aerial manipulator

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Introduction to Aerial manipulator (a.k.a. Dronipulator) Presenter : Jangmyung Lee

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Page 1: Aerial manipulator

Introduction to Aerial manipulator (a.k.a. Dronipulator)

Presenter : Jangmyung Lee

Page 2: Aerial manipulator

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

Page 3: Aerial manipulator

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

Page 4: Aerial manipulator

Research background and motivation

<Video #1: Various kinds of aerial manipulators>

Page 5: Aerial manipulator

Research background and motivation

<Fukushima nuclear power plant disaster in 2011>

Page 6: Aerial manipulator

Research background and motivation

Page 7: Aerial manipulator

Research background and motivation

Page 8: Aerial manipulator

Research background and motivation

All of th

e finally qualifie

d teams(in 2016) are humanoids!!

But biped humanoid robot is not optim

ized structure

for carrying out D

RC competitions!!

Page 9: Aerial manipulator

Research background and motivation

<Video #2: Humanoid robots falling down at DRC in 2015 >

Page 10: Aerial manipulator

Research background and motivation

<Video #3: Valve turning using a dual arm aerial manipulator>

Page 11: Aerial manipulator

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

Page 12: Aerial manipulator

Research trends in chronological order

Structure construction, University of Pensylvania, 2011~

Avian Inspired Grasping, University of Pensylvania, 2013~

FP7 ARCAS, DLR 2012

Page 13: Aerial manipulator

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

Page 14: Aerial manipulator

Research trends in chronological order

Cooperative bar transportation, Seoul National University, 2015

Parallel aerial manipulator, University of Nevada, 2015

Johns Hopkins University, 2015

Page 15: Aerial manipulator

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

Page 16: Aerial manipulator

Research trends in chronological order

FP7 ARCAS, CATEC, 2015

H2020 AEROARMS, Univ. Sevilla 2016

H2020 AEROBI, Univ. Sevilla 2016

Page 17: Aerial manipulator

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

Page 18: Aerial manipulator

Robust image stabilization using optical flow and IMU

<Vibration compensated image><Vibrated image>

Optical flow

compensation

Page 19: Aerial manipulator

Robust image stabilization using optical flow and IMU

<Stereo matching using multiple view geometry and its 3D reconstruction>

Page 20: Aerial manipulator

Robust image stabilization using optical flow and IMU

<Stereo images and feature matching> <Depth image>

<Augmented 3D image>

Page 21: Aerial manipulator

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)

Page 22: Aerial manipulator

Tight grasping based on compliance control

<Various kinds of target objects>

Page 23: Aerial manipulator

Tight grasping based on compliance control

<Effective force to target object during grasping>

<Various complex tasks with high manipulability>

Page 24: Aerial manipulator

Stable hovering even under severe weight change

Dynam

ic modeling

Dynam

ic modeling

Page 25: Aerial manipulator

Stable hovering even under severe weight change

<Typical hovering PD control algorithm>

<Proposed H/W structure for aerial manipulator>

<Overall controller architecture>

Page 26: Aerial manipulator

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>

Page 27: Aerial manipulator

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’

Page 28: Aerial manipulator

Stable landing based on compliance control

<Typical marker based landing algorithm using CamShift>Original HSF filtering Erosion Dialation

Page 29: Aerial manipulator

Stable landing based on compliance control

Reference : ‘VISION ANALYSIS SYSTEM FOR AUTONOMOUS LANDING OF MICRO DRONE’<Simple landing strategy with predefined marker>

Page 30: Aerial manipulator

Stable landing based on compliance control

<Experimental result with most advanced conventional landing algorithm>Reference : ‘On Autonomous Landing of AR.Drone: Hands-on Experience’

Page 31: Aerial manipulator

SWOT analysis

Positive Negative

High mobility & high manipulability

Vulnerable for external disturbances

Various kinds of budgets Lots of regulations

Strength Weakness

Opportunity Threat

Inte

rnal

fact

orEx

tern

al fa

ctor

Think and discuss with your own ideas and solutions!

Page 32: Aerial manipulator

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

Page 33: Aerial manipulator

References1. 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