Rollin' Justin - Design considerations and realization of a mobile platform for a humanoid upper body

Fuchs, Matthias; Borst, Christoph; Giordano, Paolo Robuffo; Baumann, Andrea; Kraemer, Eileen; Langwald, J.; Gruber, Robin; Seitz, Nikolaus; Plank, G.; Kunze, K.D.; Burger, Robert; Schmidt, Florian; Wimboeck, Thomas; Hirzinger, G.

doi:10.1109/robot.2009.5152464

Cited by 94 publications

(62 citation statements)

References 10 publications

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“…Justin consists of two lightweight robot arms (2x7DOF) [1], a torso with head (5DOF), two hands (2x12DOF) [2], and an omnidirectional base [3]. The whole upper body is constructed following lightweight principles.…”

Section: Introductionmentioning

confidence: 99%

Catching flying balls and preparing coffee: Humanoid Rollin'Justin performs dynamic and sensitive tasks

Bäuml¹,

Schmidt²,

Wimböck³

et al. 2011

2011 IEEE International Conference on Robotics and Automation

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Abstract-The mobile humanoid Rollin'Justin is a versatile experimental platform for research in manipulation tasks. Previously, different state of the art control methods and first autonomous task execution scenarios have been demonstrated. In this video two new applications with challenging task requirements are presented. One is the catching of one or even two flying balls using all of Justin's degrees of freedom. The other is the autonomous preparation of coffee. Both applications need adequate sensors to support local referencing. The required precision in position and timing is realized in software, using the sensor information, taking the varying precision of Justin's kinematic sub-chains into account and handling all timings in sub-millisecond range.

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Section: Introductionmentioning

confidence: 99%

Catching flying balls and preparing coffee: Humanoid Rollin'Justin performs dynamic and sensitive tasks

Bäuml¹,

Schmidt²,

Wimböck³

et al. 2011

2011 IEEE International Conference on Robotics and Automation

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“…State-of-the-art methods propose the use of external force sensors or torque sensors mounted on the robot [31], or compliant joints located on the robot upper part in order to measure the contact forces, as with the robots like Cody [17], [30], [32], PR2 [33], Justin [34] and IRL-1 [35]. However, these methods limit the surface on the robot body where physical interaction can take place.…”

Section: A Direct Physical Interactionmentioning

confidence: 99%

Comparison of Interaction Modalities for Mobile Indoor Robot Guidance: Direct Physical Interaction, Person Following, and Pointing Control

Jevtić

Doisy

Parmet

et al. 2015

IEEE Trans. Human-Mach. Syst.

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Abstract-Three advanced natural interaction modalities for mobile robot guidance in an indoor environment were developed and compared using two tasks and quantitative metrics to measure performance and workload. The first interaction modality is based on direct physical interaction requiring the human user to push the robot in order to displace it. The second and third interaction modalities exploit a 3D vision-based human-skeleton tracking allowing the user to guide the robot by either walking in front of it or by pointing towards a desired location. In the first task, the participants were asked to guide the robot between different rooms in a simulated physical apartment requiring rough movement of the robot through designated areas. The second task evaluated robot guidance in the same environment through a set of waypoints, which required accurate movements. The three interaction modalities were implemented on a generic differential drive mobile platform equipped with a pan-tilt system and a Kinect camera. Task completion time and accuracy were used as metrics to assess the users' performance, while the NASA-TLX questionnaire was used to evaluate the users' workload. A study with 24 participants indicated that choice of interaction modality had significant effect on completion time (F(2,61)=84.874, p<0.001), accuracy (F(2,29)=4.937, p=0.016), and workload (F(2,68)=11.948, p<0.001). The direct physical interaction required less time, provided more accuracy and less workload than the two contactless interaction modalities. Between the two contactless interaction modalities, the person-following interaction modality was systematically better than the pointing-control one: the participants completed the tasks faster with less workload. Index Terms-Human-robot interaction, mobile robots, direct physical interaction, person tracking, person following, gesture recognition

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“…The humanoid robot Rollin' Justin' [29] consists of an humanoid upper body system mounted on a mobile base with variable footprint. The humanoid upper body is composed of a 3-DOF torso, two 7-DOF arms, two 4-finger hands and a 2-DOF head.…”

Section: Justinmentioning

confidence: 99%

Knowledge Representations for Planning Manipulation Tasks

Zacharias

2012

Cognitive Systems Monographs

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A complex manipulation task for a robot has to be planned on different levels of abstraction before it can be executed on a real system. At a high-level of abstraction, a task planner generates a sequence of subtasks from a given abstract command, its pre-and postconditions and a current world model. To restrict the already huge search space of the task planner, aspects like geometric information are not contained in the world model at this level. The task planner then assigns the subtasks to planners on a lower level of abstraction like path planners or grasp planners. These planners rely on geometric information to compute their solutions. However, without considering geometric information the task planner cannot determine good parameters for these low-level planners. Therefore, the derived plans may not be valid for a given situation and may require backtracking. Knowledge representations can bridge the gap between these planning levels and thereby enable scene reasoning. The kinematic capabilities of a robot are one aspect the task planner has to be aware of. Therefore this thesis introduces a novel general representation of the kinematic capabilities of a robot arm. The versatile workspace is defined to describe in which orientations the end effector attached to a robot arm can reach a position. The capability map is a calculable representation of the versatile workspace that allows to determine how well regions of the workspace are reachable. It accurately represents the versatile workspace of arbitrary arm kinematics and enables autonomous taskspecific reasoning. Furthermore, its visualization scheme is intuitively comprehensible for the human. The versatile applicability of the capability map is shown by examples from several distinct application domains. In human-robot interaction, the capability map is used to objectively evaluate a bi-manual interface for tele-operation. The results can also supplement the design process for humanoid robot design. In low-level geometric planning, the capability map is used to reduce the search space and to parameterize path planners and grasp planners. Thus, in a manipulation task for a humanoid robot more human-like motion is planned while simultaneously the computation time is reduced. In high-level task reasoning, the capability map is used to evaluate how well a robot is suited for a task. In an example a humanoid robot has to perform a task involving 3D trajectories. Regions are extracted from the capability map that permit the task execution and the suitability of the robot is inferred.

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Rollin' Justin - Design considerations and realization of a mobile platform for a humanoid upper body

Cited by 94 publications

References 10 publications

Catching flying balls and preparing coffee: Humanoid Rollin'Justin performs dynamic and sensitive tasks

Catching flying balls and preparing coffee: Humanoid Rollin'Justin performs dynamic and sensitive tasks

Comparison of Interaction Modalities for Mobile Indoor Robot Guidance: Direct Physical Interaction, Person Following, and Pointing Control

Knowledge Representations for Planning Manipulation Tasks

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