Franziska Zacharias

German Aerospace Center (DLR), Köln, North Rhine-Westphalia, Germany

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Publications (24)0.41 Total impact

  • Franziska Zacharias · Christoph Borst · Sebastian Wolf · Gerd Hirzinger
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    ABSTRACT: More and more systems are developed that include several robot arms, like humanoid robots or industrial robot systems. These systems are designed for complex tasks to be solved in cooperation by the robot arms. However, the capabilities of the individual robot arms to perform given tasks or the suitability of a multi-robot system for cooperative tasks cannot be intuitively comprehended. For planning complex tasks or designing robot systems, a representation of a robot arm's workspace is needed that allows to determine from which directions objects in the workspace can be reached. In this paper, the capability map is presented. It is a representation of a robot arm's kinematic capabilities in its workspace. The capability map is used to compare existing robot arms, to support the design phase of an anthropomorphic robot arm and to enable robot workcell planning.
    International Journal of Humanoid Robotics 12/2013; 10(4):1350031. DOI:10.1142/S021984361350031X · 0.41 Impact Factor
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    ABSTRACT: Assistive robotic systems in household or industrial production environments get more and more capable of performing also complex tasks which previously only humans were able to do. As robots are often equipped with two arms and hands, similar manipulations can be executed. The robust programming of such devices with a very large number of degrees of freedom (DOFs) compared with single industrial robot arms however is laborious if done joint-wise. Two major directions to overcome this problem have been previously proposed. The programming by demonstration (PbD) approach, where human arm and recently also hand motions are tracked, segmented and re-executed in an adaptive way on the robotic system and the high-level planning approach which tries to generate a task sequence on a logical level and attributes geometric information as necessary to generate artificial trajectories to solve the task. Here we propose to combine the best of both worlds. For the very complex motion generation for a robotic hand, a rather direct approach to assign manipulation actions from human demonstration to a human hand is taken. For the combination of different basic manipulation actions the task constraints are segmented from the demonstration action and used to generate a task oriented plan. This plan is validated against the robot kinematic and geometric constraints and then a geometric motion planner can generate the necessary robot motions to fulfill the task execution on the system.
    01/2012: pages 59-122; , ISBN: 978-3-642-29040-4
  • Franziska Zacharias
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    ABSTRACT: In this chapter, the technical terms relevant for this work are introduced. Furthermore, on different levels of abstraction, components are described that are necessary for a service robot to solve manipulation tasks. The state of the art in high-level logical planning, also called task planning, is analyzed. Here the focus is on robotic manipulation problems. In the subsequent sections, it is analyzed how low-level planners like path planners, grasp planners and robot placement planners are used to solve the subtasks involved in manipulation, e.g. moving to an object, grasping it, and transporting it to a different position. It is examined whether knowledge representations are used to speed up or facilitate planning processes. Furthermore, it is outlined how the high-level and low-level planning can benefit from the use of knowledge representations.
    01/2012: pages 7-26;
  • Franziska Zacharias
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    ABSTRACT: This chapter summarizes the achievements presented in this book, provides some concluding remarks as well as an outlook on potential future applications and future research directions.
    Knowledge Representations for Planning Manipulation Tasks, 01/2012: pages 127-129;
  • Franziska Zacharias
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    ABSTRACT: One field of application for the capability map is the visualization and inspection of the robot arm workspace. In this chapter, the workspace is visualized for several robot arms and discussed with respect to potential tasks. Furthermore, the capability map is used to objectively evaluate the quality of a setup for human robot interaction.
    Knowledge Representations for Planning Manipulation Tasks, 01/2012: pages 71-92;
  • Franziska Zacharias
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    ABSTRACT: In general, every robot arm is designed differently, and therefore has different kinematic capabilities. These capabilities can result in directional structures specific to workspace regions. The robot’s ability to manipulate objects depends on the relative position of the objects. Two-handed manipulation is limited to a region where the workspaces of both arms overlap. The best performance is achieved in an even smaller subspace. In the previous chapter, requirements were identified that a representation of the reachability throughout the workspace has to fulfill. The reachability sphere map is a representation that meets these requirements. The choice of this name becomes clear later. It was first introduced in [117]. As a first step, the construction of the reachability sphere map is described. A visualization scheme is introduced for the representation. It allows the detection of structure in the workspace and enables its visualization. In a second step, a compact abstraction is proposed for the data of the reachability sphere map. The approach is illustrated using a DLR light weight arm (LWR ).
    Knowledge Representations for Planning Manipulation Tasks, 01/2012: pages 37-69;
  • Franziska Zacharias
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    ABSTRACT: This chapter demonstrates the use of the capability map in planning tasks. Using two examples, it is demonstrated how the capability map can be used to restrict the search space. The algorithms thus address the gap between task planning and path planning that is indicated in Figure 6.1 by the red rectangle. In the first application covered in this chapter a robot is placed to perform a given trajectory. Its suitability for the task is evaluated. In the second application the capability map is used to obtain good parameters for a path planner and bias the path planning process.
    Knowledge Representations for Planning Manipulation Tasks, 01/2012: pages 93-123;
  • Franziska Zacharias
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    ABSTRACT: Robot performance indices evaluate how well a robot can apply forces or move during a specific task or throughout the whole workspace. Hence, they potentially contribute to a general description of the versatile workspace that is the focus of this book. In this chapter, criteria used in robot arm design are compared and evaluated with respect to their objectives. It is identified whether these criteria provide measures to evaluate a robotic arm’s kinematic capabilities with respect to reachability and manipulation of objects throughout the workspace. It is determined whether they can be used to represent the robot arm workspace. Furthermore state of the art approaches to model the workspace are analyzed and evaluated.
    Knowledge Representations for Planning Manipulation Tasks, 01/2012: pages 27-35;
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    ABSTRACT: This paper presents the graspability map, a novel approach to represent for a particular object the positions and orientations that a given mechanical hand can adopt to achieve a force closure precision grasp. The algorithm is based on the intersection between the fingertip workspaces and the object, plus the verification of a necessary condition for force closure grasps. The maps are computed offline and can be used for comparing the grasp capabilities of different mechanical hands with respect to some benchmark objects. The maps have also potential applications in online grasp and manipulation planning.
    IEEE/RSJ Int. Conf. Intelligent Robots and Systems - IROS; 09/2011
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    ABSTRACT: It contradicts the human's expectations when humanoid robots move awkwardly during manipulation tasks. The unnatural motion may be caused by awkward start or goal configurations or by probabilistic path planning processes that are often used. This paper shows that the choice of an arm's target configuration strongly effects planning time and how human-like a planned path appears. Human-like goal configurations are found using a criterion from ergonomics research. The knowledge which pose of the Tool Center Point (TCP) can be reached in a natural manner is encapsulated in a restricted reachability map for the robot arm.
    IEEE International Conference on Robotics and Automation (ICRA 2011), Shanghai, Cn; 05/2011
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    ABSTRACT: Abstract-This article accompanies a video that presents a bimanual haptic device composed of two DLR/KUKA Light weight Robot (LWR) arms. The LWRs have similar dimensions to human arms, and can be operated in torque and position control mode at an update rate of 1kHz. The two robots are mounted behind the user, such that the intersecting workspace of the robots and the human arms becomes maximal. In order to enhance user interaction, various hand interfaces and additional tactile feedback devices can be used together with the robots. The presented system is equipped with a thorough safety architecture that assures safe operation for human and robot. Additionally, sophisticated control strategies improve performance and guarantee stability. The introduced haptic system is well suited for versatile applications in remote and virtual environments, especially for large unsealed movements.
    IEEE International Conference on Robotics and Automation, ICRA 2011, Shanghai, China, 9-13 May 2011; 01/2011
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    Franziska Zacharias · I.S. Howard · Thomas Hulin · Gerd Hirzinger
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    ABSTRACT: In virtual assembly verification or remote maintenance tasks, bimanual haptic interfaces play a crucial role in successful task completion. This paper proposes a method for objectively comparing how well a haptic interface covers the reachable workspace of human arms. Two system configurations are analyzed for a recently introduced haptic device that is based on two DLR-KUKA light weight robots: the standard configuration, where the device is opposite the human operator, and the ergonomic configuration, where the haptic device is mounted behind the human operator. The human operator directly controls the robotic arms using handles. The analysis is performed using a representation of the robot arm workspace. The merits of restricting the comparisons to the most significant regions of the human workspace are discussed. Using this method, a greater workspace correspondence for the ergonomic configuration was shown.
    Intelligent Robots and Systems (IROS), 2010 IEEE/RSJ International Conference on; 11/2010
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    F. Zacharias · D. Leidner · F. Schmidt · C. Borst · G. Hirzinger
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    ABSTRACT: In autonomous bimanual operation of a robot, parallelized planning and execution of a task is essential. Elements of a task have different functional and spatial relationships. They may depend on each other and have to be executed in a specific order or they may be independent and their order can be determined freely. Consequently, individual actions can be planned and executed in parallel or not. In a proof of concept, this paper shows that the structure of a task and its mapping onto subordinate planners can significantly influence planning speed and task execution. Independent tasks are planned using two parallel path planners. Dependent tasks are planned using one path planner for both arms. Using a simple, yet expandable experimentation scenario, the resulting recommendations for parameterizing path planners are verified on a humanoid robot. For execution on the real robot a violation of the rigid body model used in path planners had to be addressed.
    Intelligent Robots and Systems (IROS), 2010 IEEE/RSJ International Conference on; 11/2010
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    F. Zacharias · W. Sepp · C. Borst · G. Hirzinger
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    ABSTRACT: Humanoid robots are envisioned in general household tasks. To be able to fulfill a given task the robot needs to be equipped with knowledge concerning the manipulation and interaction in the environment and with knowledge about its own capabilities. When performing actions, e.g. opening doors or imitating human reach to grasp movements special 3-d trajectories are followed with the robot's end-effector. These trajectories can not be executed in every part of the robot's arm workspace. Therefore a task planner has to determine if and how additional degrees of freedom such as the robot's upper body or the robot's base can be moved in order to execute the task-specific trajectory. An approach is presented that computes placements for a mobile manipulator online given a task-related 3-d trajectory. A discrete representation of the robot arm's reachable workspace is used. Task-specific trajectories are interpreted as patterns and searched in the reachability model using multi-dimensional correlation. The relevance of the presented approach is demonstrated in simulated positioning tasks.
    Humanoid Robots, 2009. Humanoids 2009. 9th IEEE-RAS International Conference on; 01/2010
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    F. Zacharias · C. Borst · G. Hirzinger
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    ABSTRACT: In service robotic tasks, the ability to grasp and handle objects is mandatory. Short response times with respect to execution of commanded tasks are necessary. Planning in general and grasp planning in particular should happen online. We extend the online grasp planner by Borst et al. to generate reachable grasps while preserving the integrity and modularity of the grasp planner. To achieve this a representation of the reachable space of a robot arm is used to determine a grasp's reachability. Furthermore we show the influence of obstacles on the reachability throughout the workspace. A method to include obstacles into the representation of reachability is sketched. The resulting representation is used by the grasp planner. The performance of the algorithms is evaluated by measuring their computation times. Even in the worst case our grasp planner outperforms comparable state of the art approaches.
    Advanced Robotics, 2009. ICAR 2009. International Conference on; 07/2009
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    ABSTRACT: Research on humanoid robots for use in servicing tasks, e.g. fetching and delivery, attracts steadily more interest. With “Rollin' Justin” a mobile robotic system and research platform is presented that allows sophisticated control algorithms and dexterous manipulation. This video gives an overview of the mobile humanoid robotic system “Rollin' Justin” with special emphasis on mechanical design features, control issues and high-level system capabilities such as human robot interaction.
    ICRA2009, International Conference on Robotics and Automation; 06/2009
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    ABSTRACT: Research on humanoid robots for use in servicing tasks, e.g. fetching and delivery, attracts steadily more interest. With "Rollin' Justin" a mobile robotic system and research platform is presented that allows sophisticated control algorithms and dexterous manipulation. This video gives an overview of the mobile humanoid robotic system "Rollin' Justin" with special emphasis on mechanical design features, control issues and high-level system capabilities such as human robot interaction.
    Proceedings of the 2009 IEEE international conference on Robotics and Automation; 05/2009
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    Franziska Zacharias · Christoph Borst · Gerd Hirzinger
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    ABSTRACT: Humans use learned knowledge to solve reaching tasks and to manipulate objects and tools.We believe that representations of manipulation characteristics of an object and of the reaching capabilities of a robotic arm can speed up low-level planners, like grasp planners. They also enable sophisticated scene analysis and reasoning for high-level planners, like task planners. We present object-specific grasp maps to encapsulate an object’s manipulation characteristics. A grasp planner is shown to use the grasps maps and a representation of the reachable workspace. The exploitation of the provided knowledge focuses the planning on regions of the object that are promising to yield high quality grasps. Speed ups of factor 2-12 are reported.
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    ABSTRACT: Analytic modeling, imitation, and experiencebased learning are three approaches that enable robots to acquire models of their morphology and skills. In this paper, we combine these three approaches to efficiently gather training data to learn a model of reachability for a typical mobile manipulation task: approaching a worksurface in order to grasp an object. The core of the approach is experience-based learning. For more effective exploration, we use capability maps as analytic models of the robot’s dexterity to constrain the area in which the robot gathers training data. Furthermore, we acquire a human model of reachability from human motion data and use it to bias exploration. The acquired training data is used to learn Action-Related Places. In an empirical evaluation we demonstrate that combining the three approaches enables the robot to acquire accurate models with far less data than with our previous exploration strategy.
    International Conference on Humanoid Robots (Humanoids); 01/2009
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    Franziska Zacharias · Christoph Borst · Michael Beetz · G. Hirzinger
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    ABSTRACT: For mobile manipulators envisioned in home environments a kitchen scenario provides a challenging testbed for numerous skills. Diverse manipulation actions are required, e.g. simple pick and place for moving objects and constrained motions for opening doors and drawers. The robot kinematics and link limits however are restrictive. Therefore especially a constrained trajectory will not be executable from arbitrary placements of the mobile manipulator. A two stage approach is presented to position a mobile manipulator to execute constrained linear trajectories as needed for opening drawers. In a first stage, a representation of a robot armpsilas reachable workspace is computed. Pattern recognition techniques are used to find regions in the workspace representation where these trajectories are possible. A set of trajectories results. In the second stage mobile manipulator placements are computed and the corresponding trajectories are checked for collisions. Compared to a brute force search through the workspace, the success rate of finding a mobile manipulator placement can be increased from 2% to 70%.
    Intelligent Robots and Systems, 2008. IROS 2008. IEEE/RSJ International Conference on; 10/2008