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Goal Directed Synthesis of Serial Manipulators Based on Task Descriptions

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 581))

Abstract

Computing the optimal geometric structure of manipulators is one of the most intricate problems in contemporary robot kinematics. Robotic manipulators are designed and built to perform certain predetermined tasks. There is a very close relationship between the structure of the manipulator and its kinematic performance. It is therefore important to incorporate such task requirements during the design and synthesis of the robotic manipulators. Such task requirements and performance constraints can be specified in terms of the required end-effector positions, orientations and velocities along the task trajectory. In this work, we present a comprehensive method to develop the optimal geometric structure (DH parameters) of a non-redundant six degree of freedom serial manipulator from task descriptions. This methodology is devised to investigate possible manipulator configurations that can satisfy the task performance requirements under imposed joint constraints. Out of all the possible structures, the structures that can reach all the task points with the required orientations selected. Next, these candidate structures are then tested to see if they can attain end-effector velocities in arbitrary directions within the user defined joint constraints, so that they can deliver the best kinematic performance. Finally, the synthesized structures are tested to see if they perform the task under the operating constraints. In this work, we also present a novel approach for computing the inverse kinematics using Particle Swarm Optimization (PSO).

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Notes

  1. 1.

    [© 2002] Wolfram Research Inc.

  2. 2.

    [© 1993] Board of Trustees, University of Illinois.

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Patel, S., Sobh, T., Mahmood, A. (2015). Goal Directed Synthesis of Serial Manipulators Based on Task Descriptions. In: Azar, A., Vaidyanathan, S. (eds) Chaos Modeling and Control Systems Design. Studies in Computational Intelligence, vol 581. Springer, Cham. https://doi.org/10.1007/978-3-319-13132-0_13

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  • DOI: https://doi.org/10.1007/978-3-319-13132-0_13

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