Safe Autonomous Flight Environment for the Notional First/Last 50 - - PowerPoint PPT Presentation

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Safe Autonomous Flight Environment for the Notional First/Last 50 - - PowerPoint PPT Presentation

Jan 10, 2024 46 likes •238 views

Safe Autonomous Flight Environment for the Notional First/Last 50 Feet (SAFE50) Project Toward UAS Operations in High-Density Low-Altitude Urban Environments Dr. Corey A. Ippolito Intelligent Systems Division NASA Ames Research Center

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Presented to the SAE / NASA Autonomy and Next Generation Flight Deck Symposium. Moffett Field, CA, USA. April 18-19, 2017

Safe Autonomous Flight Environment for the Notional “First/Last 50 Feet” (SAFE50) Project Toward UAS Operations in High-Density Low-Altitude Urban Environments

  • Dr. Corey A. Ippolito

Intelligent Systems Division NASA Ames Research Center Moffett Field, CA 94035

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Presented to the SAE / NASA Autonomy and Next Generation Flight Deck Symposium. Moffett Field, CA, USA. April 18-19, 2017

UAS Operations in High-Density Low-Altitude Urban Environments

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Unmanned Aircraft Systems (UAS) Traffic Management (UTM) concepts are advancing toward flight over populated regions. Significant technical challenges are imposed by these environments that makes traffic management difficult, particularly for low-altitude flight in high-density urban environments. Studies anticipate high demand and economic growth potential in this market. How do you facilitate routine, safe, and fair access to this high-demand airspace?

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SLIDE 3

Presented to the SAE / NASA Autonomy and Next Generation Flight Deck Symposium. Moffett Field, CA, USA. April 18-19, 2017

Motivating Scenarios

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Safe and Regular Access for sUAS to High-Density Low-Altitude Urban Airspaces

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SLIDE 4

Presented to the SAE / NASA Autonomy and Next Generation Flight Deck Symposium. Moffett Field, CA, USA. April 18-19, 2017

Challenges

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  • Low-altitude autonomous flight is inherently higher risk
  • Mixed-use airspace
  • Highly-constrained spaces within urban canyons
  • High-density environment
  • Other manned and unmanned airborne vehicles
  • Flight near and above high-valued assets
  • Cluttered wireless environment
  • Hazardous ambient conditions, precipitation, and adverse winds
  • Dynamic environment with significant uncertainty
  • Limited size, weight, and power (SWaP)
  • Regulations must establish acceptable risk posture and safety margins
  • Separation assurance (SA) and collision avoidance (CA) are difficult services to

provide

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Presented to the SAE / NASA Autonomy and Next Generation Flight Deck Symposium. Moffett Field, CA, USA. April 18-19, 2017 Risk to the vehicle Nominal Operations Risk Off-nominal (failure) risks

Consideration of Risks

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Dynamic Ground Objects (DGO) Static Ground Objects (SGO) Other Aircraft

Bilateral Risks

UAS

Nominal Risk (e.g., TO and Landing) Off-Nominal (Failure) Risk

Stakeholders (e.g., general public, operators, commercial entities, insurance companies, municipalities, certifying authorities, regulatory agencies)

Risks include potential damage, litigation, insurance costs, effects of vehicle/payload loss to businesses, etc.

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SLIDE 6

Presented to the SAE / NASA Autonomy and Next Generation Flight Deck Symposium. Moffett Field, CA, USA. April 18-19, 2017

SAFE50 Vehicle Autonomy Requirements

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Dynamic Ground Objects (DGO) Static Ground Objects (SGO) Other Aircraft

Detect, Operate- Near, and Avoid- Endangering SGOs Detect, Operate-Near, and Avoid-Endangering DGOs Hazard Footprint Awareness, Risk Minimization/Avoidance, Health Monitoring Detect, Operate-Near, Avoid-Endangering Other Aircraft

UAS Environment Challenges Atmospheric Uncertainty Failures and Contingencies Degraded RF, SAT-COM, GNSS Winds and microbursts Avoid endangering

  • bjects in environment.

Ground Operators and UTM System

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SLIDE 7

Presented to the SAE / NASA Autonomy and Next Generation Flight Deck Symposium. Moffett Field, CA, USA. April 18-19, 2017

Challenges for UAS in Urban Environments

  • Low reliability of current small UAS (high failure rates)
  • Significant variability in vehicle systems and technologies on the market
  • Limitations in current guidance, navigation, and control technologies
  • Inability to see-and-avoid
  • Limited onboard autonomy
  • Limited understanding of vehicle behavior and dynamics in this environ.
  • Limited onboard failure accommodation
  • Insufficient communications technologies for urban environments
  • Vehicle to ground, vehicle to vehicle, satellite coms, GNSS derived

PNT

  • Surveillance technologies are difficult to apply to this environment
  • There is no common set of vehicle-level and systems-level requirements yet

available for UAS in low-altitude urban flight.

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SLIDE 8

Presented to the SAE / NASA Autonomy and Next Generation Flight Deck Symposium. Moffett Field, CA, USA. April 18-19, 2017

Vehicle Autonomy

  • ‘Autonomy’ broadly generalized encompasses anything that allows systems to

sense, think, communicate, and react with less human intervention.

  • Research literature in UAS and vehicle autonomy is extensive, covering a broad

range of disciplines and techniques, and touching on all of the challenges and limitations we have identified to some degree.

  • Substantial levels of private/commercial R&D investments are targeted toward

advancing vehicle autonomy technology.

  • While the technology is rapidly advancing, there are still severe limitations in

commercially available off-the-shelf (COTS) technologies and UAS vehicle systems.

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SLIDE 9

Presented to the SAE / NASA Autonomy and Next Generation Flight Deck Symposium. Moffett Field, CA, USA. April 18-19, 2017

SAFE50 Project Goals and Approach

  • Conduct an advanced study focusing on onboard vehicle-centric autonomy

requirements that will allow safe, autonomous and routine sUAS access to high- density low-altitude urban environments, and integrates into the emerging UTM framework.

  • Advanced study will guide the next phase for a larger systems-level study
  • Develop feasible point-designs for system-level and vehicle-level concept
  • Develop prototypes and demonstrate feasibility of point-design
  • Assemble and develop analysis tools
  • Validated high-fidelity sims, software/hardware prototypes, flight vehicles
  • Analyze effectiveness of the point-design in addressing technical challenges
  • Leverage UTM partnerships to track emerging trends, technologies, gaps
  • Work with academia and industry towards enabling urban area access
  • Peer-reviewed and competed awards, encouraging academic/commercial

partnerships, see announcements at https://nspires.nasaprs.com/

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SLIDE 10

Presented to the SAE / NASA Autonomy and Next Generation Flight Deck Symposium. Moffett Field, CA, USA. April 18-19, 2017

Research Highlights

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Presented to the SAE / NASA Autonomy and Next Generation Flight Deck Symposium. Moffett Field, CA, USA. April 18-19, 2017

Dynamics Modeling and Simulation

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Credit: Tim Sandstrom, NASA Ames Research Center

Using computational fluid dynamics and wind tunnel experiments to created higher-fidelity and validated flight dynamics models.

Vehicle Testing in 7x10 ft Wind Tunnel Courtesy of Carl Russel, UTM, NASA Ames Research Center Simulation Models

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SLIDE 12

Presented to the SAE / NASA Autonomy and Next Generation Flight Deck Symposium. Moffett Field, CA, USA. April 18-19, 2017

Autonomous Sensor Fusion, Environment Mapping and Hazard Characterization

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Powerline Identification and Reconstruction. Raw LiDAR point clouds (left), voxel processing (middle), reconstructed powerlines at 75m (right).

Environment Mapping Evaluations (LiDAR and Vision)

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Presented to the SAE / NASA Autonomy and Next Generation Flight Deck Symposium. Moffett Field, CA, USA. April 18-19, 2017

GNSS/GPS Denied and Degraded Environments

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Investigating integrated GNSS, LiDAR and vision for robust simultaneous localization and mapping (SLAM)

Vision-Based SLAM – NASA NUARC Test Facility LiDAR SLAM in NASA Disaster Assistance and Rescue Team (DART) Training Facility LiDAR SLAM in NASA RoverScape Test Facility (collaboration with Near-Earth Autonomy, Inc.)

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SLIDE 14

Presented to the SAE / NASA Autonomy and Next Generation Flight Deck Symposium. Moffett Field, CA, USA. April 18-19, 2017

Natural Terrain Multi-Species Wind Modeling and Estimation under Uncertainty

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Swarming Dragon-Eye Volcanic Plume Monitoring Project - CFD study investigated SO2, CO2, and water vapor plume transport at anticipated emission rates

  • ver the Turrialba Volcano in Costa Rica.
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SLIDE 15

Presented to the SAE / NASA Autonomy and Next Generation Flight Deck Symposium. Moffett Field, CA, USA. April 18-19, 2017

Urban Environment Wind Uncertainties

15 Urban Architecture and CFD Simulation of Wind Profiles.

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Presented to the SAE / NASA Autonomy and Next Generation Flight Deck Symposium. Moffett Field, CA, USA. April 18-19, 2017 16

UrbanScape Wind Uncertainties

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Presented to the SAE / NASA Autonomy and Next Generation Flight Deck Symposium. Moffett Field, CA, USA. April 18-19, 2017

Autonomy Payload Architecture and Prototyping

Var: objJoyPanel DLL: joypanel.dll m_dt_sec m_dataset1_.. m_dataset0_..(gui) m_dataset2_..(a/p) m_ouputdata_.. m_data_accelXYZ_fps2 m_data_airspeed_fps m_data_attackAngle_rad m_data_baroAltitude_ft m_data_eulerRPY_rad m_data_flightPathAngle_ra m_data_gpsAltitude_ft m_data_headingTrue_rad m_data_hwstate m_data_latitude_rad m_data_longitude_rad m_data_pos_ENUft m_data_rateEulerRPY_radps m_data_ratePQR_ba_radps m_data_sideslip_rad m_data_vel_bafps m_data_vel_ENUfps m_data_velocityMagnitude_ m_dt_sec m_in_Ml2w m_in_velocityAngular_ENU_ m_in_velocityLinear_ENU_f m_in_windVelocity_ENUfps m_Ms2l m_data_latitude_rad m_data_longitude_rad m_data_gpsAltitude_ft m_data_headingTrue_rad m_data_eulerRPY_rad[0] m_data_eulerRPY_rad[1] m_motor0_ucmd_0p1 m_motor1_ucmd_0p1 m_motor2_ucmd_0p1 m_motor3_ucmd_0p1 m_motor4_ucmd_0p1 m_motor5_ucmd_0p1 m_motor6_ucmd_0p1 m_motor7_ucmd_0p1 m_motor0_Speed_radps m_motor0_AngPos_rad m_motor1_Speed_radps ...
  • utput_position_north_ft
  • utput_position_east_ft
  • utput_altitude_ft
  • utput_velocity_mag_fps
  • utput_velocityW_wa_fps
  • utput_acceleration_y_fps2
  • utput_pitchEuler_rad
  • utput_rollEuler_rad
  • utput_heading_True_rad
CmdAileron CmdElevator CmdThrottle CmdRudder Ml2w
  • utput_velocity_wa_fps
  • utput_angVelocity_wa_radps
m_in_Ml2w m_in_velocityLinear_ENU_fps m_in_velocityAngular_ENU_radps Var: objOcto DLL: octocopter.dll Var: objOctoIMU DLL: imuemulator.dll Var: objOctoRendObj DLL: RendObj (scenevis.dll) Ml2w Var: _octocopter_map_H01 DLL: ProcAnimNode Node: Helice_H01" m_inRotation_rad[0] Var: objJoy DLL: directplay.dll m_dt_sec m_analogBuffer[] m_buttonBuffer[] m_sensorInput_airspeed_mps m_sensorInput_pos_east_ENU_m m_sensorInput_pos_north_ENU_m m_sensorInput_pos_up_ENU_m m_sensorInput_roll_rad m_sensorInput_pitch_rad m_sensorInput_heading_rad m_out_prevWpPos_ENU_m m_out_nextWpPos_ENU_m m_out_nextWpSpd_mps m_status_nWaypoints m_status_isEngaged m_status_currentWaypoint m_status_timeInCommand_se m_status_WaypointCommand Var: objFMS DLL: fms.dll m_inputs_stick[..] m_outputs_motorCmds_m1p1[0] m_outputs_motorCmds_m1p1[..] m_outputs_motorCmds_m1p1[7] m_sense_roll_rad m_sense_pitch_rad m_sense_hdg_rad m_intrm_LMNFcmd[3] m_intrm_aglCmd_m m_intrm_hdgCmd_rad m_intrm_pitchCmd_rad m_intrm_rollCmd_rad m_intrm_Ucmds_m1p1[4] m_in_prevWpPos_ENU_m m_in_nextWpPos_ENU_m m_in_nextWpSpd_mps Var: objAP DLL: octocontrol.dll Var: objOctoINS DLL: octoins.dll Var: (sensor emulator(s)) Var: (environment model(s)) Var: (Integrated INS solution) Var: (Taemin s combinator) Var: (sensor emulator(s)) Var: (environment model(s)) Var: (Integrated INS solution) Var: (sensor emulator(s)) Var: (environment model(s)) Var: (Integrated INS solution) Var: (sensor emulator(s)) Var: (environment model(s)) Var: (Integrated INS solution) Mca Lcmd PID Lcmd ferr S fest fref Roll Cmd Ref Filter (2nd order) Roll Cmd Limiter Lateral Modes LATMODE_STICKCMD LATMODE_ROLLZERO LATMODE_ROLLSTICKCMD LATMODE_ROLLCMD LATMODE_GNDSPDHOLD LATMODE_TRACKTOWP fcmd_hold fcmd_gndspd Mwa2lhv Vgn dref_lhv[2] Vgndcmd_wp_wa[2] S Vgn d_lhv_est[2] Verr[2] Roll to Moment (L) GndSpd to Roll PID Vgnd Cmd Ref Filter (2nd order) VGnd Cmd Limiter Vgn dref_wa[2] stickLat_m1p1 K L Cmd Limiter Mcm d PID Mcmd qerr S qest qref Pitch Cmd Ref Filter (2nd order) Pitch Cmd Limiter qcmd_hold qcmd_gndspd S Vgn d_lhv_est[2] Verr[2] Pitch to Moment (M) GndSpd to Pitch PID Stick Cmd Filter (1st order) K Mstickcmd M Cmd Limiter Vgn dref_lhv_y Vgn dref_lhv_x Longitudinal Modes LONMODE_STICKCMD LONMODE_PITCHCMD LONMODE_GNDSPDHOLD Heading Modes HDGMODE_STICK_N_CMD HDGMODE_HDG_ZERO HDGMODE_STICK_HDG_CMD HDGMODE_HDG_CMD Stick Cmd Filter (1st order) K Ncmd Ncmd N Cmd Limiter PID yerr yest Yaw to Moment (N) yref Hdg Cmd Ref Filter (2nd order) Ncmd Hdg Cmd Limiter Yaw Error Control Allocation (u=Mca*[LMNFz] ) Fzcmd U[1..8] Lref Nref Mref stickLon_m1p1 StickRdr_m1p1 Stick to L Ksticklat2L Kstlon Stick to Rate Stick to Rate KstickRdr2N Lcmd Thrust Modes THRMODE_STICK_THR_CMD THRMODE_VSPD_CMD THRMODE_ALT_AGL_HOLD THRMODE_ALT_MSL_HOLD hcmd_agl Alt Cmd Ref Filter (2nd order) hcmd_msl Alt. Cmd Limiter href Altitude Error to Thrust h_agl_est S MSL/AGL h_msl_est MSL/AGL herr PID Vert Speed Limiter dvspd S vspdref vspdcmd ALT/VSPD PID VSPD to FZ vspdref S Fzcmd StickThr_m1p1 Fz Cmd Limiter (0,+1) m_motorCmds[8] m_inputGearCmd (0,1) m_outGearServoCmd_pwm Convert m1p1 to pwm Top: LATMODE_STICKCMD Bot: Others Rate Limiter w/(s+w); M1p1 Limiter ; PWM convert Normalize (-1,+1) Rate Limiter w/(s+w) Kstthr y=(x+1)/2
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Note: +stick fwd means -pitch down Note: +stick right means +roll ri gh t Note: +stick is rudder right, +rotation about Z U0 U1 U2 U3 U4 U5 U6 U7 =
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S
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T L M N Z Control Allocation Matrix fCmd=0 fCmd rollCmdLimit_rad qCmd=0 Lim it ra nge from - PI to +PI safeHoldAlt_m_agl AGL Limiter AGLCmdLim itM in_m AGLCmdLim itMax_m PID AGL to Fz aglErr S aglCmd fzcmd fzCmdM in_0p1 Ksticklat2Roll K fCmdStick m_intrm. hdgCmd_rad StickRdr_m1p1 m_params. KstickRdr2Hdg Stick to HDG K m_inputs.headingCmd_rad m_inputs.prevWP_ENU m_inputs.nextWP_ENU Calculate Track-To XtrackErr xtrackerr Xtrack to Roll rollcmd A/C Position LATMODE_TRACKTOW P LATMODE_GNDSPD Calculate Track-To HdgCmd

Experimental Multicopter Flight Management and Flight Control System Real-Time Embedded Software Architecture Flight Testing - NASA SAFE50 and UTM Flight Test - August 2015 Autonomy Architecture Systems Analysis, CAD and Hardware Design Integrated Payload/Vehicle Test Platform

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SLIDE 18

Presented to the SAE / NASA Autonomy and Next Generation Flight Deck Symposium. Moffett Field, CA, USA. April 18-19, 2017

Flight System Interfaces and Ground Control System Development

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Hardware-In-The-Loop Vehicle Simulation Configuration Ground Control Stations Custom GCS Control Interfaces Multi-Vehicle Simulation Integration - Airspace Operations Laboratory (AOL) NASA Ames Research Center

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SLIDE 19

Presented to the SAE / NASA Autonomy and Next Generation Flight Deck Symposium. Moffett Field, CA, USA. April 18-19, 2017

Safe Autonomous Flight Environment for the Notional “First/Last 50 Feet” (SAFE50) Project Toward UAS Operations in High-Density Low-Altitude Urban Environments

  • Dr. Corey A. Ippolito

Intelligent Systems Division NASA Ames Research Center Moffett Field, CA 94035