integration of civil unmanned aircraft systems into the national airspace system (NAS) as soon as practicable, but not later than September 30, 2015. However, a necessary entrance criterion to operate RPAs in the NAS is the ability to Sense and Avoid (SAA) both cooperative and noncooperative air traffic to attain a target level of safety as a traditional manned aircraft platform. The goal of this research effort is twofold: First, develop techniques for calculating optimal avoidance trajectories, and second, develop techniques for estimating an intruder aircraft\u27s trajectory in a stochastic environment. This dissertation describes the optimal control problem associated with SAA and uses a direct orthogonal collocation method to solve this problem and then analyzes these results for different collision avoidance scenarios

Optimal pilot decisions and flight trajectories in air combat

The thesis concerns the analysis and synthesis of pilot decision-making and the design of optimal flight trajectories. In the synthesis framework, the methodology of influence diagrams is applied for modeling and simulating the maneuvering decision process of the pilot in one-on-one air combat. The influence diagram representations describing the maneuvering decision in a one sided optimization setting and in a game setting are constructed. The synthesis of team decision-making in a multiplayer air combat is tackled by formulating a decision theoretical information prioritization approach based on a value function and interval analysis. It gives the team optimal sequence of tactical data that is transmitted between cooperating air units for improving the situation awareness of the friendly pilots in the best possible way. In the optimal trajectory planning framework, an approach towards the interactive automated solution of deterministic aircraft trajectory optimization problems is presented. It offers design principles for a trajectory optimization software that can be operated automatically by a nonexpert user. In addition, the representation of preferences and uncertainties in trajectory optimization is considered by developing a multistage influence diagram that describes a series of the maneuvering decisions in a one-on-one air combat setting. This influence diagram representation as well as the synthesis elaborations provide seminal ways to treat uncertainties in air combat modeling. The work on influence diagrams can also be seen as the extension of the methodology to dynamically evolving decision situations involving possibly multiple actors with conflicting objectives. From the practical point of view, all the synthesis models can be utilized in decision-making systems of air combat simulators. The information prioritization approach can also be implemented in an onboard data link system.reviewe

Optimal Control Methods for Missile Evasion

Optimal control theory is applied to the study of missile evasion, particularly in the case of a single pursuing missile versus a single evading aircraft. It is proposed to divide the evasion problem into two phases, where the primary considerations are energy and maneuverability, respectively. Traditional evasion tactics are well documented for use in the maneuverability phase. To represent the first phase dominated by energy management, the optimal control problem may be posed in two ways, as a fixed final time problem with the objective of maximizing the final distance between the evader and pursuer, and as a free final time problem with the objective of maximizing the final time when the missile reaches some capture distance away from the evader.These two optimal control problems are studied under several different scenarios regarding assumptions about the pursuer. First, a suboptimal control strategy, proportional navigation, is used for the pursuer. Second, it is assumed that the pursuer acts optimally, requiring the solution of a two-sided optimal control problem, otherwise known as a differential game. The resulting trajectory is known as a minimax, and it can be shown that it accounts for uncertainty in the pursuer\u27s control strategy. Finally, a pursuer whose motion and state are uncertain is studied in the context of Receding Horizon Control and Real Time Optimal Control. The results highlight how updating the optimal control trajectory reduces the uncertainty in the resulting miss distance

Technology challenges of stealth unmanned combat aerial vehicles

The ever-changing battlefield environment, as well as the emergence of global command and control architectures currently used by armed forces around the globe, requires the use of robust and adaptive technologies integrated into a reliable platform. Unmanned Combat Aerial Vehicles (UCAVs) aim to integrate such advanced technologies while also increasing the tactical capabilities of combat aircraft. This paper provides a summary of the technical and operational design challenges specific to UCAVs, focusing on high-performance, and stealth designs. After a brief historical overview, the main technology demonstrator programmes currently under development are presented. The key technologies affecting UCAV design are identified and discussed. Finally, this paper briefly presents the main issues related to airworthiness, navigation, and ethical concerns behind UAV/UCAV operations

Obstacle Avoidance for a Game Theoretically Controlled Formation of Unmanned Vehicles

The thesis provides a game theoretical approach to the control of a formation of unmanned vehicles. The objectives of the formation are to follow a prescribed trajectory, avoiding obstacle(s) while maintaining the geometry of the formation. Formation control is implemented using game theory while obstacles are avoided using Null Space Based Behavioral Control algorithm. Different obstacle avoidance scenarios are analyzed and compared. Numerical simulation results are presented, to validate the proposed approach

Trajectory optimization with detection avoidance for visually identifying an aircraft

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2005.Includes bibliographical references (p. 115-118).Unmanned aerial vehicles (UAVs) play an essential role for the US Armed Forces by performing missions deemed as "dull, dirty and dangerous" for a pilot. As the capability of UAVs expand. they will perform a broader range of missions such as air-to-air combat. The focus of this thesis is forming trajectories for the closing phase of an air-to-air combat scenario. A UAV should close with the suspected aircraft in a manner that allows a ground operator to visually identify the suspected aircraft while avoiding visual/electronic detection from the other pilot. This thesis applies and compares three methods for producing trajectories which enable a visual identification. The first approach is formulated as a mixed integer linear programming problem which can be solved in real time. However, there are limitations to the accuracy of a radar detection model formed with only linear equations, which might justify using a nonlinear programming formulation. With this approach the interceptor's radar cross section and range between the suspected aircraft and interceptor can be incorporated into the problem formulation. The main limitation of this method is that the optimization software might not be able to reach online an optimal or even feasible solution. The third applied method is trajectory interpolation. In this approach, trajectories with specified boundary values and dynamics are formed offline; online, the method interpolates between the given trajectories to obtain similar maneuvers with different initial conditions and end- states. With this method, because the number of calculations required to produce a feasible trajectory is known, the amount of time to calculate a trajectory can be estimated.by Leonard N. Wholey.S.M

Intelligent Autonomous Decision-Making and Cooperative Control Technology of High-Speed Vehicle Swarms

This book is a reprint of the Special Issue “Intelligent Autonomous Decision-Making and Cooperative Control Technology of High-Speed Vehicle Swarms”,which was published in Applied Sciences

A reactive/deliberative planner using genetic algorithms on tactical primitives

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2006.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 99-102).Unmanned aerial systems are increasingly assisting and replacing humans on so-called dull, dirty, and dangerous missions. In the future such systems will require higher levels of autonomy to effectively use their agile maneuvering capabilities and high-performance weapons and sensors in rapidly evolving, limited-communication combat situations. Most existing vehicle planning methods perform poorly on such realistic scenarios because they do not consider both continuous nonlinear system dynamics and discrete actions and choices. This thesis proposes a flexible framework for forming dynamically realistic, hybrid system plans composed of parametrized tactical primitives using genetic algorithms, which implicitly accommodate hybrid dynamics through a nonlinear fitness function. The framework combines deliberative planning with specially chosen tactical primitives to react to fast changes in the environment, such as pop-up threats. Tactical primitives encapsulate continuous and discrete elements together, using discrete switchings to define the primitive type and both discrete and continuous parameters to capture stylistic variations. This thesis demonstrates the combined reactive/deliberative framework on a problem involving two-dimensional navigation through a field of threats while firing weapons and deploying countermeasures. It also explores the planner's performance with respect to computational resources, problem dimensionality, primitive design, and planner initialization. These explorations can guide further algorithm design and future autonomous tactics research.by Stephen William Thrasher, Jr.S.M

Real-time maneuvering decisions for autonomous air combat

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2008.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 127-129).Unmanned Aircraft Systems (UAS) have the potential to perform many of the complex and possibly dangerous missions currently flown by manned aircraft. Within visual range air combat is an extremely difficult and dynamic aerial task which presents many challenges for an autonomous UAS. An agile, unpredictable, and possibly human-piloted adversary, coupled with a complex and rapidly changing environment, creates a problem that is difficult to model and solve. This thesis presents a method for formulating and solving a function approximation dynamic program to provide maneuvering decisions for autonomous one-on-one air combat. Value iteration techniques are used to compute a function approximation representing the solution to the dynamic program. The function approximation is then used as a maneuvering policy for UAS autonomous air combat. The result is an algorithm capable of learning a maneuvering policy and utilizing this policy to make air combat decisions in real-time. Simulation results are presented which demonstrate the robustness of the method against an opponent beginning from both offensive and defensive situations. The results also demonstrate the ability of the algorithm to learn to exploit an opponent's maneuvering strategy. Flight results are presented from implementation on micro-UAS flown at MIT's Real-time indoor Autonomous Vehicle test ENvironment (RAVEN) demonstrating the practical application of the proposed solution in real-time flight with actual aircraft.by James S. McGrew.S.M

Low altitude threat evasive trajectory generation for autonomous aerial vehicles

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2004.Includes bibliographical references (p. 117-119).In recent years, high altitude unmanned aerial vehicles have been used to great success in combat operations, providing both reconnaissance as well as weapon launch platforms for time critical targets. Interest is now growing in extending autonomous vehicle operation to the low altitude regime. Because perfect threat knowledge can never be assumed in a dynamic environment, an algorithm capable of generating evasive trajectories in response to pop-up threats is required. Predetermination of contingency plans is precluded due to the enormity of possible scenarios; therefore, an on-line vehicle trajectory planner is desired in order to maximize vehicle survivability. This thesis presents a genetic algorithm based threat evasive response trajectory planner capable of explicitly leveraging terrain masking in minimizing threat exposure. The ability of genetic algorithms to easily incorporate line-of-sight effects, the inherent ability to trade off solution quality for reduced solution time, and the lack of off-line computation make them well suited for this application. The algorithm presented generates trajectories in three-dimensional space by commanding changes in velocity magnitude and orientation. A crossover process is introduced that links two parent trajectories while preserving their inertial qualities. Throughout the trajectory generation process vehicle maneuverability limits are imposed so that the resultant solutions remain dynamically feasible.(cont.) The genetic algorithm derived provides solutions over a fixed time horizon, and is implemented in a receding horizon fashion, thereby allowing evasion of threat areas of arbitrary size. Simulation results are presented demonstrating the algorithm response for a rotorcraft encountering several different threat scenarios designed to evaluate the effectiveness of the algorithm at minimizing risk to the vehicle.by Ryan L. Pettit.S.M