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Adaptive Partial Shortcuts: Path Optimization for Industrial Robotics

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Abstract

The quality of a path generated from an automated motion planning algorithm is of considerable importance, particularly when used in a real world robotic application. In this work a new path optimization algorithm, called the Adaptive Partial Shortcut algorithm, is presented. This algorithm optimizes paths as a post process to motion planning, and is designed specifically for use on industrial manipulators. The algorithm optimizes a robot’s degrees of freedom independently allowing it to produce manipulator paths of particularly high quality. This new algorithm utilizes an adaptive method of selecting the degree of freedom to optimize at each iteration, giving it a high level of efficiency. Tests conducted show the effectiveness of the algorithm; over a range of different test paths, the adaptive algorithm was able to generate solutions with a 60 % reduction in collision checks compared to the original partial shortcut approach.

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Authors and Affiliations

  1. Singapore Institute of Manufacturing Technology, Singapore, Singapore

    Joseph Polden

  2. University of Wollongong, Wollongong, NSW, Australia

    Zengxi Pan, Nathan Larkin & Stephen van Duin

Authors
  1. Joseph Polden
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  2. Zengxi Pan
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  3. Nathan Larkin
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  4. Stephen van Duin
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Correspondence to Zengxi Pan.

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Polden, J., Pan, Z., Larkin, N. et al. Adaptive Partial Shortcuts: Path Optimization for Industrial Robotics. J Intell Robot Syst 86, 35–47 (2017). https://doi.org/10.1007/s10846-016-0437-x

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  • DOI: https://doi.org/10.1007/s10846-016-0437-x

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