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 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
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
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 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!!
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 project University of Twente
2011-2014
FP7 ARCAS, CATEC 2012
Research trends in chronological order
Structure construction, University of Pensylvania, 2011~
Avian Inspired Grasping, University of Pensylvania, 2013~
FP7 ARCAS, DLR 2012
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
Research trends in chronological order
Cooperative bar transportation, Seoul National University, 2015
Parallel aerial manipulator, University of Nevada, 2015
Johns Hopkins University, 2015
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
Research trends in chronological order
FP7 ARCAS, CATEC, 2015
H2020 AEROARMS, Univ. Sevilla 2016
H2020 AEROBI, Univ. Sevilla 2016
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
Robust image stabilization using optical flow and IMU
<Vibration compensated image><Vibrated image>
Optical flow
compensation
Robust image stabilization using optical flow and IMU
<Stereo matching using multiple view geometry and its 3D reconstruction>
Robust image stabilization using optical flow and IMU
<Stereo images and feature matching> <Depth image>
<Augmented 3D image>
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)
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 high manipulability>
Stable hovering even under severe weight change
Dynam
ic modeling
Dynam
ic modeling
Stable hovering even under severe weight change
<Typical hovering PD control algorithm>
<Proposed H/W structure for aerial manipulator>
<Overall controller architecture>
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>
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’
Stable landing based on compliance control
<Typical marker based landing algorithm using CamShift>Original HSF filtering Erosion Dialation
Stable landing based on compliance control
Reference : ‘VISION ANALYSIS SYSTEM FOR AUTONOMOUS LANDING OF MICRO DRONE’<Simple landing strategy with predefined marker>
Stable landing based on compliance control
<Experimental result with most advanced conventional landing algorithm>Reference : ‘On Autonomous Landing of AR.Drone: Hands-on Experience’
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!
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
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
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