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Mojtahedzadeh, RasoulORCID iD iconorcid.org/0000-0002-2392-7146
Publications (8 of 8) Show all publications
Stoyanov, T., Vaskevicius, N., Mueller, C. A., Fromm, T., Krug, R., Tincani, V., . . . Echelmeyer, W. (2016). No More Heavy Lifting: Robotic Solutions to the Container-Unloading Problem. IEEE robotics & automation magazine, 23(4), 94-106
Open this publication in new window or tab >>No More Heavy Lifting: Robotic Solutions to the Container-Unloading Problem
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2016 (English)In: IEEE robotics & automation magazine, ISSN 1070-9932, E-ISSN 1558-223X, Vol. 23, no 4, p. 94-106Article in journal (Refereed) Published
Place, publisher, year, edition, pages
IEEE, 2016
National Category
Computer Sciences Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-53371 (URN)10.1109/MRA.2016.2535098 (DOI)000389874400011 ()2-s2.0-84981763797 (Scopus ID)
Note

Funding Agency:

EU FP7 project ROBLOG ICT-270350

Available from: 2016-11-02 Created: 2016-11-02 Last updated: 2018-07-17Bibliographically approved
Mojtahedzadeh, R. (2016). Safe Robotic Manipulation to Extract Objects from Piles: From 3D Perception to Object Selection. (Doctoral dissertation). Örebro: Örebro university
Open this publication in new window or tab >>Safe Robotic Manipulation to Extract Objects from Piles: From 3D Perception to Object Selection
2016 (English)Doctoral thesis, monograph (Other academic)
Abstract [en]

This thesis is concerned with the task of autonomous selection of objects to remove (unload) them from a pile in robotic manipulation systems. Applications such as the automation of logistics processes and service robots require an ability to autonomously manipulate objects in the environment. A collapse of a pile of objects due to an inappropriate choice of the object to be removed from the pile cannot be afforded for an autonomous robotic manipulation system. This dissertation presents an indepth analysis of the problem and proposes methods and algorithms to empower robotic manipulation systems to select a safe object from a pile elaborately and autonomously.

The contributions presented in this thesis are three-fold. First, a set of algorithms is proposed for extracting a minimal set of high level symbolic relations, namely, gravitational act and support relations, of physical interactions between objects composing a pile. The symbolic relations, extracted by a geometrical reasoning method and a static equilibrium analysis can be readily used by AI paradigms to analyze the stability of a pile and reason about the safest set of objects to be removed. Considering the problem of undetected objects and the uncertainty in the estimated poses as they exist in realistic perception systems, a probabilistic approach is proposed to extract the support relations and to make a probabilistic decision about the set of safest objects using notions from machine learning and decision theory. Second, an efficient search based algorithm is proposed in an internal representation to automatically resolve the inter-penetrations between the shapes of objects due to errors in the poses estimated by an existing object detection module. Refining the poses by resolving the inter-penetrations results in a geometrically consistent model of the environment, and was found to reduce the overall pose error of the objects. This dissertation presents the concept of minimum translation search for object pose refinement and discusses a discrete search paradigm based on the concept of depth of penetration between two polyhedrons. Third, an application centric evaluation of ranging sensors for selecting a set of appropriate sensors for the task of object detection in the design process of a real-world robotics manipulation system is presented. The performance of the proposed algorithms are tested on data sets generated in simulation and from real-world scenarios.

Place, publisher, year, edition, pages
Örebro: Örebro university, 2016. p. 105
Series
Örebro Studies in Technology, ISSN 1650-8580 ; 71
Keywords
Object Selection, Object Pose Refinement, Gravitational Support Relation, Inter-penetration Resolving, 3D Ranging Sensor Evaluation
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-51435 (URN)978-91-7529-152-9 (ISBN)
Public defence
2016-09-23, Teknikhuset, Hörsal T, Örebro universitet, Fakultetsgatan 1, Örebro, 13:15 (Swedish)
Opponent
Supervisors
Available from: 2016-07-25 Created: 2016-07-25 Last updated: 2024-01-03Bibliographically approved
Mojtahedzadeh, R. & Lilienthal, A. J. (2015). A principle of minimum translation search approach for object pose refinement. In: IEEE (Ed.), 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): . Paper presented at 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany, September 28-October 2, 2015 (pp. 2897-2903). IEEE Press
Open this publication in new window or tab >>A principle of minimum translation search approach for object pose refinement
2015 (English)In: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) / [ed] IEEE, IEEE Press, 2015, p. 2897-2903Conference paper, Published paper (Refereed)
Abstract [en]

The state-of-the-art object pose estimation approaches represent the set of detected poses together with corresponding uncertainty. The inaccurate noisy poses may result in a configuration of overlapping objects especially in cluttered environments. Under a rigid body assumption the inter-penetrations between pairs of objects are geometrically inconsistent. In this paper, we propose the principle of minimum translation search, PROMTS, to find an inter-penetration-free configuration of the initially detected objects. The target application is to automate the task of unloading shipping containers, where a geometrically consistent configuration of objects is required for high level reasoning and manipulation. We find that the proposed approach to resolve geometrical inconsistencies improves the overall pose estimation accuracy. We examine the utility of two selected search methods: A-star and Depth-Limited search. The performance of the search algorithms are tested on data sets generated in simulation and from real-world scenarios. The results show overall improvement of the estimated poses and suggest that depth-limited search presents the best overall performance.

Place, publisher, year, edition, pages
IEEE Press, 2015
Series
IEEE International Conference on Intelligent Robots and Systems, ISSN 2153-0858
Keywords
Pose estimation, robot vision, search problems, A-star search, PROMTS, cluttered environments, depth-limited search, detected poses, geometrically consistent objects configuration, inaccurate noisy poses, interpenetration-free configuration, minimum translation search, minimum translation search approach, object pose estimation approaches, object pose refinement, overlapping objects, pose estimation accuracy, rigid body assumption, shipping containers, Containers, Search problems, Shape, Solid modeling, Three-dimensional displays, Uncertainty
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-47942 (URN)10.1109/IROS.2015.7353776 (DOI)000371885403010 ()2-s2.0-84958161528 (Scopus ID)978-1-4799-9994-1 (ISBN)
Conference
2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany, September 28-October 2, 2015
Available from: 2016-02-04 Created: 2016-02-04 Last updated: 2018-01-10Bibliographically approved
Mojtahedzadeh, R., Bouguerra, A., Schaffernicht, E. & Lilienthal, A. J. (2015). Support relation analysis and decision making for safe robotic manipulation tasks. Robotics and Autonomous Systems, 71(SI), 99-117
Open this publication in new window or tab >>Support relation analysis and decision making for safe robotic manipulation tasks
2015 (English)In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 71, no SI, p. 99-117Article in journal (Refereed) Published
Abstract [en]

In this article, we describe an approach to address the issue of automatically building and using high-level symbolic representations that capture physical interactions between objects in static configurations. Our work targets robotic manipulation systems where objects need to be safely removed from piles that come in random configurations. We assume that a 3D visual perception module exists so that objects in the piles can be completely or partially detected. Depending on the outcome of the perception, we divide the issue into two sub-issues: 1) all objects in the configuration are detected; 2) only a subset of objects are correctly detected. For the first case, we use notions from geometry and static equilibrium in classical mechanics to automatically analyze and extract act and support relations between pairs of objects. For the second case, we use machine learning techniques to estimate the probability of objects supporting each other. Having the support relations extracted, a decision making process is used to identify which object to remove from the configuration so that an expected minimum cost is optimized. The proposed methods have been extensively tested and validated on data sets generated in simulation and from real world configurations for the scenario of unloading goods from shipping containers.

Place, publisher, year, edition, pages
Amsterdam: Elsevier, 2015
Keywords
Scene analysis, Machine learning, Decision making, World models, Robotic manipulation
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-40703 (URN)10.1016/j.robot.2014.12.014 (DOI)000357146000010 ()2-s2.0-84920902075 (Scopus ID)
Projects
Cognitive Robot for Automation of Logistic Processes (RobLog)
Available from: 2015-01-10 Created: 2015-01-10 Last updated: 2024-01-03Bibliographically approved
Mojtahedzadeh, R., Bouguerra, A., Schaffernicht, E. & Lilienthal, A. J. (2014). Probabilistic Relational Scene Representation and Decision Making Under Incomplete Information for Robotic Manipulation Tasks. In: Robotics and Automation (ICRA), 2014 IEEE International Conference on: . Paper presented at 2014 IEEE International Conference on Robotics and Automation (ICRA 2014, Hong Kong, China, May 31 - June 7, 2014 (pp. 5685-5690). IEEE Robotics and Automation Society
Open this publication in new window or tab >>Probabilistic Relational Scene Representation and Decision Making Under Incomplete Information for Robotic Manipulation Tasks
2014 (English)In: Robotics and Automation (ICRA), 2014 IEEE International Conference on, IEEE Robotics and Automation Society, 2014, p. 5685-5690Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we propose an approach for robotic manipulation systems to autonomously reason about their environments under incomplete information. The target application is to automate the task of unloading the content of shipping containers. Our goal is to capture possible support relations between objects in partially known static configurations. We employ support vector machines (SVM) to estimate the probability of a support relation between pairs of detected objects using features extracted from their geometrical properties and 3D sampled points of the scene. The set of probabilistic support relations is then used for reasoning about optimally selecting an object to be unloaded first. The proposed approach has been extensively tested and verified on data sets generated in simulation and from real world configurations.

Place, publisher, year, edition, pages
IEEE Robotics and Automation Society, 2014
Series
IEEE International Conference on Robotics and Automation ICRA, ISSN 1050-4729
Keywords
Containers, Manipulators, Industrial Robots, Object Detection, Support Vector Machines, Decision Making
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-40693 (URN)10.1109/ICRA.2014.6907695 (DOI)000377221105109 ()2-s2.0-84929208915 (Scopus ID)978-1-4799-3685-4 (ISBN)
Conference
2014 IEEE International Conference on Robotics and Automation (ICRA 2014, Hong Kong, China, May 31 - June 7, 2014
Projects
Cognitive Robot for Automation of Logistic Processes (RobLog)
Available from: 2015-01-10 Created: 2015-01-10 Last updated: 2024-01-03Bibliographically approved
Mojtahedzadeh, R., Stoyanov, T. & Lilienthal, A. J. (2013). Application Based 3D Sensor Evaluation: A Case Study in 3D Object Pose Estimation for Automated Unloading of Containers. In: Proceedings of the European Conference on Mobile Robots (ECMR): . Paper presented at 6th European Conference on Mobile Robots (ECMR, (pp. 313-318). IEEE conference proceedings
Open this publication in new window or tab >>Application Based 3D Sensor Evaluation: A Case Study in 3D Object Pose Estimation for Automated Unloading of Containers
2013 (English)In: Proceedings of the European Conference on Mobile Robots (ECMR), IEEE conference proceedings, 2013, p. 313-318Conference paper, Published paper (Other academic)
Abstract [en]

A fundamental task in the design process of a complex system that requires 3D visual perception is the choice of suitable 3D range sensors. Identifying the utility of 3D range sensors in an industrial application solely based on an evaluation of their distance accuracy and the noise level may lead to an inappropriate selection. To assess the actual effect on the performance of the system as a whole requires a more involved analysis. In this paper, we examine the problem of selecting a set of 3D range sensors when designing autonomous systems for specific industrial applications in a holistic manner. As an instance of this problem we present a case study with an experimental evaluation of the utility of four 3D range sensors for object pose estimation in the process of automation of unloading containers.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2013
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-33914 (URN)10.1109/ECMR.2013.6698860 (DOI)000330234600050 ()
Conference
6th European Conference on Mobile Robots (ECMR,
Available from: 2014-02-24 Created: 2014-02-24 Last updated: 2018-05-22Bibliographically approved
Mojtahedzadeh, R., Bouguerra, A. & Lilienthal, A. J. (2013). Automatic relational scene representation for safe robotic manipulation tasks. In: : . Paper presented at Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 1335-1340). IEEE
Open this publication in new window or tab >>Automatic relational scene representation for safe robotic manipulation tasks
2013 (English)Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we propose a new approach forautomatically building symbolic relational descriptions of staticconfigurations of objects to be manipulated by a robotic system.The main goal of our work is to provide advanced cognitiveabilities for such robotic systems to make them more aware ofthe outcome of their actions. We describe how such symbolicrelations are automatically extracted for configurations ofbox-shaped objects using notions from geometry and staticequilibrium in classical mechanics. We also present extensivesimulation results as well as some real-world experiments aimedat verifying the output of the proposed approach.

Place, publisher, year, edition, pages
IEEE, 2013
Series
IEEE International Conference on Intelligent Robots and Systems, ISSN 2153-0858
National Category
Robotics Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-32395 (URN)10.1109/IROS.2013.6696522 (DOI)000331367401064 ()2-s2.0-84893791900 (Scopus ID)978-1-4673-6358-7 (ISBN)
Conference
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Available from: 2013-11-14 Created: 2013-11-14 Last updated: 2018-01-11Bibliographically approved
Stoyanov, T., Mojtahedzadeh, R., Andreasson, H. & Lilienthal, A. J. (2013). Comparative evaluation of range sensor accuracy for indoor mobile robotics and automated logistics applications. Robotics and Autonomous Systems, 61(10), 1094-1105
Open this publication in new window or tab >>Comparative evaluation of range sensor accuracy for indoor mobile robotics and automated logistics applications
2013 (English)In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 61, no 10, p. 1094-1105Article in journal (Refereed) Published
Abstract [en]

3D range sensing is an important topic in robotics, as it is a component in vital autonomous subsystems such as for collision avoidance, mapping and perception. The development of affordable, high frame rate and precise 3D range sensors is thus of considerable interest. Recent advances in sensing technology have produced several novel sensors that attempt to meet these requirements. This work is concerned with the development of a holistic method for accuracy evaluation of the measurements produced by such devices. A method for comparison of range sensor output to a set of reference distance measurements, without using a precise ground truth environment model, is proposed. This article presents an extensive evaluation of three novel depth sensors — the Swiss Ranger SR-4000, Fotonic B70 and Microsoft Kinect. Tests are concentrated on the automated logistics scenario of container unloading. Six different setups of box-, cylinder-, and sack-shaped goods inside a mock-up container are used to collect range measurements. Comparisons are performed against hand-crafted ground truth data, as well as against a reference actuated Laser Range Finder (aLRF) system. Additional test cases in an uncontrolled indoor environment are performed in order to evaluate the sensors’ performance in a challenging, realistic application scenario.

Place, publisher, year, edition, pages
Elsevier, 2013
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-28856 (URN)10.1016/j.robot.2012.08.011 (DOI)000325196600006 ()2-s2.0-84883891279 (Scopus ID)
Funder
EU, FP7, Seventh Framework Programme, FP7-270350
Available from: 2013-04-29 Created: 2013-04-29 Last updated: 2018-01-11Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-2392-7146

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