Rolf Rysdyk

Unmanned Aerial Vehicle Path Following for Target Observation in Wind

This work provides flight-path geometry, guidance laws, and synchronous camera angles to observe a ground target from an unmanned aerial vehicle. The observation of the target is affected by wind, aircraft performance, and camera limits. Analytic expressions are derived for paths that result in constant line-of-sight orientation of the target relative to the aircraft body frame. Using minimal heuristics, a guidance law based on “good helmsman” behavior is developed and implemented, and stability of its integration with aircraft dynamics is assessed. An observer estimates wind data, which are used to orient path geometry about the target. Results are demonstrated in high-fidelity simulation. Nomenclature a = helmsman sensitivity parameter c(·) = cos(·) d = distance Fb = body-fixed frame Fs = Serret‐Frenet frame g = gravity constant r = radius s = arclength position along desired path s(·) = sin(·) t(·) = tan(·)

Real-time planning for teams of autonomous vehicles in dynamic uncertain environments

In a highly dynamic environment, an adaptive real-time mission planner is essential for controlling a team of autonomous vehicles to execute a set of tasks. An optimal plan computed prior to the operation will no longer be optimal when the vehicles execute the plan. This dissertation presents a framework and algorithms for solving real-time task and path planning problems by combining Evolutionary Computation (EC) based techniques with a Market-based planning architecture. The planning system takes advantage of the flexibility of EC-based techniques and the distributed structure of Market-based planning. This property allows the vehicles to evolve their task plans and routes in response to the changing environment in real time, and under varying computational time windows.* *This dissertation is a compound document (contains both a paper copy and a CD as part of the dissertation). The CD requires the following system requirements: Windows MediaPlayer or RealPlayer.

UAV Coordination for Autonomous Target Tracking

This report compares several difierent methodologies for tracking a moving target with multiple Unmanned Aerial Vehicles (UAVs). Relative position coordination of UAVs is enforced. The comparison considers minimization of heuristics and robustness of performance and stability when the UAVs are exposed to wind and target motion.

Robust nonlinear adaptive flight control for consistent handling qualities

A flight control design is presented that combines model inversion control with an online adaptive neural network (NN). The NN cancels the error due to approximate inversion. Both linear and nonlinear NNs are described. Lyapunov stability analysis leads to the online NN update laws that guarantee boundedness. The controller takes advantage of any available knowledge for system inversion, and compensates for the effects of the remaining approximations. The result is a consistency in response which is particularly relevant in human operation of some unconventional modern aircraft. A tiltrotor aircraft is capable of converting from stable and responsive fixed wing flight to sluggish and unstable hover in helicopter configuration. The control design is demonstrated to provide a tilt-rotor pilot with consistent handling qualities during conversion from fixed wing flight to hover.

Adaptive Path Planning for Autonomous UAV Oceanic Search Missions

This paper presents an autonomous mission architecture for locating and tracking of harmful ocean debris with unmanned aerial vehicles (UAVs). Mission simulations are presented that are based on actual weather data, predicted icing conditions, and estimated UAV performance degradation due to ice accumulation. Sun position is estimated to orient search and observation maneuvers to avoid sun glare. The planning algorithms are based on evolutionary computation techniques combined with market-based cooperation strategies for multiple UAVs. Both single vehicle and multiple autonomously cooperating UAVs cases are demonstrated.

Cooperative Tracking of Moving Targets by a Team of Autonomous UAVs

This paper summarizes current work on theoretical and experimental cooperative tracking of moving targets by a team of UAVs. The Insitu Group is leading a diverse group of researchers to develop building block foundations for cooperative tracking. The building block algorithms have been maturing through the partners, and the team led by Insitu is now pulling the technologies together for demonstration and commercialization. The work reported here focuses on cooperative tracking using multiple UAVs, with the ability for one operator to control many UAVs which are tasked to 1) provide autonomous tracking of moving and evading targets, and 2) report to a centralized database (without operator attention): the position, position history, velocity vector of the target being tracked. Flock guidance algorithms have been developed and simulated to enable a flock of UAVs to track an evading vehicle. Algorithms have been demonstrated in simulation that: dynamically allocate tasks and compute near-optimal paths in real-time; minimize the probability that vehicles are destroyed due to collision or damage from threat; and accommodate moving targets, time-on-targets, and sequencing, as well as the effects of weather (especially wind) and terrain. Additionally geolocation estimation algorithms and software have been developed which exchange information among vehicles, process the information robustly and in real time, and have demonstrated that the joint accuracy is improved. Work has also focused on accurate probabilistic analysis of the estimates, especially considering variations across multiple vehicle sets of ScanEagle UAVs

Autonomous Orbit Coordination for Two Unmanned Aerial Vehicles

This work considers autonomous coordination between two Unmanned Aerial Vehicles in orbit about a target, with the purpose of geo-locating the target. Wind signican tly aects the relative phase angle between the vehicles. Guidance algorithms are investigated to maintain an approximately constant phase angle in wind. A planar-kinematic aircraft model is proposed in which the eects of attitude dynamics and nonlinearities are considered. 1. NOMENCLATURE Course, [rad] Va Airspeed, [m/s] Vg Inertial speed, [m/s] Vo Nominal inertial speed, [m/s] Vw Windspeed, [m/s] p Clock angle, or bearing from orbit center, [rad] Heading, [rad] w Wind direction (from), [rad] ~ V Velocity, [m/s] xN North position [m] yE East position [m] R Radius of orbit, [m] Subscripts w Wind e Earth xed North-East-Down frame (NED) b Body xed frame 1; 2 Vehicle 1; 2

Market-based Co-evolution Planning for Multiple Autonomous Vehicles

In a highly dynamic environment, an adaptive real-time mission planner is essential for controlling a team of autonomous vehicles to execute a set of assigned tasks. The optimal plan computed prior to the start of the operation might be no longer optimal when the vehicles execute the plan. This paper proposes a framework and algorithms for solving real-time task and path planning problems by combining Evolutionary Computation (EC) based techniques with a Market-based planning architecture. The planning system takes advantage of the flexibility of EC-based techniques and the distributed structure of Market-based algorithms. This property allows the vehicles to evolve their task plans and routes in response to the changing environment in real time.

Autonomous Airborne Geomagnetic Surveying and Target Identiflcation

This work considers algorithms for maritime search and surveillance missions. Search and identiflcation of magnetic anomalies are evaluated. A combination of a particle fllter and a neural network are used to identify and classify anomalies. Communication among vehicles is assumed to update a centralized occupancy based map which represents a discretized belief of target locations. Control decisions are based on a nearest neighbor search of the surrounding cells of the occupancy map. Simulation is performed using a planar kinematic model and actual aeromagnetic data.

Search Algorithm for Teams of Heterogeneous Agents with Coverage Guarantees

Amongcommonintelligence,reconnaissance,andsurveillancetasks,searchingforatarget in a complex environment is a problem for which autonomous systems are well suited. This workconsiderstheproblemofsearchingfortargetsusingateamofheterogeneousagents.The systemmaintainsagrid-basedworldmodelwhichcontainsinformationabouttheprobability that a target is located in a given cell of the map. Agents formulate control decisions for a fixed number of time steps using a modular algorithm that allows parameterizations of agent capabilities.This paper investigates a solution that guarantees total map coverage.The control law for each agent does not require explicit knowledge of other agents. This yields a systemwhichisscalabletoalargenumberofvehicles.Theresultingsearchpatternsguarantee an exhaustive search of the map in the sense that all cells will be searched sufficiently to ensure that the probability of a target going unnoticed is driven to zero. Modifications to this algorithm for explicit cooperation between agents is also investigated.