The DLR bimanual haptic device with optimized workspace

2015 IEEE International Conference on Robotics and Automation (ICRA)

Newcombe

et al. 2015

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This work integrates visual and physical constraints to perform real-time depth-only tracking of articulated objects, with a focus on tracking a robot's manipulators and manipulation targets in realistic scenarios. As such, we extend DART, an existing visual articulated object tracker, to additionally avoid interpenetration of multiple interacting objects, and to make use of contact information collected via torque sensors or touch sensors. To achieve greater stability, the tracker uses a switching model to detect when an object is stationary relative to the table or relative to the palm and then uses information from multiple frames to converge to an accurate and stable estimate. Deviation from stable states is detected in order to remain robust to failed grasps and dropped objects. The tracker is integrated into a shared autonomy system in which it provides state estimates used by a grasp planner and the controller of two anthropomorphic hands. We demonstrate the advantages and performance of the tracking system in simulation and on a real robot. Qualitative results are also provided for a number of challenging manipulations that are made possible by the speed, accuracy, and stability of the tracking system.

“…The system consists of the multimodal human machine interface HUG [16] and the remote robot SpaceJustin, which is a modified version of DLR's humanoid robot Justin [17] (see Fig. 4).…”

Section: A Systemmentioning

confidence: 99%

Depth-based tracking with physical constraints for robot manipulation

Schmidt

2015 IEEE International Conference on Robotics and Automation (ICRA)

Newcombe

et al. 2015

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“…The most relevant hardware component of the VR-OOS framework is the DLR Bimanual Haptic Device HUG [34] shown in Fig. 10.…”

Section: The Dlr Bimanual Haptic Device Hugmentioning

confidence: 99%

VR-OOS: The DLR's virtual reality simulator for telerobotic on-orbit servicing with haptic feedback

Sagardia

2015 IEEE Aerospace Conference

Hulin

et al. 2015

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The growth of space debris is becoming a severe issue that urgently requires mitigation measures based on maintenance, repair, and de-orbiting technologies. Such on-orbit servicing (OOS) missions, however, are delicate and expensive. Virtual Reality (VR) enables the simulation and training in a flexible and safe environment, and hence has the potential to drastically reduce costs and time, while increasing the success rate of future OOS missions. This paper presents a highly immersive VR system with which satellite maintenance procedures can be simulated interactively using visual and haptic feedback. The system can be used for verification and training purposes for human and robot systems interacting in space. Our framework combines unique realistic virtual reality simulation engines with advanced immersive interaction devices. The DLR bimanual haptic device HUG is used as the main user interface. The HUG is equipped with two light-weight robot arms and is able to provide realistic haptic feedback on both human arms. Additional devices provide vibrotactile and electrotactile feedback at the elbow and the fingertips. A particularity of the realtime simulation is the fusion of the Bullet physics engine with our haptic rendering algorithm, which is an enhanced version of the Voxmap-Pointshell Algorithm. Our haptic rendering engine supports multiple objects in the scene and is able to compute collisions for each of them within 1 msec, enabling realistic virtual manipulation tasks even for stiff collision configurations. The visualization engine ViSTA is used during the simulation to achieve photo-realistic effects, increasing the immersion. In order to provide a realistic experience at interactive frame rates, we developed a distributed system architecture, where the load of computing the physics simulation, haptic feedback and visualization of a complex scene is transferred to dedicated machines. The implementations are presented in detail and the performance of the overall system is validated. Additionally, a preliminary user study in which the virtual system is compared to a physical test bed shows the suitability of the VR-OOS framework.

“…A human demonstrates the assembly task by moving a DLR Light Weight Robot (LWR) [21] using a teach tool that is attached to the seventh joint of the robot, see Figure 6. The robot arm is equipped with torque sensors, which enables backdriveable behavior and operation with gravity compensation.…”

Section: Methodsmentioning

confidence: 99%

Identification of contact formations: Resolving ambiguous force torque information

2012 IEEE International Conference on Robotics and Automation

Roa

Preusche

et al. 2012

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This paper presents the identification of contact formations using force torque information. As force torque measurements do not map uniquely to their corresponding contact formations, three steps are performed: Initially, the wrench space for each contact formation is computed automatically. Then, a contact formation graph is augmented with a similarity index that reflects the similarity of contact formations with respect to their spanned wrench spaces. A particle filter is used to represent the likeliness of a contact formation given a force torque measurement. Finally, this probability distribution is resolved taking the similarity index, the transitions of the contact formation graph and the history of identified contact formations into account. This allows the recognition of the order of demonstrated contact formations by a measured set of forces and torques. The approach is verified by experiments.