Introducing RH5 Manus: A Powerful Humanoid Upper Body Design for Dynamic Movements

Boukheddimi, Melya; Kumar, Shivesh; Peters, Heiner; Mronga, Dennis; Budhiraja, Rohan; Kirchner, Frank

doi:10.1109/icra46639.2022.9811843

Cited by 15 publications

(4 citation statements)

References 19 publications

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“…Further possible extension of this work includes dealing with seriesparallel hybrid robots, e.g. in humanoids with series elastic actuators [39], [40]. In addition to the Matlab implementation, a C++ implementation of the generic version of the algorithm is planned in Hybrid Robot Dynamics (HyRoDyn) [41] software framework.…”

Section: Discussionmentioning

confidence: 99%

A Recursive Lie-Group Formulation for the Second-Order Time Derivatives of the Inverse Dynamics of Parallel Kinematic Manipulators

Müller

Kumar

Kordik

2023

IEEE Robot. Autom. Lett.

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Series elastic actuators (SEA) were introduced for serial robotic arms. Their model-based trajectory tracking control requires the second time derivatives of the inverse dynamics solution, for which algorithms were proposed. Trajectory control of parallel kinematics manipulators (PKM) equipped with SEAs has not yet been pursued. Key element for this is the computationally efficient evaluation of the second time derivative of the inverse dynamics solution. This has not been presented in the literature, and is addressed in the present paper for the first time. The special topology of PKM is exploited reusing the recursive algorithms for evaluating the inverse dynamics of serial robots. A Lie group formulation is used and all relations are derived within this framework. Numerical results are presented for a 6-DOF Gough-Stewart platform (as part of an exoskeleton), and for a planar PKM when a flatness-based control scheme is applied.

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Section: Discussionmentioning

confidence: 99%

A Recursive Lie-Group Formulation for the Second-Order Time Derivatives of the Inverse Dynamics of Parallel Kinematic Manipulators

Müller

Kumar

Kordik

2023

IEEE Robot. Autom. Lett.

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“…The ITPNet model using different task and environment features are evaluated on a 22 DOF upper torso humanoid system "RH5 Manus" [1]. RH5 Manus has two 7 DOF manipulators with a twofinger gripper attached to its left manipulator and a three-finger gripper attached to its right manipulator.…”

Section: Methodsmentioning

confidence: 99%

ITPNet: Initial Trajectory Prediction Network - A deep learning framework for predicting initial trajectories to warm start optimization-based motion planners

Natarajan,

Kirchner

2024

Preprint

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Motion planning for robot manipulators in a cluttered environment is one of the most researched areas in the field of robotics.For high degrees of freedom (DOF) robotic systems, optimization-based motion planners are often preferred for trajectory planning, as they are computationally efficient and provide a smooth, locally optimal solution.To converge to an optimal solution, an optimization-based motion planner needs a good initial trajectory. Finding an initial trajectory, which is not far away from the basin of attraction of the optimum is not a trivial task. In this work, we propose an Initial Trajectory Prediction Network (ITPNet), a deep neural network framework for predicting an initial trajectory to warm start optimization-based motion planners.Given a planning task in the form of task and environment features, the ITPNet predicts the best initial trajectory for warm starting an optimization-based motion planner. Two different task features: joint and Cartesian features, and three types of environment features extracted using: Principal Component Analysis (PCA), Variational Autoencoder (VAE), and Signed Spatial Distance (SSD) techniques are compared.The learned models are evaluated on an upper-torso humanoid system in two different scenarios. The results show that the model, using the Cartesian task and the SSD-based environment features, efficiently learns the mapping between the planning tasks and the optimal trajectories. Warm-starting the planner with the predicted initial trajectory, even for an unseen environment results in a higher success rate and requires fewer iterations.

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“…As part of the Horizon 2020 Strategic Research Cluster (SRC) and PERASPERA projects, the DFKI has completed research in the field of space robotics like testing of generic interfaces [5] or the development of control methods, for the manufacturing of large structures in space [6]. The DFKI can also draw on experience in the development of generic gripping systems, such as those used in a humanoid robot called RH5 [7] as well as on experience in the development of actuators for space applications [8].…”

Section: Related Work and State Of The Artmentioning

confidence: 99%

Design and testing of mechanical gripping tools for On Orbit assembling

Mulsow,

Dabrowski,

Mallwitz

et al. 2024

J. Phys.: Conf. Ser.

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On-orbit servicing, assembly and manufacturing (OSAM) opens a new frontier for robotic systems. A gripping tool for such applications must meet several requirements of space mechanics, such as safety, precision and reliability, while functioning in space conditions. This paper presents a development cycle of such tools designed for the assembly of a small satellite antenna on the International Space Station. Two different grippers, driven by a common drive unit, are presented, conforming to a Multi-Purpose-Tool (MPT) for orbital robotic systems and meeting the requirements of a defined OSAM mission. Both design drivers and concepts and specific component selection are described. Proposed mechanical solutions for safe gripping of objects including space tribology aspects are covered. To adapted to operations not foreseen, the grippers and the drive unit can be reconfigured. The tool architecture presented promotes modularity, scalability, reusability and convertibility of designs, thus facilitating rapid integration in similar missions. A test campaign for critical requirements will be in place to ensure reliable performance of the tools for use in the space environment.

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Introducing RH5 Manus: A Powerful Humanoid Upper Body Design for Dynamic Movements

Cited by 15 publications

References 19 publications

A Recursive Lie-Group Formulation for the Second-Order Time Derivatives of the Inverse Dynamics of Parallel Kinematic Manipulators

A Recursive Lie-Group Formulation for the Second-Order Time Derivatives of the Inverse Dynamics of Parallel Kinematic Manipulators

ITPNet: Initial Trajectory Prediction Network - A deep learning framework for predicting initial trajectories to warm start optimization-based motion planners

Design and testing of mechanical gripping tools for On Orbit assembling

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