Air-Cobot
Country | France |
---|---|
Type | Cobot |
Website | aircobot |
Air-Cobot (Aircraft Inspection enhanced by smaRt & Collaborative rOBOT) is a French research and development project of a wheeled collaborative mobile robot able to inspect aircraft during maintenance operations. This multi-partner project involves research laboratories and industry. Research around this prototype was developed in three domains: autonomous navigation, human-robot collaboration and nondestructive testing.
Air-Cobot is presented as the first wheeled robot able to perform visual inspections of aircraft. Inspection robots using other types of sensors have been considered before, such as the European project Robair. Since the launch of the project, other solutions based on
Since the beginning of the project in 2013, the Air-Cobot robot is dedicated to inspect the lower parts of an aircraft. In the continuation of the project, there is the prospect of coupling with a drone to inspect an aircraft's upper parts. In October 2016, Airbus Group launched its research project on the hangar of the future in Singapore. The robots from the Air-Cobot and Aircam projects are included in it.
Project description
Objectives
Launched in January 2013,
Partners
The project leader is Akka Technologies. There are two academic partners; Akka Technologies and four other companies make up the five commercial partners.[6]
- Academic partners
- Armines and Institut Clément Ader of the École des mines d'Albi-Carmaux are in charge of nondestructive testing.[6][7]
- Laboratoire d'analyse et d'architecture des systèmes (LAAS-CNRS) with the Robotics, Action and Perception (RAP) team handles the autonomous navigation.[6][7][8]
- Industrial partners
- Akka Technologies, particularly the center for research and development Akka Research Toulouse, leads the project and brings skills in image analysis, navigation and aircraft maintenance.[3][6][7][9]
- 2MoRO Solutions, a company based in the French Basque Country, is in charge of the maintenance information system.[6][7]
- M3 System, a Toulouse-based company, takes care of the outdoor localization solution based on the Global Positioning System (GPS).[6][7][10]
- Sterela, based in the south of Toulouse, provides the 4MOB mobile platform.[6][7][11]
Project finance
Project finance is provided by
Expected benefits
Aircraft are inspected during maintenance operations either outdoors on an airport between flights, or in a hangar for longer-duration inspections. These inspections are conducted mainly by human operators, visually and sometimes using tools to assess defects.[A 1] The project aims to improve inspections of aircraft and traceability. A database dedicated to each aircraft type, containing images and three-dimensional scans, will be updated after each maintenance. This allows for example to assess the propagation of a crack.[4][13]
The human operator's eyes fatigue over time while an automatic solution ensures reliability and repeatability of inspections. The decrease in time taken for inspections is a major objective for aircraft manufacturers and airlines. If maintenance operations are faster, this will optimize the availability of aircraft and reduce maintenance operating costs.[4][13]
Robot equipment
All electronics equipment is carried by the 4MOB mobile platform manufactured by Sterela. The off-road platform, equipped with four-wheel drive, can move at a speed of 2 metres per second (7.2 kilometres per hour (4.47 mph)).[11] Its lithium-ion battery allows an operating time of eight hours. Two bumpers are located at the front and at the rear. These are obstacle detection bumpers. They stop the platform if they are compressed.[11]
The cobot weighs 230 kilograms (507 lb). It has two computers, one running
The autonomous navigation of the Air-Cobot robot is in two phases. The first, navigation in the airport or the factory, allows the robot to move close to the aircraft. The second navigation, around the aircraft, allows the robot to position itself at control points referenced in the aircraft virtual model. In addition, the robot must insert itself in a dynamic environment where humans and vehicles are moving. To address this problem, it has an obstacle avoidance module. Many navigation algorithms are constantly running on the robot with real time constraints. Searches are conducted on optimizing the computing time.[citation needed][clarification needed]
In an outdoor environment, the robot is able to go to the inspection site by localizing through
Another algorithm based on computer vision provides, in real-time, a lane marking detection. When visible, painted lanes on the ground can provide complementary data to the positioning system to have safer trajectories.[A 3] If in an indoor environment or an outdoor environment where GPS information is not available, the cobot can be switch to follower mode to move behind the human operator and follow her or him to the aircraft to inspect.[14][A 2]
To perform the inspection, the robot has to navigate around the aircraft and get to the checkpoints called up in the aircraft virtual model. The position of the aircraft in the airport or factory is not known precisely; the cobot needs to detect the aircraft in order to know its position and orientation relative to the aircraft. To do this, the robot is able to locate itself, either with the laser data from its laser range finders,[A 4] or with image data from its cameras.[A 1][A 5]
Near the aircraft, a point cloud in three dimensions is acquired by changing the orientation of the laser scanning sensors fixed on pan-tilt units. After filtering data to remove floor- or insufficiently large dot clusters, a registration technique with the model of the aircraft is used to estimate the static orientation of the robot. The robot moves and holds this orientation by considering its wheel odometry, its inertial unit and visual odometry.[A 4]
Laser data are also used horizontally in two dimensions. An algorithm provides a real-time position estimation of the robot when enough elements from the landing gears and engines are visible. A confidence index is calculated based on the number of items collected by lasers. If good data confidence is achieved, the position is updated. This mode is particularly used when the robot moves beneath the aircraft.[A 4]
For visual localization, the robot estimates its position relative to the aircraft using visual elements (doors, windows, tires, static ports etc.) of the aircraft. During the evolution of the robot, these visual elements are extracted from a three-dimensional virtual model of the aircraft and projected in the image plane of the cameras. The projected shapes are used for
By detecting and tracking visual landmarks, in addition to estimating its position relative to the aircraft, the robot can perform a visual servoing.[A 6] Research in vision is also conducted on simultaneous localization and mapping (SLAM).[A 7][A 8] A merger of information between the two methods of acquisition and laser vision is being considered. Artificial intelligence arbitrating various locations is also under consideration.[A 4][A 1]
Obstacle avoidance
In both navigation modes, Air-Cobot is also able to detect, track, identify and avoid obstacles that are in its way. The laser data from laser range sensors and visual data from the cameras can be used for detection, monitoring and identification of the obstacles. The detection and monitoring are better in the two-dimensional laser data, while identification is easier in the images from the cameras; the two methods are complementary. Information from laser data can be used to delimit work areas in the image.[A 6][A 9][A 10]
The robot has several possible responses to any obstacles. These will depend on its environment (navigation corridor, tarmac area without many obstacles, cluttered indoor environment etc.) at the time of the encounter with an obstacle. It can stop and wait for a gap in traffic, or avoid an obstacle by using a technique based on a spiral, or perform
Computing time optimization
Given the number of navigation algorithms calculating simultaneously to provide all the information in real time, research has been conducted to improve the computation time of some numerical methods using field-programmable gate arrays.[A 11][A 12][A 13] The research focused on visual perception. The first part was focused on the simultaneous localization and mapping with an extended Kalman filter that estimates the state of a dynamic system from a series of noisy or incomplete measures.[A 11][A 13] The second focused on the location and the detection of obstacles.[A 12]
Non-destructive testing
Image analysis
After having positioned to perform a visual inspection, the robot performs an acquisition with a
The detection uses pattern recognition of regular shapes (rectangles, circles, ellipses). The 3D model of the element to be inspected can be projected in the image plane for more complex shapes. The evaluation is based on indices such as the uniformity of segmented regions, convexity of their forms, or periodicity of the image pixels' intensity.[A 14]
The
Point cloud analysis
After having positioned to perform a scan inspection, the pantograph elevates the
By moving the pan-tilt units of the laser range finders, it is also possible to obtain a point cloud in three dimensions. Technical readjustment between the model of the aircraft and the scene point cloud is already used in navigation to estimate the static placement of the robot. It is planned to make targeted acquisitions, simpler in terms of movement, to verify the absence of chocks in front of the landing gear wheels, or the proper closing of engine cowling
Collaboration human-robot
As the project name suggests, the mobile robot is a cobot – a collaborative robot. During phases of navigation and inspection, a human operator accompanies the robot; he can take control if necessary, add inspection tasks, note a defect that is not in the list of robot checks, or validate the results. In the case of pre-flight inspections, the diagnosis of the walk-around is sent to the pilot who decides whether or not to take off.[7][14][A 21]
Other robotic inspection solutions
European project Robair
The inspection robot of the European project Robair, funded from 2001 to 2003, is designed to mount on the wings and
EasyJet drone
Airline EasyJet is interested in the inspection of aircraft with drones. It made a first inspection in 2015. Equipped with laser sensors and high resolution camera, the drone performs autonomous flight around the aeroplane. It generates a three-dimensional image of the aircraft and transmits it to a technician. The operator can then navigate in this representation and zoom to display a high-resolution picture of some parts of the aircraft. The operator must then visually diagnose the presence or absence of defects. This approach avoids the use of platforms to observe the upper parts of the aeroplane.[19]
Donecle drone
Founded in 2015, Donecle, a Toulouse start-up company, has also launched a drone approach which was initially specialized in the detection of lightning strikes on aeroplanes.[20][21] Performed by five people equipped with harnesses and platforms, this inspection usually takes about eight hours. The immobilization of the aircraft and the staff are costly for the airlines, estimated at $10 000 per hour. The solution proposed by the start-up lasts twenty minutes.[21]
Donecle uses a swarm of drones equipped with laser sensors and micro-cameras. The algorithms for automatic detection of defects, trained on existing images database with a
Project continuation
In 2015, in an
At the
During the 14th International Conference on Remote Engineering and Virtual Instrumentation in March 2017, Akka Research Toulouse, one of the centers for
Communications
On 23 October 2014, a patent was filed by
On 17 April 2015, Airbus Group distributed a project presentation video, made by the communication agency Clipatize, on its YouTube channel.
See also
Notes and references
Research publications of the project
- ^ a b c d e f Villemot, Larnier & Vetault 2016, RFIA
- ^ a b c d e Donadio et al. 2017, REV
- ^ Bauda, Bazot & Larnier 2017, ECMSM
- ^ a b c d e f g h i j Frejaville, Larnier & Vetault 2016, RFIA
- ^ a b c Jovancevic et al. 2016, ICPRAM
- ^ a b c Futterlieb, Cadenat & Sentenac 2014, ICINCO
- ^ Esparza-Jiménez, Devy & Gordillo 2014, FUSION
- ^ Esparza-Jiménez, Devy & Gordillo 2016, Sensors
- ^ Lakrouf et al. 2017, ICMRE
- ^ a b Leca et al. 2019, ECC
- ^ a b Tertei, Piat & Devy 2014, ReConFig
- ^ a b Alhamwi, Vandeportaele & Piat 2015, ICVS
- ^ a b Tertei, Piat & Devy 2016, CEE
- ^ a b c Jovancevic et al. 2015, JEI
- ^ Jovancevic et al. 2015a, QCAV
- ^ Jovancevic et al. 2015b, CMOI
- ^ Jovancevic et al. 2016, MECO
- ^ Leiva et al. 2017, ECMSM
- ^ Jovancevic et al. 2017, I2M
- ^ Bauda, Grenwelge & Larnier 2018, ETRSS
- ^ Donadio et al. 2016, MCG
Proceedings
- Futterlieb, Marcus; Cadenat, Viviane; Sentenac, Thierry (2014). "A navigational framework combining Visual Servoing and spiral obstacle avoidance techniques". Informatics in Control, Automation and Robotics (ICINCO), 2014 11th International Conference on, Vienna: 57–64.
- Esparza-Jiménez, Jorge Othón; Devy, Michel; Gordillo, José Luis (2014). "EKF-based SLAM fusing heterogeneous landmarks". 17th International Conference on Information Fusion (FUSION): 1–8.
- Tertei, Daniel Törtei; Piat, Jonathan; Devy, Michel (2014). "FPGA design and implementation of a matrix multiplier based accelerator for 3D EKF SLAM". International Conference on ReConFigurable Computing and FPGAs (ReConFig14): 1–6.
- Jovancevic, Igor; Orteu, Jean-José; Sentenac, Thierry; Gilblas, Rémi (April 2015a). Meriaudeau, Fabrice; Aubreton, Olivier (eds.). "Automated visual inspection of an airplane exterior". Proceedings of SPIE. Twelfth International Conference on Quality Control by Artificial Vision 2015. 9534: 95340Y. S2CID 29158717.
- (in French) Jovancevic, Igor; Orteu, Jean-José; Sentenac, Thierry; Gilblas, Rémi (November 2015b). "Inspection d'un aéronef à partir d'un système multi-capteurs porté par un robot mobile". Actes du 14ème Colloque Méthodes et Techniques Optiques pour l'Industrie.
- Alhamwi, Ali; Vandeportaele, Bertrand; Piat, Jonathan (2015). "Real Time Vision System for Obstacle Detection and Localization on FPGA". Computer Vision Systems – 10th International Conference, ICVS 2015: 80–90.
- Jovancevic, Igor; Viana, Ilisio; Orteu, Jean-José; Sentenac, Thierry; Larnier, Stanislas (February 2016). "Matching CAD Model and Image Features for Robot Navigation and Inspection of an Aircraft". Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods (PDF). International Conference on Pattern Recognition Applications and Methods. pp. 359–366. ISBN 978-989-758-173-1.
- Jovancevic, Igor; Arafat, Al; Orteu, Jean-José; Sentenac, Thierry (2016). "Airplane tire inspection by image processing techniques". 5th Mediterranean Conference on Embedded Computing.
- (in French) Frejaville, Jérémy; Larnier, Stanislas; Vetault, Stéphane (2016). "Localisation à partir de données laser d'un robot naviguant autour d'un avion". Actes de la conférence Reconnaissance de Formes et Intelligence Artificielle.
- (in French) Villemot, Tanguy; Larnier, Stanislas; Vetault, Stéphane (2016). "Détection d'amers visuels pour la navigation d'un robot autonome autour d'un avion et son inspection". Actes de la conférence Reconnaissance de Formes et Intelligence Artificielle.
- Donadio, Frédéric; Frejaville, Jérémy; Larnier, Stanislas; Vetault, Stéphane (2016). "Human-robot collaboration to perform aircraft inspection in working environment" (PDF). Proceedings of 5th International Conference on Machine Control and Guidance.
- Lakrouf, Mustapha; Larnier, Stanislas; Devy, Michel; Achour, Nouara (2017). "Moving Obstacles Detection and Camera Pointing for Mobile Robot Applications". Proceedings of the 3rd International Conference on Mechatronics and Robotics Engineering. pp. 57–62. S2CID 2361994.
- Donadio, Frédéric; Frejaville, Jérémy; Larnier, Stanislas; Vetault, Stéphane (2017). "Artificial intelligence and collaborative robot to improve airport operations". Proceedings of 14th International Conference on Remote Engineering and Virtual Instrumentation.
- Bauda, Marie-Anne; Bazot, Cécile; Larnier, Stanislas (2017). "Real-time ground marking analysis for safe trajectories of autonomous mobile robots". 2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and their Application to Mechatronics (ECMSM). pp. 1–6. S2CID 25210956.
- Leiva, Javier Ramirez; Villemot, Tanguy; Dangoumeau, Guillaume; Bauda, Marie-Anne; Larnier, Stanislas (2017). "Automatic visual detection and verification of exterior aircraft elements". 2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and their Application to Mechatronics (ECMSM). pp. 1–5. S2CID 9052556.
- Bauda, Marie-Anne; Grenwelge, Alex; Larnier, Stanislas (2018). "3D scanner positioning for aircraft surface inspection" (PDF). Proceedings of European Congress Embedded Real Time Software and Systems.
- Leca, Dimitri; Cadenat, Viviane; Sentenac, Thierry; Durand-Petiteville, Adrien; Gouaisbaut, Frédéric; Le Flécher, Emile (2019). "Sensor-based Obstacles Avoidance Using Spiral Controllers for an Aircraft Maintenance Inspection Robot". Proceedings of European Control Conference: 2083–2089.
Journal articles
- Jovancevic, Igor; Larnier, Stanislas; Orteu, Jean-José; Sentenac, Thierry (November 2015). "Automated exterior inspection of an aircraft with a pan-tilt-zoom camera mounted on a mobile robot". Journal of Electronic Imaging. 24 (6): 061110. S2CID 29167101.
- Esparza-Jiménez, Jorge Othón; Devy, Michel; Gordillo, José Luis (2016). "EKF-based SLAM fusing heterogeneous landmarks". Sensors. 16 (4): 489. PMID 27070602.
- Tertei, Daniel Törtei; Piat, Jonathan; Devy, Michel (2016). "FPGA design of EKF block accelerator for 3D visual SLAM". Computers and Electrical Engineering.
- Jovancevic, Igor; Pham, Huy-Hieu; Orteu, Jean-José; Gilblas, Rémi; Harvent, Jacques; Maurice, Xavier; Brèthes, Ludovic (2017). "Détection et caractérisation de défauts de surface par analyse des nuages de points 3D fournis par un scanner". Instrumentation, Mesure, Métrologie, Lavoisier (in French). 16: 261–282.
PhD thesis reports
- Jovancevic, Igor (2016). Exterior inspection of an aircraft using a Pan-Tilt-Zoom camera and a 3D scanner moved by a mobile robot: 2D image processing and 3D point cloud analysis. École nationale supérieure des mines d'Albi-Carmaux.
- Futterlieb, Marcus (2017). Vision based navigation in a dynamic environment. Université Paul Sabatier.
Other references
- ^ a b (in French) Xavier Martinage (17 June 2015). "Air-Cobot : le robot dont dépendra votre sécurité". lci.tf1.fr. La Chaîne Info. Archived from the original on 3 January 2016. Retrieved 12 July 2016.
- ^ a b (in French) "Air-Cobot : un nouveau mode d'inspection visuelle des avions". competitivite.gouv.fr. Les pôles de compétitivité. Archived from the original on 11 October 2016. Retrieved 12 July 2016.
- ^ a b c d e f (in French) Olivier Constant (11 September 2015). "Le projet Air-Cobot suit son cours". Air et Cosmos (2487). Retrieved 12 July 2016.
- ^ a b c (in French) "Rapport d'activité 2013–2014 de l'Aerospace Valley" (PDF). aerospace-valley.com. Aerospace Valley. Archived from the original (PDF) on 24 September 2016. Retrieved 12 July 2016.
- ^ a b (in French) "News du projet Air-Cobot". aircobot.akka.eu. Akka Technologies. Archived from the original on 10 July 2016. Retrieved 12 July 2016.
- ^ Capital. 1 July 2014. Archived from the originalon 25 June 2016. Retrieved 14 July 2016.
- ^ a b c d e f g h i (in French) "Air-Cobot, le robot qui s'assure que vous ferez un bon vol !". Planète Robots (38): 32–33. March–April 2016.
- Laboratoire d'analyse et d'architecture des systèmes. Archived from the originalon 14 September 2015. Retrieved 17 July 2016.
- ^ (in French) "Akka Technologies : une marque employeur orientée sur l'innovation". Le Parisien. 15 February 2016. Retrieved 17 July 2016.
- ^ a b "M3 Systems Flagship Solution". M3 Systems. Archived from the original on 6 August 2016. Retrieved 17 July 2016.
- ^ a b c (in French) "4MOB, plateforme intelligente autonome" (PDF). Sterela Solutions. Archived from the original (PDF) on 9 August 2016. Retrieved 17 July 2016.
- ^ (in French) "Financeurs". aircobot.akka.eu. Akka Technologies. Archived from the original on 4 August 2016. Retrieved 15 July 2016.
- ^ a b (in French) Véronique Guillermard (18 May 2015). "Aircobot contrôle les avions avant le décollage". Le Figaro. Retrieved 14 July 2016.
- ^ YouTube
- ^ (in French) Pascal NGuyen (December 2014). "Des robots vérifient l'avion au sol". Sciences et Avenir (814). Archived from the original on 8 August 2016. Retrieved 17 July 2016.
- ^ (in French) "Robair, Inspection robotisée des aéronefs". European Commission. Retrieved 16 July 2016.
- ^ "Robair". London South Bank University. Retrieved 16 July 2016.
- .
- ^ (in French) Newsroom (8 June 2015). "Easy Jet commence à utiliser des drones pour l'inspection de ses avions". Humanoides. Archived from the original on 12 October 2015. Retrieved 16 July 2016.
- ^ (in French) Florine Galéron (28 May 2015). "Aéronautique : la startup Donecle invente le drone anti-foudre". Objectif News, la Tribune. Retrieved 16 July 2016.
- ^ a b c (in French) Arnaud Devillard (20 April 2016). "Des drones pour inspecter des avions". Sciences et Avenir. Archived from the original on 8 August 2016. Retrieved 16 July 2016.
- ^ YouTube
- ^ "Pimp my Hangar: Excelling in MRO". airbusgroup.com. Airbus. Archived from the original on 21 December 2016. Retrieved 21 December 2016.
- ^ (in French) Éric Parisot (21 June 2013). "Co-Friend, le système d'analyse d'images qui réduit les temps d'immobilisation des avions". Usine Digitale. Retrieved 24 February 2018.
- ^ (in French) Aeroscopia, ed. (August 2017). "Le Musée accueille le projet AIR-COBOT". musee-aeroscopia.fr. Archived from the original on 14 October 2017. Retrieved 24 February 2018.
- ^ "Espacenet – Bibliographic data – Collaborative robot for visually inspecting an aircraft". worldwide.espacenet.com. Retrieved 1 June 2016.
- ^ (in French) Juliette Raynal; Jean-François Prevéraud (15 June 2015). "Bourget 2015 : les dix rendez-vous technos à ne pas louper". Industrie et Technologies. Retrieved 16 July 2016.
- ^ (in French) "Akka Technologies au Salon du Bourget". Maurice Ricci. 21 June 2015. Archived from the original on 4 April 2016. Retrieved 16 July 2015.
- ^ "Singapore Airshow 2016 Trends: Emerging Technologies Take Off – APEX | Airline Passenger Experience". apex.aero. Retrieved 1 June 2016.
- ^ "Communications du projet Air-Cobot". aircobot.akka.eu (in French). Akka Technologies. Archived from the original on 11 August 2016. Retrieved 14 July 2016.
- ^ "Best MCG2016 Final Application Award" (PDF). mcg2016.irstea.fr. Machine Control and Guidance. October 2016. Retrieved 22 February 2020.
- ^ "AirCobot – Introducing Smart Robots for Aircraft Inspections". clipatize.com. Clipatize. Archived from the original on 6 August 2016. Retrieved 15 August 2016.
- YouTube
- ^ Air-Cobot, le robot d'assistance aux inspections des aéronefs (PDF). Programme de la fête de la science (in French). 2015. Retrieved 17 July 2016.
External links
- Official website
- Air-Cobot Archived 1 July 2016 at the Wayback Machine