ApplicationCorey Ippolito

NASA Ames Research CenterMoffett Field, CA 94035

6.50-604-160.5c orey. a. ippolito @nas a. gov

Applications of Payload Directed FlightMatt Fladeland

Yoo Hsiu YehNASA Ames Research Center

Carnegie Mellon University

Moffett Field, CA 94035

Moffett Field, CA 940356.50-604-332.5

6.50-604-0863matthew.m. fladeland(anasa. goy-• yoo_h_yeh@nasa.gov

Abstract—Next generation aviation flight control conceptsrequire autonomous and intelligent control systemarchitectures that close control loops directly aroundpayload sensors in manner more integrated and cohesive thatin traditional autopilot designs. Research into payloaddirected flight control at NASA Ames Research Center isinvestigating new and novel architectures that can satisfy therequirements for next generation control and automationconcepts for aviation. Tighter integration between sensorand machine requires definition of specific sensor-directedcontrol modes to tie the sensor data directly into a vehiclecontrol structures throughout the entire control architecture,from low-level stability- and control loops, to higher levelmission planning and scheduling reasoning systems.Payload directed flight systems can thus provide guidance,navigation, and control for vehicle platforms hosting a suiteof onboard payload sensors. This paper outlines relatedresearch into the field of payload directed flight ; andoutlines requirements and operatin g concepts for payloaddirected flight systems based on identified needs from thescientific literature.' z

TABLE OF CONTENTS

1. INTRODUCTION ............................................................12. RELATED RESEARCH ...................................................23. GENERAL CO\CEPTS OF OPERATIONS .......................44. GENERAL REQUIREMENTS ..........................................95. CONCLUSION AND FUTURE RESEARCH .....................126. REFERENCES ..............................................................137. BIOGRAPHIES .............................................................15

1. INTRODUCTION

The enormous benefits of fielding sensor payloads onsuborbital flight platforms is being firmly reinforced throughthe growing number of successful high profile missionsinvolving sensors with ever increasing sophistication andaccuracy. In the current practice of fielding advanced sensorpayload systems on manned and large unmanned aircraftplatforms, automation architectures and mission concepts donot involve explicit autonomous loop closure from thepayload sensor data to the autopilot system. However, in therealm of small-scale autonomous aerial vehicle systems,significant technological advances have been made by tyingautonomic systems with payload components ; especially in

1` U.S. Government work not protected by U.S. copyright.2 IEEEAC paper #1410, Version 4. Updated 2008:11:03

the milieu of academic research: the results of theseinnovative research projects (as summarized in Section 2)provide strong arguments for the benefit of sensor-directedloop closure and tight coupling between the autopilot andpayload sensor subsystems.

Development of a sensor-integrated vehicle automationarchitecture on a full-scale manned or unmanned flightplatform is inherently a large cross-disciplinary endeavorrequiring significant resource and schedule allocation. Theengineering approach to such undertakings must be firmlyestablished before the proliferation of such concepts can berealized. The purpose of the Payload Directed Flight (PDF)project at NASA Ames Research Center is to researcharchitectures and methodologies through which subsonicfixed-wing aerial vehicles y can meet payload-specificobjectives through controllers that close the loop aroundpayload sensors. These architectures must be capable ofsatisfying mission objectives of the sensor payloads inspecific regard to providing observations of partiallyobservable phenomena, such as earth science subjects whichare typically large in size and external to the controlledvehicle system. These next generation architectures areenvisioned to be payload-centric, closing multiple controlloops directly around the output of payload sensors,reconfigurable, able to close the loop on a variety of sensorspayloads, both existing and yet to be developed, adaptive,able to respond to change data input from sensor payloads,and intelligent, endowed with limited decision makingcapabilities that help the aircraft and,-or pilot maximize datareturn from the onboard payloads and remote sensors.

Onboard sensors must be able to relay information beyondraw and filtered sensor data to the control systemarchitecture, such as the quality of data being return ; desiredtargeting locations within the external phenomena, desiredtarget models and tracking filters, constraints that thepayload must impart on the vehicle to perform datacollection, and higher level mission objectives with regardsto the payload. At various layers of control — where eachlayer is characterized by its operating frequency — thevarious autonomous and intelligent components must utilizethe sensor information and process the data to affect controlover the vehicle to meet the requirements of the mission.

This paper outlines the operational concepts and generalrequirements for payload directed flight systemarchitectures, details a prototypical architecture, anddescribes applications to an autonomous vehicle system.

The architecture described allows for sensor data to supportoperational control modes and functionality throughout thecontrol architecture. A prototypical middle-layer controllersystem is introduced to satisfy the requirements of payloaddirected flight. This system produces fli ght guidancethrough trajectory generation and path planning, given therequirements, constraints, and objectives of missionsdirected by sensor payloads.

2. RELATED RESEARCH

There are a vast nuinber of applications of payload directedcontrol systems involving UAVs in the literature, and acomprehensive summary is beyond the scope of this paper.This section presents a sunimary of few select applicationsthat have relevance to current PDF research directions.

In [1] and [2] vision systems are used to extract roadcenterlines. Here the road identification process is achievedthrough linage processing that exploits the linear or locallylinear nature of roadways. Once the road center line isextracted, the tracking problem is reduced to a simple pathfollowing problem. The traditional approach to this problemis to formulate an inner loop controller using linear controltheory and design an outer loop controller that uses thedesired path to specify the desired bank angle or lateralacceleration and, in some instances, altitude (this is usuallyconstant for the entire mission). In [1] several nonlinearcontrol laws are proposed for solving this problem;interesting control results are also found in [3], [4], [5], and[6]. Additionally, similar research tracking rivers andshorelines is presented in [7] and [8]. An approach fortracking a ground vehicle is presented in [9]. In [10],payload directed flight for fixed-winged aircraft isconcerned with having a UAV serve as a communicationrepeater in a larger communication network. To achieve this,the UAV orbits a radio or communication ground sourcemaintaining a fixed signal-to-noise ratio (SNR) with thatsource. The problem is strongly analogous to the contourfollowing problem with in-situ sensors. Presented in [10] isa traditional PID based control law formulated around theSNR error between time steps. This formulation, asdemonstrated through simulation, will cause the UAV tospiral towards the specified contour (specified SNR value)and remain there once it is acquired. However, implicit inthe formulation of this control law is the assumption that theSNR field is monotonic. In many of the cases alreadypresented, this assumption was not applicable andnecessitated the development of behavioral approaches.

The re-tasking problem for UASs has also been explored inthe literature. In [11] a list of targets to service is treated as aTraveling Salesman Problem. The problem of incorporatingvehicle dynanucs is achieved by solving the TravelingSalesman Problem using heuristics from traditionalcombinatorial optimization and then alternating the pathsbetween targets as linear paths and nuninitun Dubins paths

[12]. Several competing approaches using Dubins minimumpaths are also available in [I 1 ], [ 13], [ 14]. and [ 15].

In [16], the notion of effectively planning given a sensorswath was explored. In this work a UAS has a downwardfacing camera, which in turn has a certain field of view. Theproblem is to determine the optimal tour through the targetssuch that all targets are observed. Unlike the TSP problemwhere the UAS passes through all the tar gets, this is a casewhere it is only necessary for the targets to pass through thesensor's field of view. To solve this problem the UAS wasmodeled as a Dubins car with a discrete set of inputs uoperating over a finite At. The operational space for theUAS was then explored using this model and the learningA* algorithm operating with an admissible heuristic. Asimilar problem as this was also considered in [17]. Here,however, the operational space is explored using aprobabilistic planning approach based on the Rapidly-Exploring Random Root Tree algorithm, which isthoroughly discussed in [18].

Some of the research in the vein of payload directed flightfor fixed-winged vehicles is focused on searching for targetswith an unknown location. One example in [19] isprobabilistic in nature and involves selecting a search space,discretizing that search space as a grid of cell locations,applying a probability that a target is in a given cell, andidentifying the optimal path (in a probabilistic sense) foridentifying targets. The research in this field is currentlyfocused on optimal searching with multiple UAVs andoptimizing the target identification or mapping ability overmany vehicles [20].

Proposed Architectures for Payload Directed Flight Control

Payload Directed Flight investigates methods for closingcontrol loops around payload sensors, and there arenumerous methods used in the literature that accomplishthis. Architectures and frameworks are varied in theliterature; often suited to fit a particular set of requirements.Architectures are broken into layered functionalcomponent-blocks; where `higher' level blocks deal withincreasing complexity and decreasing frequency responserates. The functional decomposition of the system is alsohighly dependent on the research: research into higher levelbehavioral algoritlnis may group lower-level functions intoa single block, whereas more fundamental control researchmay further decompose the lower level control systems intoseveral blocks.

The time response for loop closure can be categorized basedon the frequency time response needed for closed-loopsystem response, as similar to the following categorizationsproposed in [21].

Direct Closed Loop Sensor Feedback: At the highestfrequency update, many requirements call for direct

closed loop sensor feedback ; where control must baseoff sensor input in a continuous time fashion with ashort time period. Such situations as tracking orpointing is a requirement for many missions (e.g., forestfire monitoring for small UAVs [22], carbon fluxmonitoring [23], or autonomous plume tracking [24]).Methods of feedback control design, such as optimaland robust control, are used to design controllers thatweigh benefits of increased science returns with otherobjectives [21].

Tactical Planning: Certain situations call for tacticalplanning, where current objectives are traded for thepossibility of future objectives. For instancemaintaining a certain altitude when passing over anobject, momentarily sacrificing science return at thecurrent time in order to reposition the aircraft toincrease return in the near future. Methods such aspredictive control and data-based model prediction havebeen proposed [21].

Strategic Planning: Strategic planning may benecessary to compute trajectories that will position theaircraft and sensors over targets based on previoussensor readings. For instance, based on previous passesover an object, it may be necessary to conduct anotherpass over an area at a different altitude when sensorreadings were insufficient [21][22], or in response tonew objectives uploaded by a human operator [25] suchas re-tasking requirements for science missions [26][24]or forest fire missions [27]. The system must revise itsplan to account for previous and future goals [21 ].

Contingency Planning: Certain scenarios may call for alevel of contingency planning. For instance, smoke mayobscure sensor instruments, excess aircraft jitter maydegrade sensor reading, high wind conditions may causethe aircraft fuel burn rate to be higher than expectedbetween waypoints, or communication links may fail.In these cases, the goals of the system have notchanged, but the system must revise its mission planbased on unexpected conditions [21].

Three-Tiered architectures are used in many systems forhigh-level decomposition, for instance in [31]. Thesesystems typically decompose the control system into high-level, nud-level, and low-level functional blocks. High levelcontrol functional blocks deal with situational awareness,reactive control, and mode selection. Mid-level controllershandle mode transitions of the lower-level components, aswell as providing fault detection algorithms and healthmonitoring functions. The low-level controller providesstability augmentation and control.

Boskovic (et al) introduce a four-layer architecture wherethe research deals with autonomous intelligent control [35].The architecture consists of the following layers:

1. Redundancy Management Layer that consists of theonline Failure Detection and Identification and robustfeedback Adaptive Reconfigurable Controller;

2. Autonomous Trajectory Generation layer whose role isto fit feasible trajectories through the desired way-points in real time;

3. Autonomous Path Planning laver that generates way-points on-line in response to a dynamically changingenvironment, and

4. Autonomous Decision Making layer whose role is toassess the available control authority after failures, andmake mission related decisions in near-real time.

Mathematical and computational frameworks have beenrecently introduced in the literature, which wrap a consistentframework around multiple approaches. For instance, thearchitecture in [45] introduces a computational frameworkthat addresses trajectory optimization of higher-ordersystems with general nonlinear constraints.

The following architecture was proposed in [43], which isfocused largely on path generation in response to an externalsource of,vaypoints:

1. Waypoint Path Planner (WPP): The WPP computes aproposed vehicle route, which is planned without regardfor the dynamic constraints on the vehicle. Thissimplification ensures it is easier to handle dynanucconstraints

2. Dynamic Trajectory Smoother (DTS): The DTSapplies kinematic constraints to the paths and plansgenerated by the WPP. The plan is refined throughkinematic feasibility.

3. Trajectory Tracker (TT): The trajectory trackergenerates uses the kinematically corrected plan from theDTS and computes a feasible state trajectory that can befollowed by a standard autopilot.

4. Low-level Autopilot Compensator (LAC): The LACprovides a standard autopilot implementation to followthe trajectory computed from the TT.

The architecture proposed in [25] outlines an IntelligentAutonomous Architecture (IAA) concept in combinationwith a Collaborative and Coordinated Systems (CCS)component. The IAA combines on-board and ground-basedautomated systems to control the vehicle and its payload. Anautonomous executive performs the basic task of flight andpayload operation. The IAA modifies unattainable orconflicting goals in coordination with intelligent specialistsystems, which provide goal-directed behavior based onadaptive decision-making models of tactical and strategic

planning. Plans and execution are modified in directresponse to changes in internal conditions, externalconditions, and payload related findings. The CCScontinuously keeps UAV operators and scientists aware ofthe current situation onboard the LJAV, similar to theCollaborative Information Portal on the Mars ExplorationRover. Participants have a unified interface and access tothe internal state of the UAS, including controllers, sensors,payload, and other relevant data sets.A five-tiered architecture was proposed in [44]. At thehighest level, a path plainer is responsible for generatingpaths, which are sets of 3D waypoints without timeconstraints. A trajectory smoother takes the generated pathsand transforms the paths into trajectories, which in thecontext of this architecture are time parameterized curves;these trajectories define the desired position of the aircraft atspecified times throughout the plan. A trajectory trackercomponent transforms the trajectories and the current stateof the aircraft into desired velocity commands, altitudeconnnands, and heading commands, which is then fed intothe autopilot system. The autopilot uses sensor feedback toconvert the tracking commands into control surfaceconnnands. Finally, the aircraft hardware level converts thecontrol surface connnands into actual surface deflections.

3. GENERAL CONCEPTS OF OPERATIONS

This section outlines general subsonic fixed wing GN&Coperational concepts derived from a survey of ongoing andfuture missions from the Suborbital Science Program (SSP)under NASA's Science Mission Directorate (SMD). Thesegeneric operating concepts are specified to direct aeronautictechnology development and innovation in a direction thatmeets earth science requirements and goals [1][33][39].Payload directed flight control for suborbital uninhabitedplatforms provide numerous advanta ges over maimedaircraft and orbital measurements, many benefits are laid outin [26][24][25]. thus defining both a need and applicationarea for payload directed flight research. This listsummarizes previous efforts that outline future missionconcepts and capabilities, as captured in the requirementsdocument for suborbital observations [26], which derivedfrom planning sessions and workshops including the NASASMD sponsored Suborbital Science Missions of the FutureWorkshop (SSMFW) in 2004 ([24]-[35]) and the follow-onspecial sessions at the INTEX site [39].

General requirements for future science missions outlined bythe SSP will be roughly divided into three broad categories.

Platform RequirementsSensor and Payload Requirements (includingcoinimunications)General Autonomy Requirements

a. Intelligent Mission Management (IMM)Requirements

b. Payload Directed Guidance; Navigationand Control (GN&C) Requirements

A dedicated effort to enumerate all specific requirements forpayload directed flight, as a subset of general autonomyrequirements, has not been conducted in the literature, and isbeyond the scope of this paper. "Payload Directed Flight" isdirectly identified in [26] as a requirement, but does notprovide any further elaboration beyond this. The SSMFWsessions [24]-[39] defined candidate mission scenarios,which were used to enumerate and elaborate requirementsfor platforms and sensor payloads: the publications allude tovarying levels of autonomy and intelligence without preciserequirement specification at the GN&C level. Many of theproposed missions outlined in the literature make referenceto advanced payload-directed autonomous or semi-autonomous flight control modes (e.g., dirty and dangerousplume measurement and tracking [24], identification andtracking of chemical tracers released in cloud systems [24]).IMM enabling concepts have been proposed in theliterature [25][21][40][22], from which operationalconcepts may be drawn.

The workshop members in [24] were divided into sixdifferent cate gories, representing the major focuses of theSSP program. It is interestin g to note that each groupdiffered in their inclination or disinclination to includeadvanced concepts in their mission concepts; such advancedconcepts as swarm and hive autonomy, mother/daughter shipconcepts, and multiple vehicle coordination were proposedin varying degrees.

NASA has conducted several planning sessions for definingsuborbital science missions involving uninhabited aerialsystems. The Suborbital Science Missions of the FutureWorkshop identified over 33 candidate missions [24] [38].with additional mission defined in [39][23][27]. Missionoperational concepts and requirements extracted from thesemissions are varying and wide ranging: analysis in [24]extracts certain trends as general requirements for autonomyacross multiple mission.

Table 1. Science Missions Requirements: Atmospheric Composition and Chemistry

1 Clouds and Aerosols [24]Multiple Coordinated Aircraft with Real-Time Command CapabilitiesStacked formation flyingSensor web based vectoring of aircraftPrecise release of chemical tracersTracking of chemical tracers through cloud system and out-flow regionSevere weather flying (armored UAV)

2 Stratospheric Ozone [24]Lagrangian SamplingRace-track samplingSpiral Descent Vertical ProfilesIsentropic flight — aircraft adjusts altitude in order to remain on a fixedisentrope [q =T ( P/1000 )3.5]Formation flying (alternative scenario)

3 Tropospheric Ozone [24]Formation flying with four aircraftNear real-time retasking based upon observations fiom remote sensing platform.Following plume eventsObservations of plume synergistic with geostationary platform UV-Vis and IR observations.Pre-programnned scenario with retaskingNear real-time retaskingbased upon observations fionn remote sensing platform.Terrain following radar necessary for near-surface in-situ platforms

4 Water Vapor and Total Water [24]Spiral Descent Vertical Profiles

Table 2. Science Missions Requirements: Tropospheric

5 Tracking long-distance pollution [39]Plume TrackingLong endurance is key

6 Cloud Systems [39]Multiple coordinated aircraftStacked formation flyingSensor web based vectoring of aircraftIdentification and Tracking of Cloud Phenomena

7 Long time-scale vertical profiling [39]Stacked formation flying (high altitude and low altitude in vertical column)Sensor web based vectoring of aircraftSpiral Descent/Ascent Vertical Profiles

8 Global 3-D Species [39]Global Large-Scale Multiple Vehicle Control Coordination (1000 Heterogenious Platforms)Vertical Profiling

9 Transport and Chemical Evolution in the Troposphere [39]Mothership with Extensive Instumentation Coordinating Smaller DronesLagrangian SamplingAutonomy (mothership) to identify spatial extent of air mass being probedCoordinated Control (from Mothership to Drones)Closed-loop control around external sensors

10 Physical oceanography 39

Table 3. Science Missions Requirements: Climate Variability and Change

11 Aerosol, Cloud and PrecipitationMultiple Coordinated Aircraft (planning level, no closed loop control required)Waypoint followingRemote Re-Tasking (altitude leg changes)

12 Glacier and Ice Sheet DynamicsWaypoint/Trajectory FollowingRemote Re-Tasking of Altitudes and LocationLow Altitude Terrain AvoidancePrecision Navigation (Performance Specs Not Given)

13 RadiationMultiple coordinate aircraft (fleet of small UAVs within a vertical column)Tight coordinated race track patternsPointing of instrument in NADIR and ZENIGHT for short periods of timeTight coordinated coward/downward spirals

Table 4. Science Missions Requirements: Water and Energy Cycles

14 Cloud Properties [37]Waypoint Navigation System with Remote Re-TaskingHovering and Circling Search BehaviorsLook for interesting feature, Release In Situ SamplersMothership/Daughtership

15 River Discharge [37]Waypoint Navigation System with Remote Re-TaskingClosed-Loop Tracking of Riverbed (LIDAR)Low Altitude Terrain AvoidancePrecision NavigationStacked formation flyingClosed-Loop Coordinated Control (High Altitude Controls Low Altitude)Coordinated Control for Maximum Return

16 Snow-Liquid Water Equivalent [37]Waypoint Navigation System with Remote Re-TaskingPrecision NavigationPrecision Sensor PointingSearch, Identify, DeploySensor Probe DeploymentLow Altitude Terrain Avoidance

17 Soil Moisture and Freeze/Thaw States [37]Preferred Multiple Aircraft Scenario (Coordinated Flight Control, UAV Sensor Tandems)Grid Search and Circling Search BehaviorsWavuoint Navi gation System with Remote Re-Tasking

Table 5. Science Missions Requirements: Carbon Cycle, Ecosystem, and Biogeochemistry

18 Coastal Ocean Observations [34]Near real-time communication and control coordination with underwater vehiclesLoiter while taking measurements

19 Active Fire, Emissions and Plume Assessment [34]Loiter while taking measurementsDangerous and dirty plume measurementsMultiple coordinated aircraft (low plume in situ measurements, higher altitude fire monitoring)Instrument deployrnent/dropPlume and Air-Mass tracking and followingNavigation in extreme temperatures and wind shearCollaborative control with other UAVs in swarmTerrain Aviodance for low altitude manueveringRace-track samplingAscending spiral for vertical measurementsStacked coordinated formation flying

20 CO2, 02 and Trace Gas Flux Study [34]21 Vegetation Structure, Composition & Canopy Chemistry [34]

Waypoint followingRemote retaskingPossible: Coordinated control with ground towers and satellites

22 Eddy Covariance Measurements in the Southern Ocean Marine Boundary Layer [23]Swann of interactive UAVsLow Altitude Ocean NavigationObstacle Detection and Avoidance (Nautical Vessels, High Waves, Icebergs)Vertical Profile SamplingNAS Requirements (See and Avoid, etc.)Autonomous Boundary Layer Detection and Tracking

Table 6. Science Missions Requirements: Weather

23 Cloud Microphysics / Properties [38]Coordinated Formation Flight of High Altitude AircraftSpiral SearchHorizontal Grid SearchCoordinated Flight with Remote SensorsFlight in Adverse Weather Conditions (fly in the middle of a storm)

'N Extreme Weather [38]Adaptive release of sensor probes / sondesAutonomous MissionFlight in Adverse Weather Conditions (fly in the middle of a storm)Suggest: Horizontal/Spiral Grid SamplingSuggest: Identification of Features fi-om Data

25 Forecast Initialization [38]Suggest: Horizontal/Spiral Grid SamplingRegular Deployment of Sensors / SondesSpecial Location Deployment of SondesReal-Time Data Assimilation into Forecast ModelsReal-Time Re-Tasking out of Circuit into Sensitive Zones

26 Hurricane Genesis, Evolution and Landfall [38]Formation FlyingCoordination of Multiple AircraftStacked coordinated formation flyingAdaptive Release of Sensor Sondes or Daughter ShipsDecision Support Tools leading Into Autonomous OperationsIdeally a Fully .Autonomous MissionAutonomous TrackingLow Altitude Ocean Navigation

Table 7. Science Missions Requirements: Earth Surface and Interior Structure [35]

27 Surface Deformation

[35]Precision Navigation (+/- 5 meters)Precision Pointing of InstrumentsFormation Flying - Collaborative ControlFly Long Level Flight Lines in a Grid Pattern, +/- 5 meters

28 Ice Sheets

[35]

Precision Navigation (+/- 5 meters)Precision Pointing of InstrumentsFonnation Flying - Collaborative ControlFly Long Level Flight Lines in a Grid Pattern, +/- 5 meters

29 Surface Measurements using Imaging Spectroscopy

[35]Precision Navigation (+/- 5 meters)Formation Flying - Collaborative ControlFly Long Level Flight Lines in a Grid Pattern, +/- 5 meters

30 Topography using LIDAR

[35]Fly Long Level Flight LinesPrecision Navigation in Corridors

31 Gravitational Acceleration

[35]Precision Navigation (+/- 30 meters)Preprogrammed Waypoints and Trajectories

32 International Polar Year

[35]Waypoint following

33 Magnetic Fields

[35]Collaborative control with other UAVs in swarmTight Coordinated Grid SearchFonnation FlyingPrecision Navigation

14 Terrestrial reference frame stability

[35]

the various platforms and sensors to specified locations in4. GENERAL REQUIREMENTS

order to meet the objectives.

Req 1.0. Sensor Web IntegrationAlmost every scenario requires integration of multipleplatforms and payloads — from satellites carrying imagers orradar to subsurface underwater vehicles with optical camerasor water chemistry instruments — in order to accomplish themission goals. This requirement is mission dependent, andwill likely require tactical or strategic planning, for instance,based on real-time data collected and processed andtransmitted from multiple remote platforms to an aerialplatforni within a given time constraint. The mechanism bywhich this data can be shared is largely an informationtechnology problem. and is the subject of current activeresearch. PDF algorithms must operate over existingcoimnunication networks that implement the sensor web.Therefore the science payload guiding PDF implementationsshould not necessarily be assumed to be integrated on theaircraft of interest.

Req 1.1. Sensor Web Based Vectoring of AircraftHigh level integration at the outer loop planner andscheduler is required in several missions, such as theVegetation Structure, Composition and Canopy Chemistrymission in [34] , or synergizing plume observations withgeostationary platforms and other aerial platforms in theTropospheric Ozone mission [24]. Lower level integrationis a requirement for several missions. A particular sensor-web integration requirement at a lower level comes from theSouthern Ocean mission described in [23]. A hi gh altitudeaircraft or satellites carrying imaging spectrometers ortunable LIDAR must provide guidance vectors or sensorinformation to a low altitude aircraft. The low altitudeaircraft, carrying gas samplers and nadir and zenith highresolution imaging spectrometers, will utilize thisinformation, along with information from satellites, toattempt to sample locations of maximum carbon flux

Req 1.2. Convergent Modeling and Control of PlatformsThe following possible requirement came from discussionswith earth scientists. Given a sensor web integration ofmultiple platforms, consider the problem of real-timeintegration of various sensors and payloads into a givenmodel. An example would be to map and model theemission plume of a 1 ha forest fire using optical, active, andgas sampling systems. Models would ingest weather, fuels,and high resolution wind fields to provide probable plumebehavior and this would set initial conditions for the flightplanner. Processed data from instruments on differentaircraft would then be used to update flight paths, and themodel could reset initial conditions for follow-on runs. Thisapproach might be used to verify an existing model, orprovide data to generate a new model of poorly understoodphenomena. The uncertainty in the model is used to vector

Req 2.0. Precision Maneuvering, Navigation andTracking

Aerial platforms are required to perform navigation tasksand track objectives to various degrees of accuracy. Theexact accuracy required is dependent on the specific missionrequirements. The accuracy requirement must be used tomatch missions to particular platforms and suites ofavionics. More stringent requirements on navigationalaccuracy may place requirements on the navigationalinstrumentation. Certain platforms may not have sufficientcontrollability to meet the mission required tolerances.

Req 2.1. General Programmable AutopilotGeneral capabilities for most missions require an autopilotwith autonomous flight capability. A minimum requirementis rough tracking between waypoints, while some nussionsrequire programmable trajectories, for instance, the SurfaceDeforniation nussion [35] require high accuracy trajectorytracking to within +/- 5 meters.

Req 2.2. Remote Re-TaskingPer requirements in Req 2. 1, all functionality of the generalprogrammable autopilot system must be fully accessiblethrough over the horizon communication links. At any pointin the mission, operators must be allowed to repro gram theautopilot with new waypoints or trajectories. V

Req 2.3. Lagrangian SamplingThe influence of the sampling time on processes which areunder the influence of atmospheric difiiusion, such as plumesampling, can be accounted for using Lagrangian sampling,in which a volume is sampled over a time interval Lambda ata fixed downwind distance (travel time) from the source,using a simple modification to Taylor's equation for absolutediffusion. Direct application of this general approach is toocomplex to apply in practice, and simplifications andapproximations have been developed for application [41].The guidance algorithms for implementation must besynchronized with the payload for accurate sample control,as well as the onboard sensors suite for accurate datacollection that is synchronized with the onboard sensor suite.

Req 2.4. Race-Track and Grid Search PatternsReprogrammed sampling at periodic 2D intervals provides asimpler sampling requirement than in Req 2.3. In thisscenario, depending on the accuracy needed, the generalautopilot requirement of Req 2.1 may be sufficient fornavigation. Onboard avionics and navigation sensors mustbe synchronized with the payload for accurate samplecontrol.

Req 2.5. Vertical Profiling through Spiral Ascent andDescent in Vertical ColumnVertical profiling is a requirement for several missions thatrequire sampling gas constituents or measuring radiativeproperties within a column of the atmosphere. Generalascending and descending modes must be synchronized withthe payload for accurate sample control, as well as theonboard sensors suite for accurate data collection that issynchronized with the onboard sensor suite.

Req 2.6 Tracking of Chemical Tracer through CloudSystem and Outflow RegionThe requirement for tracking a chenucal tracer comes fromthe Cloud and Aerosol mission outlined in [24]. A tracerchemical is released in the boundary layer near the bottomof the in-flow region and trace its transport through thecloud system to the out-flow region. Proposed tracerchemicals include hydrocarbons, formaldehyde (1), HNO3,NOy*, CO2*, CO*, HCI, CH3I*, and sulfur species (e.g.,H2SO4, S02). The controller must position the aircraft tosample the species with onboard sensors, and then controlthe vehicle to accomplish two simultaneous objectives: thecontroller must position the vehicle such that the sensors totrack the target, and the controller must navigate to track thephenomena. This maneuvering must be performed insynchronization with sensors for accurate data collectionthat is synchronized with the onboard sensor suite.

Req 2.7. Isentropic Flight TrackingThe Strospheric Ozone nussion in [24] requires isentropicflight. In this control scenario, the aircraft must adjustaltitude (presumably on an arbitrary navigation path) inorder to remain on a fixed isentrope. This maneuveringmust be performed as a function of concern upon airborneozone instruments and ozone column concentration mapsfrom orbital assets such as OMI on AURA, and modelestimate. Each data source would be weighted differentlybased upon known biases or other uncertainties in the dataproducts and as a function of their particular vantage point.

Req 2.8. Autonomous Bor.mdaiy Layer Detection andTrackingSeveral science missions, such as the Southern Oceansnussion in [23] and the Cloud and Aerosol mission outlinedin [24], require a vehicle to autonomously detect a surfaceboundary layer via pressure, temperature, and chemicalcomposition, and track this layer. This requirement may beseen as similar to isentropic flight requirements in Req 2.7.

Req 2.9. Phime Air-Mass Following/TrackingThe aircraft must be able to identify and track a plume ofwater vapor or other constituent undergoing atmosphericprocesses, including diffusion, air mass translation, etc.This requirement stems from several nussion, including theTracking Long-Distance Pollution [39] mission and theActive Fire Enussion and Plume Assessment mission [34].The vehicle must position the aircraft to sample the species

with onboard sensors, and guide the vehicle to (1) positionthe sensors to track the target, and (2) navigate whilepositioning to track the phenomena. This maneuvering mustbe performed in synchronization with sensors for accuratedata collection that is synchronized with the onboard sensorsuite.

Req 2.10. Low Altitude Terrain Altitude FollowingLow-altitude maneuvering is required for many types ofmissions. In this control scenario, the aircraft must adjustaltitude (presumably on an arbitrary navigation path) inorder to remain on a fixed altitude from the terrain.Depending on the altitude and terrain characteristics, thismay require a mixture of low-level control fixed around analtimeter (such as a LIDAR or RADAR sensor) withstrategic and tactical planning to avoid obstacles such asbuilding, trees, mountains, etc.

Req 2.11. Low Altitude Terrain Avoidance, 3D ManeuveringGenerally, this requirement requires the planner to be able tocompute new trajectories with an arbitrary number and typeof constraints. This requirement is sinular to Req 2.10, butin a 3D navigational context (rather than just altitudeadjustments). This may require computing new trajectoriesgiven the constraints of pointing for a particular sensor whileavoiding obstacles. This requirement requires the planner tohandle pre-specified as well as newly added obstacles.

Req 2.12. Low Altitude Terrain Feature Following andTrackingThis requirement requires the aircraft to follow a particularground feature, such as river outwashes, while payloads areconstraining the position of the aircraft in some manner.Generally, the trajectory planner and tracker must guide theaircraft along a feature being identified in real time by anoptical, Radar, or LIDAR, under arbitrary constraints whichmay be provided by the payloadisensor or other sources.For instance, the aircraft may be constrained to point abody-fixed sensor to track a feature while avoidingobstacles, or fly straight-and-level at a desired altitude aboveground level over undulating terrain.

Req 2.13. Low Altitude Urban AvoidanceThis requirement includes Req 2.11 and Req 2.12, butrequires consideration of the characteristics of urbanenvironments. This includes constraints for safety, safeflight termination zones, and sensors that can work on man-made obstacles such as thin power-lines.

Req 2.14. Low Altitude Ocean Navigation and AvoidanceThis requirement is similar to Req 2.11 through Req 2.13.Ocean navigation requires altitude tracking and obstacleavoidance. Such obstacles include large waves, nauticalvessels, and icebergs.

Req 2.15. Maneuvering in Extreme Weather ConditionsSeveral missions require maneuvering in severe wind,weather, and environmental conditions, such as surviving

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hurricane conditions in the Extreme Weather mission andthe Hurricane Genesis, Evolution, and Landfall nussion[38], Dirty and Dangerous Plume Measurements [34], orflight over active forest fires with dangerous and turbulentwind shear due to extreme thermal gradients [34]. The exactaccuracy needed depends on the particular mission.However, general approaches for robust control formulationand disturbance rejection may be critical for successfulcompletion of these missions. Information from otheraircraft, dropsondes, sonobuoys, and satellites will beneeded in addition to onboard pressure, temperature,humidity and LIDAR or Radar altimeters.

Req 2.16 Loitering while Sensor/Payload Data CollectingSeveral missions require loitering while collecting sensorand payload operations. Loitering is an endurance phase offlight that often seeks to maximize time over a given target.This requires minimal energy maneuvering with constraintsof fielding the payload sensors, which serves to maximizeavailable time over target during this phase of the mission.Optical cues could be incorporated for imagers, whileaircraft state data (GPS, INS) and an altimeter could be usedfor gas sampling.

Req 2.17. Dangerous and Dirty Phrme MeasurementsFlight through dirty plumes is highly problematic. Forinstance, air breathing engines can stall due to aerosolcontamination and oxygen starvation. Sensors; such as thosedesigned for atmospheric sampling, may become inoperabledue to an accumulation of residue and dirty contaminants.Strong updrafts and turbulent wind conditions can occur inthese regions in the presence of extreme thermal gradients.Additional navigation and communication sensors maybecome inoperable due to flight in this environment. Therequirement for measurements in dangerous and dirtyplumes must account for these issues. See section 1.2 abovefor a mission description.

Req 3.0. Multiple Vehicle Coordination

Req 3.1. General Formation Flying RequirementsA survey of the variety of techniques for implementingformation flight is well beyond the scope of this document.Techniques include simple swarming algorithms, follow theleader, energy and entropy techniques, distributed controlapproaches, etc.. Each of these approaches requires theirown set of requirements, including commnunicationhardware, networking requirements, specific sensors whichmust point and track other aircraft, etc. Precise specificationof these requirements is dependent on the approach chosen.

Req 3.2. Precision Sub-Meter Precision FlyingThe requirement for precision, sub-meter formation flyingwas specified as a `miracle' technology in [24] that would beof great value to the scientific community. The approachesto solve this problem may include highly accurate stateestimation sensors onboard formation flying aircraft, withlow latency point-to-point cornrnunication, or dedicated

high-precision sensors for ship to ship distance and vectormeasurements. The avionics system recently implementedon the NASA DFRC G-III in order to fly repeat passeswithin 10m of previous passes is the state of the art for thatclass of aircraft. This system was implemented to supportinterferometry from the UAVSAR.

Req 3.3. Tightly Coordinated Race-Track/Grid-PatternSamplingSeveral missions, including the radiation mission in [38].specified formation flying aircraft performing precisioncoordinated race-track and grid-search patterns. Theaccuracy needed depends on the particular missionrequirements. This accuracy requirement would be neededto determine the approach used for coordination.

Req 3.4. Vector-Based Formation FlyingSeveral mission scenarios require vector-based formations.There are several techniques for this approach, includingreliance on high-accuracy sensors and navigational aids,combined with real-time communication between aircraft.Other approaches include vision-based navigation, andRADAR based sensing of surrounding aircraft. Precisespecification of these requirements is dependent on theapproach chosen.

Req 3.5. Stacked Formation Maneuvers and FlyingStacked formation flying requires performing coordinatedmaneuvers, such as the requirements in Req 2.0, whilecoordinating at a low-level control, tactical, or strategiclevel with other vehicles in the team that are spaced atvertical intervals of a specific altitude-delta. As with otherformation flying requirements, precise specification of theserequirements is dependent on the approach chosen.

Req 3.6. Stacked Coordinated Closed-Loop ControlStacked control coordination requires vehicles at differentaltitudes to close the loop around remote sensors and datasets, as is required in missions such as the River Dischargemission [37]. This application was demonstrated in theMaldives in 2007 by Ramanathan (Science citation) using aMantas below, within, and above clouds in the same col uruito within a few meters.

Req 3.7. Autonomous Coordinated Control for MaximumData ReturnThese requirements, such as found in the River Dischargemission [37] and Southern Ocean mission [23], stem fromgeneric statements about the desire for a team of multipleunmanned platforms to share information and maxinuze datareturn given the available resources. The specificrequirements will depend on the approaches used toaccomplish this goal.

Req 3.8. Coordinated Spiral Descent/Ascent in VerticalColumnThis requirement is the same as Req 3.5. However, therequirement for vertical profiling or vertical traverse in a

vertical colunm is prevalent enough and unique enough thata specific formulation for this problem may be warranted, soit is included as its own requirement. As with otherformation flying requirements, precise specification of theserequirements is dependent on the approach chosen.

Req 3.9. Interactive UAV SwarmsVarious swanning algorithms have been proposed andimplemented in the literature. The use of UAV swamis isreferenced in the Southern Oceans mission in [23]. Manyalgorithms implementations require agent-based UAV's to beaware of the location of other UAV's either through specificpayload sensors or inter agent communication to sharesensor information. Agent to agent communication requiresefficient utilization of communication bandwidth.Alternatively, advanced networking approaches such asintroduced in [46] can be utilized to optimize networkusage.

Req 3.10. UAV Sensor- TandemsThe sensor tandem requirement (from such missions as theSoil Moisture and Freeze/Thaw States mission in [37])requires a team of UAVs with identical sensors to bedeployed in a area and coordinate their traversal tomaximize coverage of a specified over time, whileminimizing overlap. This general requirement may bemodified to some extent, given the requirements of themission. For instance, teams may be required to overlapsensor coverage within a given time window, in order tocorrelate sensor readings.

Req 4.0. Sensor and Daughtership DeploymentThe ability for aircraft to deploy sensors, sondes, bouys,daughterships, and small expendable platforms wasidentified as a `miracle' technology in [24] that would be ofgreat value to the scientific cominunity. Such a system hasbeen demonstrated by NRL in the Finder, which is deployedfrom the wing of Predator-class aircraft. Global Hawkflights over hurricanes beginning in 2010 will likely deliverdropsondes and so models of hurricane , GOES imagery,weather data from P-3 Hurricane Hunters, and low altitudeUAS will likely be used to optimize deployment.

Req 4.1. Autonomous Search, Identif ^, DeployThis requirement is specified in missions such as the Snow-Liquid Water Equivalent mission [37]. The unmannedautonomous platform must direct onboard payload sensorsto search a particular area for a specified feature. Thevehicle must deploy expendable or reusable sensor payloadsat precise locations. Deployment may occur inmiediately onrecognition, or after based on a certain criteria. Oncedeployed, the vehicles must act as data collectors and relaysfor the deployed sensors.

Req 4.2. Precision Release of Tracer Chemical AgentTracer-The requirement for tracking a chemical tracer comes fromthe Cloud and Aerosol mission outlined in [24]. This

requirement is the precursor to the tracer tracking in Req2.6. In this particular nussion, a tracer chemical is releasedin the boundary layer near the bottom of the in-flow regionand its transport is traced throu gh the cloud system to theout-flow region. The platform must determine the preciselocation for the chemical release. This mi ght be throughsampling, tracking around a sensor, etc. Once determined,the platform must reach a particular position/orientationwithin a time constraint, and release the agent at thisposition. The release actuation must be synchronized withonboard sensors and payloads.

Req 4.3. Mother/Daughtership Deployment and ControlThe Transport and Chemical Evolution in the Tropospheremission [39] species coordinated control of a multiple UAVsystem from a single mothership. The mothership containsextensive remote instrumentation, and will deploy multipledrones over a target area. The mothership will provideconnnunications, comunand and control of the drones with insitu instrumentation flying above and below. Thisparticular mission specification may be overly specified at tothe particulars of the nussion (for instance, the control maybe agent-based peer-to-peer, rather than a master-slaverelationship), but gives a general and very future lookingmission scenario.

Req 4.4. Mother/Daughtership RedockingThe ability for a mothership to deploy smaller vehicles andthen have these autonomous vehicle redock with themothership was specified as a `miracle' technology in [24]that would be of great value to the scientific community,Current state of the art is insufficient to pursue a missionwhich requires an aircraft to consistently deploy and retrievesmaller aircraft in a single mission. Robust and reliableredocking is an area of current research, and specificrequirements would be drawn from the specific approachused.

5. CONCLUSION AND FUTURE RESEARCH

Next generation aviation flight control concepts requireautonomous and intelligent control system architectures thatclose control loops directly around payload sensors inmanner more integrated and cohesive that in traditionalautopilot modes. This paper has presented a sampling ofvarious requirements and operating concepts for payloaddirected flight concepts. Presently, flight systems designedto perform payload-centric maneuvers require pre-constructed procedures and special hand-tuned guidancemodes. To enable intelligent maneuvering via strongcoupling between the goals of payload-directed fli ght andthe autopilot functions, there exists a need to rethinktraditional autopilot design and function. Continuingresearch into payload directed flight examines sensor andpayload-centric autopilot modes, architectures, andalgorithms that provide layers of intelli gent guidance,navigation and control for flight vehicles to achieve missiongoals related to the payload sensors, taking into account

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External/Remote 41Sensor Observations(e.g., Satellite Data) i= -

r'

External i UnmodeledSystem Theoretical Commands/

Models Objectives

Mission objectives Controller PlantNehicle

. JC1y wr Llr,. r,I^^I J^(.r,rr,r}bJ/

l Known/Controlled Sensors/Aerial Platform Observations

Payload Sensor —^Observations

Figure 1 — A Prototypical Payload Directed Flight Problem

various constraints such as the performance limitations ofthe aircraft, target tracking and estimation, obstacleavoidance, and constraint satisfaction.

A central problem to address in payload directed flight is tocontrol a known and controllable plant interacting with anexternal system based on payload and sensor data feedbackthat gives partial observation and understanding of theexternal system, to satisfy mission objectives and constraintson the combined system. This is shown conceptually in theblock diagram in Figure 1, where a controllable system iscoupled with an external system which may be un modeledor poorly modeled for various reasons. These reasons mayinclude complexity, uncertainty, lack of observability fromsensor to state, the size of the external system's state mayoverwhelm computational and modeling resources, or lackof available data to generate a model. A suite of sensorsprovide some set of observations into the system, and a setof mission objectives are defined concerning the combinedsystem. The PDF research objectives seek methods, tools,and techniques for designing controllers around these blocksto ensure the combined system meets mission objectiveunder varying constraints.

Current payload directed flight research at NASA AmesResearch Center focuses on near-optimal trajectorygeneration and flight control under varying constraints in ahighly dynamic environment, autonomous feature detectionand estimation, and modeless autopilot design concepts formulti-objective system control. Application of this researchis targeted towards increasing capabilities, performance, andefficiency in the execution of missions that require payload-directed and target-directed maneuvering, towards the goalof proliferation of this technology throughout the mannedand unmanned aviation sector.

6. REFERENCES

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[2] S. Rathinam, Z. Kim, A. So ghikian, R. Sengupta"VisionBased Following of Locally Linear Structures Using anUnmanned Aerial Vehicle", Proc. 44th IEEE Conferenceon Decision and Control. 2005-

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[4] H. Kim, D. H. Shim; S. Sastry; "Nonlinear ModelPredictive Tracking Control for Rotorcraft-basedUnmanned Aerial Vehicles," Proc. of the AmericanControl Confer., 2002.

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[6] A. Matsuura, S. Suzuki, M. Kono, A. Sakaguchi."Lateral Guidance Control of UAV using Feedback ErrorLearning," AIAA Infotech q^Aerospace, Rohnert Park,CA 2007.

[7] S. Rathinam, P. Almeida, Z. W. Kim, S. Jackson, ATinka, W. Grossman. R. Sengupta, "AutonomousSearching and Tracking of a River Using an UAV",Proc. of the American Control Conference, 2007.

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[15]A. Scheuer, Ch. Laugier, "Planning Sub-Optimal andContinuous-Curvature Paths for Car-Like Robots,"Proceedings of the 1998 IEEE/RSJInternationalConference on Intelligent Robots and Systems, Victoria,B.C., Canada, October 1998.

[ 16] J. K. Howlett, T. W. Mc lain, and M. A. Goodrich,"Learning Real-Time A* Path Planner for Unmanned AirVehicle Target Sensing," Journal of AerospaceComputing, Information, and Communication, Vol. 3,March 2006-

[17] J. J. Kehoe, A. S. Watkins, and R. Lind, "TrajectoryGeneration for Effective Sensing," AIAA Guidance,Navigation, and Control Conference and Exhibit, HiltonHead, SC, 2007.

[ 18]E. Frazzoli, M. Dahleh, and E. Ferron, "Real-timemotion planning for agile autonomous vehicles," inProceedings of the AIAA Guidance, Navigation, andControl Conference, (Denver, CO), August 2000. AIAAPaper No. AIAA-2000-4056.

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[22]Casbeer, D.W. Beard. R.W. McLain, T.W. Sai-Ming Li Mehra, R.K. Forest fire monitoring withmultiple small UAVs. American Control Conference,200.5. Proceedings of the 2005, 3530- 3535 vol. 5

[23]M. Fladeland et al ; Eddy flux measurements in theSouthern Ocean marine boundary layer using longduration, low-altitude robotic aircraft. NASA SuborbitalScience Missions of the Future, March 11, 2005

[24]M. Kurylo, Atmospheric Composition - Science FocusWork Group Products, NASA Suborbital ScienceMissions of the Future Workshop, July 10-12, 2004,Arlington, VA

[25]M Fladeland et al, A suborbital observation system formeasuring carbon flux over land and water,Infotech@Aerospace, 26 - 29 September 2005,Arlington, VA

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Yoo Hsiu Yeh is a Project Engineer atCarnegie-Mellon University West CoastCampus. She graduated with aB.S.E.E. from Stanford University in2006, and has been working with theExploration Aerial Vehicle project atNASA Ames Research Center. She is amember of the IEEE and AIAA.

[33]NASA Report to Committees on AppropriationsRegarding Potential Use of Unmanned Aircraft Systems(UAS) for NASA Science Missions, May 8, 2006,available at http:;;/suborbital.nasa.gov .

[34]M. Fladeland, Carbon Cycle, Ecosystems, andBiogeochemistry - Science Focus Work Group Products,NASA Suborbital Science Missions of the FutureWorkshop, July 10-12, 2004 ; Arlington, VA

[35]J. Boskovic, R. Prasanth, R. Mehra, "A Multi-LayerAutonomous Intelligent Control Architecture forUnmanned Aerial Vehicles", JOURNAL OFAEROSPACE COMPUTING, INFORMATION, ANDCOMMUNICATION. Vol. 1, December 2004

[36]J. LaBrecque, Earth Surface and Interior Structure-Science Focus Work Group Products, NASA SuborbitalScience Missions of the Future Workshop, July 10-12,2004, Arlington, VA

[37]J. Entin, Water and Energy - Science Focus WorkGroup Products, NASA Suborbital Science Missions ofthe Future Workshop ; July 10-12, 2004, Arlington, VA

[38]R. Hood, Weather - Science Focus Work GroupProducts, NASA Suborbital Science Missions of theFuture Workshop, July 10-12, 2004, Arlington, VA

[39]C Yuhas, Atmospheric Composition (Troposphere) -Science Focus Work Group Products ; INTEX SpecialSession of NASA Science Missions of the FutureWorkshop, July 13-15, 2004, New Hampshire

[40] S. Wegener et al. UAV Autonomous Operations forAirborne Science Missions. AIAA-2004-6416, AIAA3rd "Unmanned Unlimited" Technical Conference,Workshop and Exhibit, Chicago, Illinois, Sep. 20-23,2004

[41]R. Eckman, The Influence of the Sampling Time onDiffusion Measurements in the Atmosphere. Thesis(PH.D.)--THE PENNSYLVANIA STATEUNIVERSITY, 1989 . Source: Dissertation AbstractsInternational, Volume: 51-02, Section: B, page: 0799.

[42]J. Sonntag at al. Mission Concepts for UninhabitedAerial Vehicles in Cryospheric Science Applications.AIAA 2005-6921, Infotech@.Aerospace, 26 - 29September 200.5, Arlington, Virginia

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[45]T Veeraklaew and S Agrawal, "New ComputationalFramework for Trajectory Optimization of Higher-OrderDynamic Systems" JOURNAL OF GUIDANCE,CONTROL, AND DYNAMICS, Vol. 24, No. 2, March—April 2001

[46]C Ippolito, S Joo, K Al-Ali, Y Yeh, "Flight TestingPolymorphic Control Reconfiguration in an AutonomousUAV with UGV Collaboration", IEEE AerospaceConference, Big Sky, Montana USA, March 2008-

7. BIOGRAPHIES

Corey Ippolito is a Research Scientistand Aerospace Engineer at NASA Ames

I N ` Research Center, task lead for thepayload directed flight project, andrains the Exploration Aerial Vehicle labat Ames. An MSAE from Georgia Tech,Mr. Ippolito is recipient of the NASAAward of Excellence and the NASA

Team Achievement Award, with research interests thatinclude vehicle autonomy, control reconfiguration andprobabilistic methods in artificial intelligence. He is acontributing member of AIAA and IEEE, with aff nationsthat include the NASA Haughton-Mars Project, and theNASA Biologically-Inspired Engineering for ExplorationSystems for Mars project.

Matthew Fladeland is Manager of theAirborne Science Program Office at

a F̂ NASA Ames Research Center.Mr. Fladeland received an M.S. fromthe School of Forestry andEnvironmental Studies at YaleUniversity.

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