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Introduction to Aerial manipulator
(a.k.a. Dronipulator)
Presenter : Jangmyung Lee
Table of contents
1. Research background & motivation
2. Latest trends in chronological order
3. Market analysis and SWOT
...
Research background and motivation
<Mobile manipulator>
<Drone>
Manipulation in 2D
Mobility in 3D Manipulation in 3D(Actua...
Research background and motivation
<Video #1: Various kinds of aerial manipulators>
Research background and motivation
<Fukushima nuclear power plant disaster in 2011>
Research background and motivation
Research background and motivation
Research background and motivation
All of the finally qualified teams(in 2016) are humanoids!!
But biped humanoid robot is...
Research background and motivation
<Video #2: Humanoid robots falling down at DRC in 2015 >
Research background and motivation
<Video #3: Valve turning using a dual arm aerial manipulator>
Research trends in chronological order
Aerial grasping, Yale Univ., 2011
Maintain contact and pushing,
FP7 AIRobots projec...
Research trends in chronological order
Structure construction,
University of Pensylvania, 2011~
Avian Inspired Grasping,
U...
Research trends in chronological order
FP7 ARCAS, University of Sevilla 2014~
FP7 ARCAS, CATEC 2014
Manipulation with two ...
Research trends in chronological order
Cooperative bar transportation,
Seoul National University, 2015
Parallel aerial man...
Research trends in chronological order
Opening a door,
Tokyo Institute of Technology, 2015
Operating an Unknown Drawer,
Se...
Research trends in chronological order
FP7 ARCAS, CATEC, 2015
H2020 AEROARMS, Univ. Sevilla 2016
H2020 AEROBI, Univ. Sevil...
Key technologies for Dronipulator to carry out moving object
1. Image stabilization using optical flow and IMU
2. Integrate...
Robust image stabilization using optical flow and IMU
<Vibration compensated image><Vibrated image>
Opticalflowcompensation
Robust image stabilization using optical flow and IMU
<Stereo matching using multiple view geometry and its 3D reconstructi...
Robust image stabilization using optical flow and IMU
<Stereo images and feature matching> <Depth image>
<Augmented 3D imag...
Tight grasping based on compliance control
<Multi-purpose gripper for tight grasping>
Assumption
• Trajectory planning and...
Tight grasping based on compliance control
<Various kinds of target objects>
Tight grasping based on compliance control
<Effective force to target object during grasping>
<Various complex tasks with h...
Stable hovering even under severe weight change
Dynamicmodeling
Dynamicmodeling
Stable hovering even under severe weight change
<Typical hovering PD control algorithm>
<Proposed H/W structure for aerial...
Stable hovering even under severe weight change
<Block diagram of Fuzzy logic controller for stable hovering>
<Block diagr...
Stable hovering even under severe weight change
<Performance for each controller for drone’s hovering algorithm>
Reference...
Stable landing based on compliance control
<Typical marker based landing algorithm using CamShift>
Original HSF filtering E...
Stable landing based on compliance control
Reference : ‘VISION ANALYSIS SYSTEM FOR AUTONOMOUS LANDING OF MICRO DRONE’
<Sim...
Stable landing based on compliance control
<Experimental result with most advanced conventional landing algorithm>
Referen...
SWOT analysis
Positive Negative
High mobility &
high manipulability
Vulnerable for external
disturbances
Various kinds of ...
Conclusions and future works
• Future cargo transportation system without pilots
• Aerial manipulator should overcome :
• ...
References
1. Vision-based Autonomous Control and Navigation of a UAV
2. Vision-Based Object Tracking Algorithm With AR. D...
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Aerial manipulator

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Introduction to Aerial manipulator.

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Aerial manipulator

  1. 1. Introduction to Aerial manipulator (a.k.a. Dronipulator) Presenter : Jangmyung Lee
  2. 2. Table of contents 1. Research background & motivation 2. Latest trends in chronological order 3. Market analysis and SWOT 4. Key technologies of aerial manipulator 1. Robust image stabilization using optical flow and IMU 2. Integrated trajectory planning of drone and manipulator 3. Tight grasping from feature extraction 4. Stable hovering even under severe weight change 5. Battery management system using optimal control 6. Stable landing based on compliance control 5. Some interesting video clips 6. Conclusions and future works
  3. 3. Research background and motivation <Mobile manipulator> <Drone> Manipulation in 2D Mobility in 3D Manipulation in 3D(Actually in 6D) <Prototype Dronipulator designed by IRL> Integration
  4. 4. Research background and motivation <Video #1: Various kinds of aerial manipulators>
  5. 5. Research background and motivation <Fukushima nuclear power plant disaster in 2011>
  6. 6. Research background and motivation
  7. 7. Research background and motivation
  8. 8. Research background and motivation All of the finally qualified teams(in 2016) are humanoids!! But biped humanoid robot is not optimized structure for carrying out DRC competitions!!
  9. 9. Research background and motivation <Video #2: Humanoid robots falling down at DRC in 2015 >
  10. 10. Research background and motivation <Video #3: Valve turning using a dual arm aerial manipulator>
  11. 11. Research trends in chronological order Aerial grasping, Yale Univ., 2011 Maintain contact and pushing, FP7 AIRobots project University of Twente 2011-2014 FP7 ARCAS, CATEC 2012
  12. 12. Research trends in chronological order Structure construction, University of Pensylvania, 2011~ Avian Inspired Grasping, University of Pensylvania, 2013~ FP7 ARCAS, DLR 2012
  13. 13. Research trends in chronological order FP7 ARCAS, University of Sevilla 2014~ FP7 ARCAS, CATEC 2014 Manipulation with two hands, University of Zagreb, 2014~ 3D Printing, Imperial College, 2014
  14. 14. Research trends in chronological order Cooperative bar transportation, Seoul National University, 2015 Parallel aerial manipulator, University of Nevada, 2015 Johns Hopkins University, 2015
  15. 15. Research trends in chronological order Opening a door, Tokyo Institute of Technology, 2015 Operating an Unknown Drawer, Seoul,National University, 2015 FP7 ARCAS, DLR, 2015
  16. 16. Research trends in chronological order FP7 ARCAS, CATEC, 2015 H2020 AEROARMS, Univ. Sevilla 2016 H2020 AEROBI, Univ. Sevilla 2016
  17. 17. Key technologies for Dronipulator to carry out moving object 1. Image stabilization using optical flow and IMU 2. Integrated trajectory planning of drone and manipulator 3. Precise position and velocity control for aerial manipulator 4. Obstacle avoidance scheme 5. Tight grasping based on compliance control 6. Stable hovering even under severe weight change 7. Battery management system using optimal control 8. Real-time SLAM using visual odometry 9. Stable landing based on compliance control gray : not stated in this keynote black : stated from next slide
  18. 18. Robust image stabilization using optical flow and IMU <Vibration compensated image><Vibrated image> Opticalflowcompensation
  19. 19. Robust image stabilization using optical flow and IMU <Stereo matching using multiple view geometry and its 3D reconstruction>
  20. 20. Robust image stabilization using optical flow and IMU <Stereo images and feature matching> <Depth image> <Augmented 3D image>
  21. 21. Tight grasping based on compliance control <Multi-purpose gripper for tight grasping> Assumption • Trajectory planning and control of manipulator has been done. • Don’t care about manipulator and gripper’s energy consumption. • Dynamics contains relatively small modeling error, can be treated as disturbances for robust controller • Object’s 3D coordinate doesn’t contain high frequency noises from body’s fluctuations (Perfect compensation using previous section)
  22. 22. Tight grasping based on compliance control <Various kinds of target objects>
  23. 23. Tight grasping based on compliance control <Effective force to target object during grasping> <Various complex tasks with high manipulability>
  24. 24. Stable hovering even under severe weight change Dynamicmodeling Dynamicmodeling
  25. 25. Stable hovering even under severe weight change <Typical hovering PD control algorithm> <Proposed H/W structure for aerial manipulator> <Overall controller architecture>
  26. 26. Stable hovering even under severe weight change <Block diagram of Fuzzy logic controller for stable hovering> <Block diagram of Sliding mode controller for stable hovering>
  27. 27. Stable hovering even under severe weight change <Performance for each controller for drone’s hovering algorithm> Reference : ‘A Review of Control Algorithms for Autonomous Quadrotors’
  28. 28. Stable landing based on compliance control <Typical marker based landing algorithm using CamShift> Original HSF filtering Erosion Dialation
  29. 29. Stable landing based on compliance control Reference : ‘VISION ANALYSIS SYSTEM FOR AUTONOMOUS LANDING OF MICRO DRONE’ <Simple landing strategy with predefined marker>
  30. 30. Stable landing based on compliance control <Experimental result with most advanced conventional landing algorithm> Reference : ‘On Autonomous Landing of AR.Drone: Hands-on Experience’
  31. 31. SWOT analysis Positive Negative High mobility & high manipulability Vulnerable for external disturbances Various kinds of budgets Lots of regulations Strength Weakness Opportunity Threat InternalfactorExternalfactor Think and discuss with your own ideas and solutions!
  32. 32. Conclusions and future works • Future cargo transportation system without pilots • Aerial manipulator should overcome : • Strictly limited payload • Flight endurance due to battery capacity • Lots of complex regulations and laws • Posture control problems including tele-operation • Some other related research topics • Real-time SLAM including precise 3D localization • Optimal posture with minimal energy consumption • Coordination with multiple aerial manipulators • Wireless Comm. protocol and topology for aerial manipulator
  33. 33. References 1. Vision-based Autonomous Control and Navigation of a UAV 2. Vision-Based Object Tracking Algorithm With AR. Drone 3. VISION ANALYSIS SYSTEM FOR AUTONOMOUS LANDING OF MICRO DRONE 4. Autonomous Landing for a Multirotor UAV Using Vision 5. Quadrotor prototype 6. VISION ANALYSIS SYSTEM FOR AUTONOMOUS LANDING OF MICRO DRONE 7. Full Control of a Quadrotor 8. Quadcopter Dynamics, Simulation, and Control 9. Autonomous Fixed-Point Landing for Quadrotor Aerial Vehicles 10.Vision Based Algorithm for Automatic Landing System of Unmanned Aerial Vehicles: A Review

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