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Efficient Closed-Form Task Space Manipulability for a 7-DOF Serial Robot

Chair of Automatic Control Engineering (LSR), Department of Electrical and Computer Engineering, Technical University of Munich (TUM), 80333 Munich, Germany
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Robotics 2019, 8(4), 98; https://doi.org/10.3390/robotics8040098
Received: 29 September 2019 / Revised: 14 November 2019 / Accepted: 23 November 2019 / Published: 26 November 2019
(This article belongs to the Special Issue Kinematics and Robot Design II, KaRD2019)
With the increasing demand for robots to react and adapt to unforeseen events, it is essential that a robot preserves agility at all times. While manipulability is a common measure to quantify agility at a given joint configuration, an efficient direct evaluation in task space is usually not possible with conventional methods, especially for redundant robots with an infinite number of Inverse Kinematic solutions. Yet, this is essential for global online optimization of a robot posture. In this work, we derive analytical expressions for a conventional 7-degrees of freedom (7-DOF) serial robot structure, which enable the direct evaluation of manipulability from a reduced task space parametrization. The resulting expressions allow array operation and thus achieve very high computational efficiency with vector-optimized programming languages. This direct and simultaneous calculation of the task space manipulability for large numbers of poses benefits many optimization problems in robotic applications. We show applications in global optimization of robot mounting poses, as well as redundancy resolution with global online optimization w.r.t. manipulability. View Full-Text
Keywords: manipulability; inverse kinematics function; kinematic optimization; redundant robot; 7-DOF; redundancy resolution manipulability; inverse kinematics function; kinematic optimization; redundant robot; 7-DOF; redundancy resolution
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MDPI and ACS Style

Huber, G.; Wollherr, D. Efficient Closed-Form Task Space Manipulability for a 7-DOF Serial Robot. Robotics 2019, 8, 98.

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