Aníbal Ollero - IEEE Aerial Robotics and UAVs

Aerial Robotics:

Challenges and Opportunities

Aníbal Ollero

Professor and Head of GRVC Universidad de Sevilla

(Spain)

[email protected]

Scientific Advisor of the Center for Advanced Aerospace

Technologies (Sevilla, Spain)

[email protected]

mailto:[email protected]



Outline

General challenges

Manipulation challenges

Energy and safety: GRIFFIN

Conclusions

A. Ollero. IROS 2018. Key Note, Challenges inAerial Robotic Manipulation October3, 2018

6/1/2019 2Workshop: The Future of Aerial Robotics: Challenges and Opportunities. May 23. ICRA 2019Anibal Ollero. Aerial Robotic Challenges and Opportunities

Workshop: The Future of Aerial Robotics: Challenges and Opportunities. May 23. ICRA 2019Anibal Ollero. Aerial Robotic Challenges and Opportunities

Low High ChallengesRange Short Long Energy

Line of Sight VLOS BVLOS

AutonomyGNSS Available Denied

Comms Available Denied

Interactions Information Physical Soft

Challenges


Cross effects

Energy Autonomy Soft

Energy • Autonomousrefuelling, perching, ……

• Wind efficiency

• Morphing

Autonomy • Increasing on-boardcapabilities

• Embeddedsensing and actuation

Soft • Increaseactuatorresponse

Adaptivecompliance

Challenges

The application needs: work at height

Aerial robot with physical interaction and manipulation capabilities

Unmanned aerial

systems

Mobile robotic

manipulation

General manipulation

with control, perception

and planning capabilities

Aerial manipulation

Increasing number of DoF:

• Flexibility for multiple applications

• Dexterity

• Cancel perturbations


• Indoor• Conventional platform• Few DoFs arms• Motion tracking systems• No perception• No planning

• Outdoor/ Indoor• Adapted platforms• 6/7 DoF Arms• Compliance• GPS navigation/ Beacons

indoor• On-board perception

with markers• Planning at Ground

station

• Outdoor/ Indoor• Full actuated platforms• Multiple arms• GPS free navigation• On-Board SLAM• On-board perception

without markers• Ground station with

control awareness• Planning on-board

?

Increase TRL: Industrial applications

Generations of aerial manipulators

AEROARMS H2020 ProjectARCAS FP7 Project


MISSION• Mapping• Localize objects (Comm

or perception)• Planning trajectories• Fly eventually avoiding

obstacles• Object recognition• Accurate positioning• Plan manipulation• Manipulation eventually

using visual servoing and contact information

• Reactions and local re-planning if needed

Aerial manipulation

Aerial Manipulator control in grasping phase using visual servoingCrawler detection with DNN (use of colour and depth image). Point cloud model of the crawler is provided to perform an ICP-based algorithm (Iterative closest Point algorithm)


Increasing TRLIndustrial Applications

Oil and gas applications:Refinery inspectionWall Thickness Measurements

Bridge inspection:Crack detection and evaluationDeformation measurement

Aerial manipulation


New book

Springer STAR

Aerial Robotic ManipulationA.Ollero and B. Sicilano editors27 Chapters• Introduction• Design, modelling and mechatronics. • Control• Perception• Planning• Applications

Structure constructionInspection and Maintenance

• Conclusions and Future Directions

• Emphasize Research and Application

• From ARCAS FP7 and AEROARMS H2020 European projects

To appear in

June 2019


• Indoor• Conventional platform• Few DoFs arms• Motion tracking systems• No perception• No planning

• Outdoor/ Indoor• Adapted platforms• 6/7 DoF Arms• Compliance• GPS navigation/ Beacons

indoor• On-board perception

with markers• Planning at Ground

station

• Outdoor/ Indoor• Full actuated platforms• Multiple arms• GPS free navigation• On-Board SLAM• On-board perception

without markers• Ground station with

control awareness• Planning on-board

AccuracyForcesEnergySafety

Increase TRL: Industrial applications

Generations of aerial manipulators

AEROARMS H2020 Project


• Power sources• Engines, turbines, ..• Battery technologies• Solar, Fuel Cells, others

• Recharging• Flying: Refueling, wire or wireless transmission• Perching

• Wind• Static soaring• Dynamic soaring

Energy challenge


ERC Advanced GrantGeneral compliant aerial Robotic manipulation system Integrating

Fixed and Flapping wings to INcrease range and safety GRIFFIN

Aerial robots with gliding and flapping wing phases which are able to

perch on curved surfaces to fold the wings and maintain contact by using

grips or hands and to perform dexterous manipulation


GRIFFIN research 1) Transitions fixed wings < --- > flapping wings < --- > perching or grabbing

• Aerodynamic model of transitions

• Extension to flexible wings

• Control based on models (aerodynamic transitions, aeroelastic)

2) Perching based on on-board perception, planning and learning trajectories

• Landing on constrained and curved surfaces: bars, pipes, ………

• Fusion of cameras and lasers sensors

• Saliency maps and deep learning techniques for fast landing place detection

• Dynamic vision systems and event cameras for faster perception

• Artificial muscles

3) Manipulation with fixed contact points subject to destabilizing forces

• Active control of the compliance of the body to adapt to perturbations and, at the

same time, to provide enough accuracy for the dexterous manipulation

• Joint analysis of equilibrium and manipulability conditions

• High frequency integration of visual perception and force sensing

• Cooperative manipulation



First Aerodynamic Models.

2D model

Infinite rigidity

Harmonic flapping motion

Angle between wing chord and

stroke plane fixed α0

= α + β

Angle between tail chord and

stroke plane fixed α0t

= αt+ β

Forces

Wing: Thrust (T) and Lift (L)

Body: Drag

Tail: Drag (Dt) and Lift (Lt)

Definition of Non-dimensional Newton-Euler Equations in the GRIFFIN framework ξ – ζ


Preliminary Models

Lighthill's number (𝐿 =𝐴𝑏

𝐴𝑤

𝐶𝐷)

Verified

in Wind

Tunnel

Prandlt’s Lifting Line Theory

R.T. Jones - flat delta wing

α0

= 90 deg, α0t

=80 deg, αi= 0


Comparison of unstable modes in fixed wing and flapping wings.

Faster in flapping wings, related to bioinspired tail designs

Flight control and maneuvering (take off, landing, perching, …) in flapping wings

Nonlinear systems, and generally time periodic. Nonlinear control

Main problem in GRIFFIN:

Increase payload

Power consumption

Flexible wing designs

Wing control

Wing power consumption

𝑃𝑊 = 𝑃𝐹 + 𝑃𝑇 + 2𝐼𝜃 𝜃 𝜃 + 2𝐼𝜂 𝜂 𝜂


Flight Control


GRIFFIN perception

Landing and perching with

multisensor perception


Perception


Low weight claws: Compliant grasping

Twisted and coiled polymer muscles

• Contractive actuators• Ultra lightweight• High ratio force-weight• Actuated with electrical heating


Bioinspired designs


Perform manipulation when ornithopter is perched:

Keep the equilibrium

React safely against unavoidable perturbationsSymmetric systemProblem in 2D

Objective: reach a particular point

𝑥𝑡 , 𝑦𝑡 θ1, θ2,θ3,θ4,θ5

Underactuated system:

1 passive joint (the first one)

4 actuated joints (DC motors)

Equilibrium points:

θ1=0, πΓ 𝑖= 𝑈𝑖 (i=2,3,4,5)

Robust control

techniques to

stabilize the

system !!!!

Energy and safety: GRIFFINManipulation control


ICRA 2019. Workshop “The Future of Aerial Robotics: Challenges and Opportunities”. May 23, 2019A. Ollero.Aerial Robotics: Challengesand Opportunities

Towards Robotic Arms for Winged Aerial Robots

Human-size compliant dual arm – 50 cm reach, 1.3 kg

Half-scale compliant dual arm – 25 cm reach, 0.2 kg

Harder payload constraints in flapping-wing platforms

One order of magnitude lighter manipulators:

o Dual arm for multirotor platform 1.5 – 2.5 kg weight

o Dual arm for ornithopter platform 0.2 – 0.3 kg weight

Smaller actuators (micro servos), simpler mechanics

Compliance for force/torque estimation and control


Conclusions

• General Aerial Robotic Challenges in energy, autonomy and interactions

• Aerial robotics manipulation challenges in accuracy, autonomyand application of forces

• Limits in the aerial manipulation while flying• Landing/Perching for manipulation• New approaches to overcome energy and safety constraintso Flapping/fixed wingo Compliance in the aerial platform and the limbso Integrated control, perception and planning

Aníbal Ollero - IEEE Aerial Robotics and UAVs

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