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Wzorek, Mariusz
Publications (10 of 24) Show all publications
Andersson, O., Wzorek, M. & Doherty, P. (2017). Deep Learning Quadcopter Control via Risk-Aware Active Learning. In: Satinder Singh and Shaul Markovitch (Ed.), Proceedings of The Thirty-first AAAI Conference on Artificial Intelligence (AAAI): . Paper presented at Thirty-First AAAI Conference on Artificial Intelligence (AAAI), 2017, San Francisco, February 4–9. (pp. 3812-3818). AAAI Press, 5
Open this publication in new window or tab >>Deep Learning Quadcopter Control via Risk-Aware Active Learning
2017 (English)In: Proceedings of The Thirty-first AAAI Conference on Artificial Intelligence (AAAI) / [ed] Satinder Singh and Shaul Markovitch, AAAI Press, 2017, Vol. 5, p. 3812-3818Conference paper, Published paper (Refereed)
Abstract [en]

Modern optimization-based approaches to control increasingly allow automatic generation of complex behavior from only a model and an objective. Recent years has seen growing interest in fast solvers to also allow real-time operation on robots, but the computational cost of such trajectory optimization remains prohibitive for many applications. In this paper we examine a novel deep neural network approximation and validate it on a safe navigation problem with a real nano-quadcopter. As the risk of costly failures is a major concern with real robots, we propose a risk-aware resampling technique. Contrary to prior work this active learning approach is easy to use with existing solvers for trajectory optimization, as well as deep learning. We demonstrate the efficacy of the approach on a difficult collision avoidance problem with non-cooperative moving obstacles. Our findings indicate that the resulting neural network approximations are least 50 times faster than the trajectory optimizer while still satisfying the safety requirements. We demonstrate the potential of the approach by implementing a synthesized deep neural network policy on the nano-quadcopter microcontroller.

Place, publisher, year, edition, pages
AAAI Press, 2017
Series
Proceedings of the AAAI Conference on Artificial Intelligence, ISSN 2159-5399, E-ISSN 2374-3468 ; 5
National Category
Computer Vision and Robotics (Autonomous Systems) Computer Sciences
Identifiers
urn:nbn:se:liu:diva-132800 (URN)978-1-57735-784-1 (ISBN)
Conference
Thirty-First AAAI Conference on Artificial Intelligence (AAAI), 2017, San Francisco, February 4–9.
Projects
ELLIITCADICSNFFP6SYMBICLOUDCUGS
Funder
Linnaeus research environment CADICSELLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsEU, FP7, Seventh Framework ProgrammeCUGS (National Graduate School in Computer Science)Swedish Foundation for Strategic Research
Available from: 2016-11-25 Created: 2016-11-25 Last updated: 2018-01-13Bibliographically approved
Doherty, P., Kvarnström, J., Rudol, P., Wzorek, M., Conte, G., Berger, C., . . . Stastny, T. (2016). A Collaborative Framework for 3D Mapping using Unmanned Aerial Vehicles. In: Baldoni, M., Chopra, A.K., Son, T.C., Hirayama, K., Torroni, P. (Ed.), PRIMA 2016: Principles and Practice of Multi-Agent Systems: . Paper presented at PRIMA 2016: Principles and Practice of Multi-Agent Systems (pp. 110-130). Springer Publishing Company
Open this publication in new window or tab >>A Collaborative Framework for 3D Mapping using Unmanned Aerial Vehicles
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2016 (English)In: PRIMA 2016: Principles and Practice of Multi-Agent Systems / [ed] Baldoni, M., Chopra, A.K., Son, T.C., Hirayama, K., Torroni, P., Springer Publishing Company, 2016, p. 110-130Conference paper, Published paper (Refereed)
Abstract [en]

This paper describes an overview of a generic framework for collaboration among humans and multiple heterogeneous robotic systems based on the use of a formal characterization of delegation as a speech act. The system used contains a complex set of integrated software modules that include delegation managers for each platform, a task specification language for characterizing distributed tasks, a task planner, a multi-agent scan trajectory generation and region partitioning module, and a system infrastructure used to distributively instantiate any number of robotic systems and user interfaces in a collaborative team. The application focusses on 3D reconstruction in alpine environments intended to be used by alpine rescue teams. Two complex UAV systems used in the experiments are described. A fully autonomous collaborative mission executed in the Italian Alps using the framework is also described.

Place, publisher, year, edition, pages
Springer Publishing Company, 2016
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 9862
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:liu:diva-130558 (URN)10.1007/978-3-319-44832-9_7 (DOI)000388796200007 ()978-3-319-44831-2 (ISBN)
Conference
PRIMA 2016: Principles and Practice of Multi-Agent Systems
Funder
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsEU, FP7, Seventh Framework ProgrammeVINNOVASwedish Research Council
Note

Accepted for publication.

Available from: 2016-08-16 Created: 2016-08-16 Last updated: 2018-02-20
Berger, C., Rudol, P., Wzorek, M. & Kleiner, A. (2016). Evaluation of Reactive Obstacle Avoidance Algorithms for a Quadcopter. In: Proceedings of the 14th International Conference on Control, Automation, Robotics and Vision 2016 (ICARCV): . Paper presented at 14th International Conference on Control, Automation, Robotics and Vision (ICARCV), Phuket, Thailand, November 13-15, 2016. IEEE conference proceedings, Article ID Tu31.3.
Open this publication in new window or tab >>Evaluation of Reactive Obstacle Avoidance Algorithms for a Quadcopter
2016 (English)In: Proceedings of the 14th International Conference on Control, Automation, Robotics and Vision 2016 (ICARCV), IEEE conference proceedings, 2016, article id Tu31.3Conference paper, Published paper (Refereed)
Abstract [en]

In this work we are investigating reactive avoidance techniques which can be used on board of a small quadcopter and which do not require absolute localisation. We propose a local map representation which can be updated with proprioceptive sensors. The local map is centred around the robot and uses spherical coordinates to represent a point cloud. The local map is updated using a depth sensor, the Inertial Measurement Unit and a registration algorithm. We propose an extension of the Dynamic Window Approach to compute a velocity vector based on the current local map. We propose to use an OctoMap structure to compute a 2-pass A* which provide a path which is converted to a velocity vector. Both approaches are reactive as they only make use of local information. The algorithms were evaluated in a simulator which offers a realistic environment, both in terms of control and sensors. The results obtained were also validated by running the algorithms on a real platform.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2016
Series
International Conference on Control Automation Robotics and Vision, ISSN 2474-2953
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-130956 (URN)10.1109/ICARCV.2016.7838803 (DOI)000405520900204 ()2-s2.0-85015170851 (Scopus ID)9781509035496 (ISBN)9781509047574 (ISBN)9781509035502 (ISBN)
Conference
14th International Conference on Control, Automation, Robotics and Vision (ICARCV), Phuket, Thailand, November 13-15, 2016
Note

Funding agencies:This work is partially supported by the Swedish Research Council (VR) Linnaeus Center CADICS, the ELLIIT network organization for Information and Communication Technology, and the Swedish Foundation for Strategic Research (CUAS Project, SymbiKCIoud Project).

Available from: 2016-09-01 Created: 2016-09-01 Last updated: 2018-01-10Bibliographically approved
Andersson, O., Wzorek, M., Rudol, P. & Doherty, P. (2016). Model-Predictive Control with Stochastic Collision Avoidance using Bayesian Policy Optimization. In: IEEE International Conference on Robotics and Automation (ICRA), 2016: . Paper presented at IEEE International Conference on Robotics and Automation (ICRA), 2016, Stockholm, May 16-21 (pp. 4597-4604). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Model-Predictive Control with Stochastic Collision Avoidance using Bayesian Policy Optimization
2016 (English)In: IEEE International Conference on Robotics and Automation (ICRA), 2016, Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 4597-4604Conference paper, Published paper (Refereed)
Abstract [en]

Robots are increasingly expected to move out of the controlled environment of research labs and into populated streets and workplaces. Collision avoidance in such cluttered and dynamic environments is of increasing importance as robots gain more autonomy. However, efficient avoidance is fundamentally difficult since computing safe trajectories may require considering both dynamics and uncertainty. While heuristics are often used in practice, we take a holistic stochastic trajectory optimization perspective that merges both collision avoidance and control. We examine dynamic obstacles moving without prior coordination, like pedestrians or vehicles. We find that common stochastic simplifications lead to poor approximations when obstacle behavior is difficult to predict. We instead compute efficient approximations by drawing upon techniques from machine learning. We propose to combine policy search with model-predictive control. This allows us to use recent fast constrained model-predictive control solvers, while gaining the stochastic properties of policy-based methods. We exploit recent advances in Bayesian optimization to efficiently solve the resulting probabilistically-constrained policy optimization problems. Finally, we present a real-time implementation of an obstacle avoiding controller for a quadcopter. We demonstrate the results in simulation as well as with real flight experiments.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016
Series
Proceedings of IEEE International Conference on Robotics and Automation, ISSN 1050-4729
Keywords
Robot Learning, Collision Avoidance, Robotics, Bayesian Optimization, Model Predictive Control
National Category
Robotics Computer Sciences
Identifiers
urn:nbn:se:liu:diva-126769 (URN)10.1109/ICRA.2016.7487661 (DOI)000389516203138 ()
Conference
IEEE International Conference on Robotics and Automation (ICRA), 2016, Stockholm, May 16-21
Projects
CADICSELLIITNFFP6CUASSHERPA
Funder
Linnaeus research environment CADICSELLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsEU, FP7, Seventh Framework ProgrammeSwedish Foundation for Strategic Research
Available from: 2016-04-04 Created: 2016-04-04 Last updated: 2018-01-10Bibliographically approved
Danelljan, M., Khan, F. S., Felsberg, M., Granström, K., Heintz, F., Rudol, P., . . . Doherty, P. (2015). A Low-Level Active Vision Framework for Collaborative Unmanned Aircraft Systems. In: Lourdes Agapito, Michael M. Bronstein and Carsten Rother (Ed.), Lourdes Agapito, Michael M. Bronstein and Carsten Rother (Ed.), COMPUTER VISION - ECCV 2014 WORKSHOPS, PT I: . Paper presented at 13th European Conference on Computer Vision (ECCV) Switzerland, September 6-7 and 12 (pp. 223-237). Springer Publishing Company, 8925
Open this publication in new window or tab >>A Low-Level Active Vision Framework for Collaborative Unmanned Aircraft Systems
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2015 (English)In: COMPUTER VISION - ECCV 2014 WORKSHOPS, PT I / [ed] Lourdes Agapito, Michael M. Bronstein and Carsten Rother, Springer Publishing Company, 2015, Vol. 8925, p. 223-237Conference paper, Published paper (Refereed)
Abstract [en]

Micro unmanned aerial vehicles are becoming increasingly interesting for aiding and collaborating with human agents in myriads of applications, but in particular they are useful for monitoring inaccessible or dangerous areas. In order to interact with and monitor humans, these systems need robust and real-time computer vision subsystems that allow to detect and follow persons.

In this work, we propose a low-level active vision framework to accomplish these challenging tasks. Based on the LinkQuad platform, we present a system study that implements the detection and tracking of people under fully autonomous flight conditions, keeping the vehicle within a certain distance of a person. The framework integrates state-of-the-art methods from visual detection and tracking, Bayesian filtering, and AI-based control. The results from our experiments clearly suggest that the proposed framework performs real-time detection and tracking of persons in complex scenarios

Place, publisher, year, edition, pages
Springer Publishing Company, 2015
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 8925
Keywords
Visual tracking; Visual surveillance; Micro UAV; Active vision
National Category
Computer Vision and Robotics (Autonomous Systems) Computer Sciences
Identifiers
urn:nbn:se:liu:diva-115847 (URN)10.1007/978-3-319-16178-5_15 (DOI)000362493800015 ()978-3-319-16177-8 (ISBN)978-3-319-16178-5 (ISBN)
Conference
13th European Conference on Computer Vision (ECCV) Switzerland, September 6-7 and 12
Available from: 2015-03-20 Created: 2015-03-20 Last updated: 2018-02-07Bibliographically approved
Doherty, P., Kvarnström, J., Wzorek, M., Rudol, P., Heintz, F. & Conte, G. (2014). HDRC3 - A Distributed Hybrid Deliberative/Reactive Architecture for Unmanned Aircraft Systems. In: Kimon P. Valavanis, George J. Vachtsevanos (Ed.), Handbook of Unmanned Aerial Vehicles: (pp. 849-952). Dordrecht: Springer Science+Business Media B.V.
Open this publication in new window or tab >>HDRC3 - A Distributed Hybrid Deliberative/Reactive Architecture for Unmanned Aircraft Systems
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2014 (English)In: Handbook of Unmanned Aerial Vehicles / [ed] Kimon P. Valavanis, George J. Vachtsevanos, Dordrecht: Springer Science+Business Media B.V., 2014, p. 849-952Chapter in book (Other academic)
Abstract [en]

This chapter presents a distributed architecture for unmanned aircraft systems that provides full integration of both low autonomy and high autonomy. The architecture has been instantiated and used in a rotorbased aerial vehicle, but is not limited to use in particular aircraft systems. Various generic functionalities essential to the integration of both low autonomy and high autonomy in a single system are isolated and described. The architecture has also been extended for use with multi-platform systems. The chapter covers the full spectrum of functionalities required for operation in missions requiring high autonomy.  A control kernel is presented with diverse flight modes integrated with a navigation subsystem. Specific interfaces and languages are introduced which provide seamless transition between deliberative and reactive capability and reactive and control capability. Hierarchical Concurrent State Machines are introduced as a real-time mechanism for specifying and executing low-level reactive control. Task Specification Trees are introduced as both a declarative and procedural mechanism for specification of high-level tasks. Task planners and motion planners are described which are tightly integrated into the architecture. Generic middleware capability for specifying data and knowledge flow within the architecture based on a stream abstraction is also described. The use of temporal logic is prevalent and is used both as a specification language and as an integral part of an execution monitoring mechanism. Emphasis is placed on the robust integration and interaction between these diverse functionalities using a principled architectural framework.  The architecture has been empirically tested in several complex missions, some of which are described in the chapter.

Place, publisher, year, edition, pages
Dordrecht: Springer Science+Business Media B.V., 2014
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:liu:diva-113613 (URN)10.1007/978-90-481-9707-1_118 (DOI)978-90-481-9706-4 (ISBN)978-90-481-9707-1 (ISBN)
Funder
EU, FP7, Seventh Framework Programme, 600958Swedish Foundation for Strategic Research Linnaeus research environment CADICSeLLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Available from: 2015-01-26 Created: 2015-01-26 Last updated: 2018-01-11
Conte, G., Kleiner, A., Rudol, P., Korwel, K., Wzorek, M. & Doherty, P. (2013). Performance evaluation of a light weight multi-echo LIDAR for unmanned rotorcraft applications. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-1/W2: . Paper presented at Conference on Unmanned Aerial Vehicle in Geomatics (UAV-g 2013), 4-6 September 2013, Rostock, Germany.
Open this publication in new window or tab >>Performance evaluation of a light weight multi-echo LIDAR for unmanned rotorcraft applications
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2013 (English)In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-1/W2, 2013Conference paper, Published paper (Refereed)
Abstract [en]

The paper presents a light-weight and low-cost airborne terrain mapping system. The developed Airborne LiDAR Scanner (ALS) sys- tem consists of a high-precision GNSS receiver, an inertial measurement unit and a magnetic compass which are used to complement a LiDAR sensor in order to compute the terrain model. Evaluation of the accuracy of the generated 3D model is presented. Additionally, a comparison is provided between the terrain model generated from the developed ALS system and a model generated using a commer- cial photogrammetric software. Finally, the multi-echo capability of the used LiDAR sensor is evaluated in areas covered with dense vegetation. The ALS system and camera systems were mounted on-board an industrial unmanned helicopter of around 100 kilograms maximum take-off weight. Presented results are based on real flight-test data.

Series
International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 2194-9034
National Category
Robotics
Identifiers
urn:nbn:se:liu:diva-95889 (URN)000358305000016 ()
Conference
Conference on Unmanned Aerial Vehicle in Geomatics (UAV-g 2013), 4-6 September 2013, Rostock, Germany
Projects
Artificial Intelligence & Integrated Computer Systems
Funder
eLLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsEU, FP7, Seventh Framework ProgrammeLinnaeus research environment CADICS
Available from: 2013-08-07 Created: 2013-08-07 Last updated: 2016-08-22
Wzorek, M. (2011). Selected Aspects of Navigation and Path Planning in Unmanned Aircraft Systems. (Licentiate dissertation). Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>Selected Aspects of Navigation and Path Planning in Unmanned Aircraft Systems
2011 (English)Licentiate thesis, monograph (Other academic)
Abstract [en]

Unmanned aircraft systems (UASs) are an important future technology with early generations already being used in many areas of application encompassing both military and civilian domains. This thesis proposes a number of integration techniques for combining control-based navigation with more abstract path planning functionality for UASs. These techniques are empirically tested and validated using an RMAX helicopter platform used in the UASTechLab at Linköping University. Although the thesis focuses on helicopter platforms, the techniques are generic in nature and can be used in other robotic systems.

At the control level a navigation task is executed by a set of control modes. A framework based on the abstraction of hierarchical concurrent state machines for the design and development of hybrid control systems is presented. The framework is used to specify  reactive behaviors and for sequentialisation of control modes. Selected examples of control systems deployed on UASs are presented. Collision-free paths executed at the control level are generated by path planning algorithms.We propose a path replanning framework extending the existing path planners to allow dynamic repair of flight paths when new obstacles or no-fly zones obstructing the current flight path are detected. Additionally, a novel approach to selecting the best path repair strategy based on machine learning technique is presented. A prerequisite for a safe navigation in a real-world environment is an accurate geometrical model. As a step towards building accurate 3D models onboard UASs initial work on the integration of a laser range finder with a helicopter platform is also presented.

Combination of the techniques presented provides another step towards building comprehensive and robust navigation systems for future UASs.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2011. p. 107
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1509
Keywords
Path planning, motion planning, autonomous Unmanned Aircraft Systems (UAS), Hierarchical Concurrent State Machines (HCSM), UAV
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-71147 (URN)LiU-Tek-Lic-2011:48 (Local ID)978-91-7393-037-6 (ISBN)LiU-Tek-Lic-2011:48 (Archive number)LiU-Tek-Lic-2011:48 (OAI)
Presentation
2011-11-10, Alan Turing, Hus E, Campus Valla, Linköpings universitet, Linköping, 13:15 (English)
Opponent
Supervisors
Available from: 2011-11-28 Created: 2011-10-03 Last updated: 2018-01-12Bibliographically approved
Wzorek, M., Kvarnström, J. & Doherty, P. (2010). Choosing Path Replanning Strategies for Unmanned Aircraft Systems. In: Ronen Brafman, Héctor Geffner, Jörg Hoffmann, Henry Kautz (Ed.), Proceedings of the Twentieth International Conference on Automated Planning and Scheduling (ICAPS). Paper presented at International Conference on Automated Planning and Scheduling (ICAPS) (pp. 193-200). Toronto, Canada: AAAI Press
Open this publication in new window or tab >>Choosing Path Replanning Strategies for Unmanned Aircraft Systems
2010 (English)In: Proceedings of the Twentieth International Conference on Automated Planning and Scheduling (ICAPS) / [ed] Ronen Brafman, Héctor Geffner, Jörg Hoffmann, Henry Kautz, Toronto, Canada: AAAI Press , 2010, p. 193-200Conference paper, Published paper (Refereed)
Abstract [en]

Unmanned aircraft systems use a variety of techniques to plan collision-free flight paths given a map of obstacles and no- fly zones. However, maps are not perfect and obstacles may change over time or be detected during flight, which may in- validate paths that the aircraft is already following. Thus, dynamic in-flight replanning is required.Numerous strategies can be used for replanning, where the time requirements and the plan quality associated with each strategy depend on the environment around the original flight path. In this paper, we investigate the use of machine learn- ing techniques, in particular support vector machines, to choose the best possible replanning strategy depending on the amount of time available. The system has been implemented, integrated and tested in hardware-in-the-loop simulation with a Yamaha RMAX helicopter platform.

Place, publisher, year, edition, pages
Toronto, Canada: AAAI Press, 2010
Keywords
artificial intelligence, path planning, motion planning, machine learning, autonomous unmanned vehicles, autonomous aircraft systems
National Category
Computer Sciences
Identifiers
urn:nbn:se:liu:diva-59986 (URN)978-1-57735-449-9 (ISBN)
Conference
International Conference on Automated Planning and Scheduling (ICAPS)
Projects
Strategic Research Center MOVIIILinnaeus Center CADICSthe Center for Industrial Information Technology CENIITthe ELLIIT network for Information and Communication TechnologyLinkLabSwedish Research Council VR
Available from: 2010-10-01 Created: 2010-10-01 Last updated: 2018-01-12
Rudol, P., Wzorek, M. & Doherty, P. (2010). Vision-based Pose Estimation for Autonomous Indoor Navigation of Micro-scale Unmanned Aircraft Systems. In: Proceedings of the 2010 IEEE International Conference on Robotics and Automation (ICRA). Paper presented at 2010 IEEE International Conference on Robotics and Automation, May 3-8, Anchorage, Alaska, USA (pp. 1913-1920). IEEE conference proceedings
Open this publication in new window or tab >>Vision-based Pose Estimation for Autonomous Indoor Navigation of Micro-scale Unmanned Aircraft Systems
2010 (English)In: Proceedings of the 2010 IEEE International Conference on Robotics and Automation (ICRA), IEEE conference proceedings , 2010, p. 1913-1920Conference paper, Published paper (Refereed)
Abstract [en]

We present a navigation system for autonomous indoor flight of micro-scale Unmanned Aircraft Systems (UAS) which is based on a method for accurate monocular vision pose estimation. The method makes use of low cost artificial landmarks placed in the environment and allows for fully autonomous flight with all computation done on-board a UAS on COTS hardware. We provide a detailed description of all system components along with an accuracy evaluation and a time profiling result for the pose estimation method. Additionally, we show how the system is integrated with an existing micro-scale UAS and provide results of experimental autonomous flight tests. To our knowledge, this system is one of the first to allow for complete closed-loop control and goal-driven navigation of a micro-scale UAS in an indoor setting without requiring connection to any external entities.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2010
Series
Proceedings - IEEE International Conference on Robotics and Automation, ISSN 1050-4729 ; 2010
Keywords
UAV, UAS, UAS indoor navigation
National Category
Computer Sciences
Identifiers
urn:nbn:se:liu:diva-60006 (URN)10.1109/ROBOT.2010.5509203 (DOI)978-1-4244-5038-1 (ISBN)
Conference
2010 IEEE International Conference on Robotics and Automation, May 3-8, Anchorage, Alaska, USA
Available from: 2010-10-04 Created: 2010-10-04 Last updated: 2018-01-12Bibliographically approved
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