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Vision-based Landing of an Unmanned Air Vehicle

This research focuses on developing a vision-based control system for autonomous landing of unmanned air vehicles (UAVs) on ship decks. The goal is to overcome challenges such as hostile environments, ground effect, pitching deck, and high winds. The use of a passive vision sensor allows for observation of relative motion and enables accurate tracking and landing. The simulation and experimental results demonstrate the effectiveness of the vision-based control system.

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Vision-based Landing of an Unmanned Air Vehicle

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  1. Vision-based Landing of an Unmanned Air Vehicle Omid Shakernia Department of EECS, UC Berkeley

  2. Predator Global Hawk UCAV X-45 SR/71 Fire Scout Applications of Vision-based Control

  3. Goal: Autonomous landing on a ship deck Challenges • Hostile environments • Ground effect • Pitching deck • High winds, etc Why vision? • Passive sensor • Observes relative motion

  4. Simulation: Vision in the loop

  5. Vision-Based Landing of a UAV • Motion estimation algorithms • Linear, nonlinear, multiple-view • Error: 5cm translation, 4° rotation • Real-time vision system • Customized software • Off-the-shelf hardware • Vision in Control Loop • Landing on stationary deck • Tracking of pitching deck

  6. Vision-based Motion Estimation Current pose Image plane Feature Points Pinhole Camera Landing target

  7. Pose Estimation: Linear Optimization • Pinhole Camera: • Epipolar Constraint: • Planar constraint: • More than 4 feature points • Solve linearly for • Project onto to recover

  8. Pose Estimation: Nonlinear Refinement • Objective: minimize error • Parameterize rotation by Euler angles • Minimize by Newton-Raphson iteration • Initialize with linear algorithm

  9. Pinhole Camera Multiple View Matrix Rank deficiency constraint Multiple-View Motion Estimation

  10. Multiple-View Motion Estimation • n points in m views • Equivalent to finding s.t. • Initialize with two-view linear solution • Least squared solution: • Use to linearly solve for • Iterate until converge

  11. UAV Pan/Tilt Camera Onboard Computer Real-time Vision System • Ampro embedded Little Board PC • Pentium 233MHz running LINUX • 440 MB flashdisk HD robust to vibration • Runs motion estimation algorithm • Controls Pan/Tilt/Zoom camera • Motion estimation algorithms • Written and optimized in C++ using LAPACK • Estimate relative position and orientation at 30 Hz

  12. On-board UAV Vision System Vision Computer Camera RS232 Vision Algorithm WaveLAN to Ground Frame Grabber RS232 Navigation System Navigation Computer INS/GPS RS232 RS232 WaveLAN to Ground Control & Navigation Hardware Configuration

  13. Feature Extraction • Acquire Image • Threshold Histogram • Segmentation • Target Detection • Corner Detection • Correspondence

  14. Camera Control • Pan/Tilt to keep features in image center • Prevent features from leaving field of view • Increased Field of View • Increased range of motion of UAV

  15. Ground Station Comparing Vision with INS/GPS

  16. Motion Estimation in Real Flight Tests

  17. Landing on Stationary Target

  18. Tracking Pitching Target

  19. Conclusions • Contributions • Vision-based motion estimation (5cm accuracy) • Real-time vision system in control loop • Demonstrated proof of concept prototype: first vision-based UAV landing • Extensions • Dynamic vision: Filtering motion estimates • Symmetry-based motion estimation • Fixed-wing UAVs: Vision-based landing on runways • Modeling and prediction of ship deck motion • Landing gear that grabs ship deck • Unstructured environments: Recognizing good landing spots (grassy field, roof top etc)

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