application of robotics in radiation measurements

Application of robotics in radiation measurements

Juha Röning

[email protected]

Cores symposium on applications of big data, sensor networks,

robotics and artificial intelligence in radiation safety

5th of September 2018, Tampere

Content

•BISG

•euRobotics aisbl

•Arctic Drone Labs /

Digital Innovation Hub

•Robots for radiation measurements

•Projects11.6.2018 Juha Röning 2

Oulun yliopisto

Biomimetics and Intelligent Systems Group (BISG)

Data analysis and inference

‒ Data mining, machine learning

- AI-based tools that support decision making

- Well-being, industrial processes (e.g. steel)

Robotics‒ Modular robot architecture and design

- Ground, surface and aerial unmanned vehicles

- Navigation, environmental exploration, manipulation

Secure software

‒ Dependable and robust software

- Fuzzy testing

- Security and privacy preserving

solutions

Bio-IT

‒ Natural and artificial intelligent systems

- ”In silico” systems modelling

- Bio-tech nano systems

“A fusion of expertise

from computer science

and biology”

Four groups:

Several spin-offs: IndoorAtlas, Indalgo; Probot, Aquamarine Robotics, Atomia; Codenomicon, Clarified Networks

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Oulun yliopisto

Data analysisand inference

http://www.oulu.fi/bisg

‒ Data mining, machine learning

- Sensor measurements analysis and

related modelling

- Quality monitoring for steel industry:

- Smart manufacturing with AI-based

tools that support decision making

‒ Several spin-offs, including

IndoorAtlas and Indalgo

Oulun yliopisto

‒ Fuzzy testing for software

dependability and robustness

‒ Security and privacy

preserving solutions


Codenomicon, Clarified

Networks

‒ Example application areas

include

- Cloud services,

- IoT, …

Secure software


Oulun yliopisto

‒ Bridges the other groups

‒ Natural and artificial intelligent

systems

‒ Systems modelling

- ”In silico”

- Social interaction

‒ Bio-tech nano systems

- The next step in cyber-physical

systems

Bio-IT


Oulun yliopisto

‒ Ground, surface and aerial

unmanned vehicles

‒ Modular robot architecture

and design

‒ Navigation, environmental

exploration, manipulation


Probot, Aquamarine Robotics,

Atomia

Robotics


Europe leading the way in robotics

euRobotics aisbl

Market data – industrial robots

Name / Date 9

• According to IFR statistics, the worldwide robot density in the manufacturing industries increased. Latest data show 2016 statistics:

• 74 robot units per 10,000 employees (2015: 66 units). • Europe 99 units• the Americas 84 units• Asia 63 units

• Large spread also within Europe

• Germany 309 units• Austria 144• UK 71 units• Poland 32 units

Market data

10

• Even more increase for professional and domestic service robots with an annual growth rate >20% expected for 2018-2020

• Europe is very strong!

• Use momentum and creative power

Name / Date

11

> 250 Member OrganizationsLegend:

Industry

Research

Associate

euRobotics

aisbl

Headquarters

• non-profit organization for all stakeholders in European robotics, founded in 2012

• Largest network of roboticists and business in Europe

• Main goal is to leverage network to build upon Europe’s leadership in robotics

• 250+ members and growing

• 30+ Topic Groups: “grass roots” talking shops covering wide range of robotics-related issues

• Successful collaboration between industrial and academic players

euRobotics aisbl – the private side of SPARC

SPARC – The Robotics PPP – ICT committee presentation, Brussels,

18 May 2017

SPARC: Creating the Eco-system

12

Arctic Drone LabsDigital Innovation Hub

University of OuluOulu Univ. Of Applied Sc.VTTBusiness Oulu

University of JyväskyläJAMK Univ. of Applied Sc.VTTCity of Jyväskylä

Tampere Univ. of TechnologySMACCVTTCity of Tampere

University of TurkuÅbo AkademiTurku Univ. of Applied Sc.Novia Univ. of Applied Sc.Turku Business Region

Oulu

Jyväskylä

Tampere

TurkuUniversity of HelsinkiHIIT research instituteAalto University (via HIIT)VTT

Helsinki

Univ of Oulu:

J. RöningRobotics

DroneFablab

Univ of Jyväskylä:Dronejournalisim

Univ of Tampere:

J. AaltonenRobotiikka

TAMK:Drone-program

AviationtechnologyMetropolia:

LaureaCentria

HagaHelia:Safety

BordercontrolEU hankkeet

OAMK:Agri

Univ of Helsinki:

ComputingAgri

Univ of Turku:

SwarmsBD

Agri

Maanmittauslaitos/PaikkatietokeskusEija Honkovaara

VTT:Sensors

Infrachecks

ADL competence centers

16

Arctic Drone Labs - Services

DAAS opens new business opportunities.

Innovate together novel solutions

and business models utilizing UAV

technologies.

Easy way to find big data, analytics,

IoT, 5G and sensor expertise

through cooperation with other

HILLA ecosystems

ADL assets will provide excellent

opportunities to pilot novel solutions

ADL assets will provide excellent

opportunities to find novel

application areas and test service

concepts based on acquired data.

ADL provides R&D cooperation for

companies and public institutes

from different application areas

Follow and influence regulation

SPARC – The Robotics PPP – ICT committee presentation, Brussels,

18 May 2017

• Last tournament: 15-23 September 2017, Piombino, Italy.

• Outdoor robotics competition with a focus on realistic, multi-domain disaster-response scenarios.

• Inspired by Fukushima accident, the ERL Emergency challenge requires teams of land, underwater and flying robots to collaborate in order to survey the disaster scene, collect environmental data and identify critical hazards.

ERL Emergency Robots

www.robotics-league.eu

Maintenance and & Inspection of Infrastructure

18Name / Date

Drivers for use of robotics – asset owner view• Safety impact• Environmental impact• Economic impact

Majority of assets need to be inspected on regular intervals – due to maintenance needs or safety requirements

Assets may be located in hazardous and/or remote locations

• Transportation (rails, streets, bridges, …)• Renewable energy, Oil & Gas• Power distribution• Process industry (chemical, fertilisers, …)• Fresh and waste water (pipes, canalization)

Reference: TG Maintenance and Inspection, SINTEF

Maintenance and & Inspection of Infrastructure- examples of European services and systems

19Name / Date

petrobotproject.eu

aeroarms-project.eu

www.sevendof.com

© WälischmillerEngineering GmbH

Reference: TG Maintenance and Inspection, SINTEF

20

Test environment, Ouluzone

http://www.oulu.fi/bisg/robotics

Test area for differentautonomous systems :- Trucks- Cars- Drones- Robots- Logistics- Safety

UAVs – payloadsUAV can be equipped withstandalone payload

• Own GNSS localization and computer with com-link

• Can be used for measuring fromnarrow places

• Ground contact without landing

• UAV systems does not disturbmeasurements

• Sensors like radiation

• NORDUM 2016 in Norway for finding radiation sources likeCesium-137

16.10.2017 Juha Röning 21

University of Oulu

NORDUM exercise

The aim of the NORDUM (Intercomparison of

Nordic unmanned aerial monitoring

platforms) project is to cover and compare

different radiation measurement systems

and approaches for use in UAVs.

- The project partners include:

- Norwegian Radiation Protection Authority (NRPA)

- Danish Emergency Management Agency (DEMA)

- University of Oulu

- Linköping University

- Finnish Defence Research Agency FDRA

- The Norwegian Armed Forces - Forsvarets ABC-skole

(FABCS)

- Andøya Space Center (ASC)

- Institute for Energy Technology (IFE)

The first joint NORDUM exercise took place

in 5. – 7. September 2016.

9/15/2018 Replace footertext if needed22

University of Oulu

NORDUM exercise

Exercise area and the Scenarios.

- The exercise site was divided in a basecamp and

scenario areas one to three

- At the basecamp, the participants had access to

necessary basic needs like, power, food, water and

restroom.

- Right outside the camp building, the participants were

provided with a calibration area.

- The exercise was divided into three different scenarios.


University of Oulu

NORDUM exercise

Scenario 1:


Location The location was a small area with

containers and storage of metal

shelves.

Objective Search the area for any radioactive

sources using unmanned platforms, and

report your findings. Provide as much

information as possible.

Challenges Blockage of the radio signals, lot of

obstacles, and small area.

Sources Am-241, Cs-137, U-238

University of Oulu

NORDUM exercise

Scenario 2:


Location The location for scenario 2 was a

rectangular shaped open area with a

few containers on one side of the

field.

Objective Search the area for any radioactive

sources using unmanned platforms, and

report your findings. Provide as much

information as possible.

Challenges Hard to get an overview of the sit if the

team didn’t had a camera on their

system

Sources Eu-152, Two Co-60, and Pu-238

University of Oulu

NORDUM exercise

Scenario 3:


Location Semi-open area with a lots of vegetation and trees

Objectiv

e

Search the area for any radioactive fragments using unmanned

platforms, and report your findings. Provide as much information as

possible.

Challeng

es

Windy and turbulent area.

Sources Two Cs-137, Co-60, Sr-90

University of Oulu

NORDUM exercise

The exercise consisted of three different scenarios with different radiation sources and obstructions.

- Radiation sources used were Cs-137, U-238, Am-241, Eu-152, Co-60, Pu-238 and Sr-90. The sources were hidden to the environment and had different activities in different scenarios.

Five teams successfully tested their radiation detection / monitoring platforms to compare results and submitted their results to the final report.

- The CZT (Cadmium Zinc Telluride) based sensors seemed to be quite popular due to light weight and high sensitivity compared to detector volume. The sensor also has a high energy resolution which is helpful for identifying radiation sources.

- Other sensors used included GM (Geiger–Müller), NaI(Tl) (Sodium iodide activated with thallium) and LaBr3

(Lanthanum(III) bromide) based detectors.

- The biggest problems encountered during the exercises were radio link related, especially in locations with a lot of metal structures.

- The need for autonomous operation was apparent for environments with poor GPS signal quality and radio links.


University of Oulu

NEXUS exercise further expanded the challenges to urban

environments, contaminated fields and

scenarios for fixed wing systems.

The exercise was held at an open, joint

exercise area where the teams could

observe each other’s systems and

techniques directly.

The scenarios included small areas for

rotary wing UAVs searching for point

sources, larger areas for assessment of

contaminated areas, and surveys in urban

environment.

The NEXUS exercise took place in 31st

Otober-a 2nd November 2017 in Björka and

Revinge, Sweden.


NEXUS – results


IAEA – Robotic challenge

2017-11-20-> @ Brisbane, Australia

Challenge Category 2: Small Unmanned Ground Vehicle (UGV)

• The IAEA would like to identify small unmanned vehicles / robotized rolling platforms able to assist the inspector by performing the following tasks: moving autonomously across a storage area, counting items of a specific geometry, recording their ID tags, and carrying specific IAEA instrument payloads.


ELROB – robot challenge- Mobile robot’s in Harsh environmentParticipated on several international robot competitions with success:• C-Elrob 2007, 2nd in Combined scenario• M-Elrob 2008, winner in Camp security, 4th in Mule scenarios• ESA lunar robot challenge 2008, 3rd place • C-Elrob 2009 in Oulu 15.-18.6. 2009

• M-Elrob 2014 in Poland: • “Best Innovative solution”-award

• Eurathlon 2015 – summer school organization

• Eurathlon 2016 – summer school organization

• TARGET: ELROB 2018 in Belgium


HYFLIERSHYbrid FLying-rollIng with-snakE-aRm

robot for contact inSpection

Prof. Juha Röning, University of Oulu

Project ID: 779411

Call: H2020-ICT-25-2016-2017

4 years (2018–2021)

3,9 MEUR

8 partners

5 countries

Univ. of

Oulu

CREATE

Univ. of

Seville

Chevron

Oronite

FADA-CATEC

GE

Inspection

Robotics

Total

DASEL

sistemas

Automated non-destructive thickness measurements

Targets oil & gas refineries, but applicable to chemical plans and other inspection technologies

Reduce inspection costs and casualties

Current Status: Catastrophic Consequences

• Despite measurements, there may be failures. Example:

• Silver Eagle Refinery, Utah USA

• Aftermath of an explosion due to erroneous piping thickness measurements

• “10-inch pipe failed catastrophically. Although the failure mechanism has yet to be determined, the pipe showed evidence of significant thinningwhich had not been detected by the refinery’s mechanical integrity program “[Statement of U.S. Chemical Safety Board Chairman John Bresland -Friday November 13, 2009]

25 May 2018 J. Röning: HYFLIERS H2020 Project 33

• The Salt Lake Tribune

• Credit: US Chemical Safety Board

• https://www.youtube.com/watch?v=Y9lFqeEoNXc

https://www.youtube.com/watch?v=Y9lFqeEoNXc

Ambition/Objectives

Ambition

World's first industrial integrated robot Hybrid robot with aerial & ground mobility

Reach sites no other robot can access Single long-reach, hyper-redundant arm

High accuracy Inspection platform attached to the pipe

Endurance Combination of aerial & ground locomotion

• Top Objective: Exploit a robotic inspection system• Reduce inspection costs (ladders / scaffold /rope access / cranes to ensure safety of

inspectors)

• Improve safety (reduce exposition of inspectors to potentially dangerous working conditions)

• Use case: Oil plant thickness measurements• Large number of pipes (atmospheric and

processing elements → corrosion)

• Ultrasound thickness measurements: dangerous & costly when carried by humans

13-15 Mar 2018 J. Röning: HYFLIERS 34

M M

field magnet,

height adjustable

to tube diameter

axial drive

(motorised)

lifter motor (slow)circ drive

(motorised)

air gap 0.5...7mm

NDT sensor

(UT)

Approach

• Prototype A: Hybrid Mobile Robot

• Robot moves to bring sensor to inspection site.

• Magnetic attraction (stability on pipe, modulation for landing/take-off)

13-15 Mar 2018 J. Röning: HYFLIERS 35

• Prototype B: Hybrid Robot with Arm

• Arm brings sensor to inspection site.

• Stability: propeller tilting, movingsystem’s centre of gravity

• Complete system, including

• Hybrid (flying and rolling) robot

• Robotic snake-arm

• Miniaturised ultrasonic sensor

• Mobile operation support platform

• Navigation support, battery & couplant refill, data communication & processing

Radiation source localization using swarm robotics and 3D-

SLAM methods University of Oulu

Radiation and Nuclear Safety Authority (STUK)The Finnish Defence Research Agency (FDRA)

University of Helsinki

Unmanned vehicles (1/3)

• Unmanned aerial vehicles• Customized octocopter

DJI S1000+

• Payload ca. 6 kg• Multiple cameras and a laser scanner

can be used at the same time

• Flight time approximately 25 min depending on the payload

• Computing hardware• Jetson TX2 –mini computer + PIXEVIA

CORE X1

Unmanned vehicles (2/3)

• Optional unmanned aerial vehicles• Quadcopter, hexacopter

• Custom made

• Light and agile

• Development and testing of algorithms

• Flight time > 15 min.

• Mini computer

• Commersial DJI Inspire 1• Remotely controlled

Unmanned vehicles (3/3)• Unmanned ground vehicle, Mörri

• Custom made• Fast computer

• 7th gen. Core-i7-prosessor• GPU: Nvidia Geforce GTX 1070

• Capable of real-time high-precision 3D mapping

• High payload• WiFi access point• Long distance radios• High precision measurement

equipments

• Complements the copters• Indoor operations• Very close to the radiation source in

some cases

Measurement system• Mörri ground vehicle

• Communication support for the copters

• Support for coordinating the swarm operations

• Performs large-scale planning algorithms for the copters

Steps for locating radiation sources (1/3)• 1. Mapping by the copter

• Real-time mapping of interest points for the navigation map

• GPS is not mandatory

• Metric coordinates are obtained by means of an optical flux sensor

• Simultaneous collection of image data for 3D reconstruction

• High resolution 3D model

• The radiation measurements are accurately positioned in the 3D reconstruction of the site

Steps for locating radiation sources (2/3)

• 2. Location of the radiation source• On several platforms simultaneously

• Real-time location based on the common navigation map using the camera image

• The common navigation algorithm instructs the copter flight controllers

• On-board functions• Positioning

• Obstacle avoidance and automatic flight

15/09/2018

43

Faculty of ScienceUniversity of Helsinki

Tuukka Petäjä, Juha Kangasluoma, Ella Häkkinen, Runlong Cai and Frans Korhonen

Aerosol number concentrationaround strong radioactivesources:

Technology and first results

Aerosolic particles:

• Drones provide a versatileplatform to determine spatio-temporal variability of atmospheric aerosol particles

• Finding aerosol sources• Tracking pollution transport Challenges:

• Weight, sampling, electricityrequirements

• Rapid movement of thedrone (flooding optics)

• Detection efficiency

Field trials in Lakiala 21.8.2018

• Mapping of radiation intensity and fine particle density in the vicinity of radiation point sources

• Real world data for developing and testing location algorithms

• Testing the equipment and data collection

• Goals: 2D and 3D maps of radiation intensity and fine particle density

• The following sources were utilized:Nuklidi Activity 21.8.2018

Am-241 185 MBq

Ba-133 180 MBq

Cs-137 66 GBq

Octocopter equipped with CPC (condensation particle counter) and Kromek radiation sensor used in Lakiala measurements

Lakiala measurement area and location of radiation sources

Am-241

Ba-133

Radiation sources were placed over a concrete "cross"

Ba-133

Am-241

The collimated Cs-137 radiation source is located in the building and measurements were made above the street (dashed line)

Tests with radiation sources:

• Test flights in Lakiala, Finland, August 2018

• Variability in aerosol concentration• Co-incidence correction not taken into

account• Detection efficiency needs to be tuned

towards smaller sizes

• Data processing on-going to connect GPS location data with aerosol data and Kromek(radiation intensity) Ba-133

Am-241

Prof. Juha Röning

[email protected]

Biomimetics and

Intelligent Systems

(BISG)


application of robotics in radiation measurements

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