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  • 1.
    Mojtahedzadeh, Rasoul
    Örebro University, School of Science and Technology.
    Safe Robotic Manipulation to Extract Objects from Piles: From 3D Perception to Object Selection2016Doctoral 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.

  • 2.
    Mojtahedzadeh, Rasoul
    et al.
    Örebro University, School of Science and Technology.
    Bouguerra, Abdelbaki
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Automatic relational scene representation for safe robotic manipulation tasks2013Conference 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.

  • 3.
    Mojtahedzadeh, Rasoul
    et al.
    Örebro University, School of Science and Technology.
    Bouguerra, Abdelbaki
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Probabilistic Relational Scene Representation and Decision Making Under Incomplete Information for Robotic Manipulation Tasks2014In: Robotics and Automation (ICRA), 2014 IEEE International Conference on, IEEE Robotics and Automation Society, 2014, 5685-5690 p.Conference 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.

  • 4.
    Mojtahedzadeh, Rasoul
    et al.
    Örebro University, School of Science and Technology.
    Bouguerra, Abdelbaki
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J
    Örebro University, School of Science and Technology.
    Support relation analysis and decision making for safe robotic manipulation tasks2015In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 71, no SI, 99-117 p.Article in journal (Refereed)
    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.

  • 5.
    Mojtahedzadeh, Rasoul
    et al.
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    A principle of minimum translation search approach for object pose refinement2015In: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) / [ed] IEEE, IEEE Press, 2015, 2897-2903 p.Conference 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.

  • 6.
    Mojtahedzadeh, Rasoul
    et al.
    Örebro University, School of Science and Technology. Univ Örebro, Ctr Appl Autonomous Sensor Syst AASS, Örebro, Sweden.
    Stoyanov, Todor
    Örebro University, School of Science and Technology. Univ Örebro, Ctr Appl Autonomous Sensor Syst AASS, Örebro, Sweden.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology. Univ Örebro, Ctr Appl Autonomous Sensor Syst AASS, Örebro, Sweden.
    Application Based 3D Sensor Evaluation: A Case Study in 3D Object Pose Estimation for Automated Unloading of Containers2013In: Proceedings of the European Conference on Mobile Robots (ECMR), IEEE conference proceedings, 2013, 313-318 p.Conference 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.

  • 7.
    Stoyanov, Todor
    et al.
    Örebro University, School of Science and Technology.
    Mojtahedzadeh, Rasoul
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Comparative evaluation of range sensor accuracy for indoor mobile robotics and automated logistics applications2013In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 61, no 10, 1094-1105 p.Article in journal (Refereed)
    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.

  • 8.
    Stoyanov, Todor
    et al.
    Örebro University, School of Science and Technology.
    Vaskevicius, Narunas
    Jacobs University Bremen, Bremen, Germany.
    Mueller, Christian Atanas
    Jacobs University Bremen, Bremen, Germany.
    Fromm, Tobias
    Jacobs University Bremen, Bremen, Germany.
    Krug, Robert
    Örebro University, School of Science and Technology.
    Tincani, Vinicio
    University of Pisa, Pisa, Italy.
    Mojtahedzadeh, Rasoul
    Örebro University, School of Science and Technology.
    Kunaschk, Stefan
    Bremer Institut für Produktion und Logistik (BIBA), Bremen, Germany.
    Ernits, R. Mortensen
    Bremer Institut für Produktion und Logistik (BIBA), Bremen, Germany.
    Canelhas, Daniel R.
    Örebro University, School of Science and Technology.
    Bonilla, Manuell
    University of Pisa, Pisa, Italy.
    Schwertfeger, Soeren
    ShanghaiTech University, Shanghai, China.
    Bonini, Marco
    Reutlingen University, Reutlingen, Germany.
    Halfar, Harry
    Reutlingen University, Reutlingen, Germany.
    Pathak, Kaustubh
    Jacobs University Bremen, Bremen, Germany.
    Rohde, Moritz
    Bremer Institut für Produktion und Logistik (BIBA), Bremen, Germany.
    Fantoni, Gualtiero
    University of Pisa, Pisa, Italy.
    Bicchi, Antonio
    Università di Pisa & Istituto Italiano di Tecnologia, Genova, Italy.
    Birk, Andreas
    Jacobs University, Bremen, Germany.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Echelmeyer, Wolfgang
    Reutlingen University, Reutlingen, Germany.
    No More Heavy Lifting: Robotic Solutions to the Container-Unloading Problem2016In: IEEE robotics & automation magazine, ISSN 1070-9932, E-ISSN 1558-223X, Vol. 23, no 4, 94-106 p.Article in journal (Refereed)
1 - 8 of 8
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