Juris Vagners

54.3: Modeling and Control of the Resonant Fiber Scanner for Laser Scanning Display or Acquisition

The resonant fiber scanner produces a flying laser spot scan for display or image acquisition purposes. Dynamic nonlinearities during large amplitude vibrations of the resonant fiber scanner result in distortions in the two-dimensional scan pattern and the acquired images. A dynamic model which includes the fibers dynamic nonlinearities has been developed to understand the nonlinear behavior and as the basis of a controller to remove the scan distortion. A robust state-space controller has been implemented to force the resonant fiber scanner to follow a spiral scan pattern. Acquired images at 250×250 pixel resolution demonstrate improved image fidelity over previous images taken with open-loop scanning.

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.

Experimental evaluation of a hybrid position and force surface following algorithm for unknown surfaces

This paper experimentally implements a surface following algorithm for unknown surfaces under a hybrid position and force scheme. The mechanization uses a low friction roller at the end of a two-link manipulator so that primarily the normal force is measured. With the normal force information and joint angle information, the surface normal direction is calculated and a position reference is calculated along the surface tangent. Experimental evaluation shows the algorithm works for a line, a concave arc, and a convex arc. Also, when different force levels are commanded or different controllers are used, the algorithm still performs well. Thus, the algorithm allows a constant force to be applied while following a surface whose shape is not known a priori.

Assessing and Estimating Risk of Operating Unmanned Aerial Systems in Populated Areas

In order to operate in the national airspace, an aircraft system must have documentation and analysis to show that it can operate at a satisfactory level of safety. For traditional manned aircraft systems, this is equivalent to operating a reliable system. However with Unmanned Aerial Systems (UAS), a relatively unreliable system can safely be operated provided that the risk to bystanders on the ground is sufficiently low. This paper presents a set of design tools and methodologies which can be used to assess the risk associated with operating an UAS in a potentially populated area. The intended use of the tool is discussed and a risk assessment is provided for an existing UAS.

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

Control aspects of the Single Fiber Scanning Endoscope

The Single Fiber Scanning Endoscope (SFSE) is a new class of endoscopes being developed at the University of Washingtons Human Interface Technology lab which uses combinations of a resonating optical fiber and a single photodetector to produce large field of view, high-resolution images from a small flexible package. Although current prototypes show the validity of the concept, the nonlinear response of the resonant optical fiber under open loop control creates image distortion or limits the frame rate. Due to low damping and nonlinear effects in the fiber, open loop control, phase lock loops, PID control, classical and modern controllers are all unable to produce accurate, reproducible, robust high frequency 2D scans. A nonlinear control scheme, feedback linearization, is capable of accurately producing a scan and is robust to most of the unavoidable manufacturing and environmental variability in the resonant scanner. Through theoretical analysis and simulations, this paper reviews the application of the following variety of open loop and closed loop controllers to the nonlinear scanner of the SFSE: open loop control, modelless closed loop control (phase lock loop and PID control), feedforward plus feedback classical and modern state space tracking control, and nonlinear feedback linearization control.

A Nonlinear State-Space Model of a Resonating Single Fiber Scanner for Tracking Control: Theory and Experiment

A nonlinear state-space dynamic model of a resonating single fiber scanner is developed to understand scan distortion-jump, whirl, amplitude dependent amplitude and phase shifts-and as the basis for controllers to remove those distortions. The non-planar nonlinear continuum dynamics of a resonating base excited cantilever are reduced to a set of state-space coupled Duffing equations with centripetal acceleration. Methods for experimentally determining the model parameters are developed. The analytic frequency responses for raster, spiral and propeller scans are derived, and match experimental frequency responses for all three scan patterns, for various amplitudes, and using the same model parameters.

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.

An Error Space Controller for a Resonating Fiber Scanner: Simulation and Implementation

A robust error-space controller for an amplitude modulated resonating fiber scanner is developed and implemented. Using a nonlinear dynamics model, the systems open loop temporal response for a spiral scan pattern is simulated and compared to experiments to understand scan distortions, such as, toroid, swirl, and beating. A feedback linearized linear quadratic regulator based on error dynamics drives the tracking error to zero. Controller simulations determine robustness limits, and effects of fiber nonlinearities and actuator time delay. Using real-time hardware, the controller asymptotically tracks and is capable of removing the scan distortions. Image acquisition with the controlled scanner produces nearly pixel accurate images.

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.