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Path Planning for Tentacle Robots Using Soft Constraints

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Agents and Artificial Intelligence (ICAART 2013)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 449))

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Abstract

Tentacle robots – robots with many degrees of freedom with one fixed end – offer advantages over traditional robots in many scenarios due to their enhanced flexibility and reachability. Planning practical paths for these devices is challenging due to their high number of degrees of freedom (DOFs). Sampling-based path planners are a common approach to the high DOF planning problem associated with tentacle robots but the solutions found using such planners are often not practical in that they do not take into account soft application-specific constraints. This paper describes a general sample adjustment method for tentacle robots which adjusts the nodes and edges generated by the sampling-based planners within their local neighborhood to satisfy soft constraints associated with the problem. Experiments with real and simulated tentacle robots demonstrate that our approach is an effective enhancement to the basic probabilistic planner to find practical paths.

This is an extended version of “Planning Practical Paths for Tentacle Robots” which appeared in the 5th International Conference on Agents and Artificial Intelligence (ICAART), Barcelona, Spain, 2013.

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Acknowledgments

The financial support of NSERC Canada and NCFRN are gratefully acknowledged. The authors would like to thank Robert Codd-Downey and Junquan Xu for their help with the robot infrustructure.

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Authors and Affiliations

  1. Department of Electrical Engineering and Computer Science, York University, Keele Street, Toronto, 4700, Canada

    Jing Yang, Patrick Dymond & Michael Jenkin

Authors
  1. Jing Yang
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  2. Patrick Dymond
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  3. Michael Jenkin
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Corresponding author

Correspondence to Jing Yang .

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Editors and Affiliations

  1. INSTICC, Setúbal, Portugal

    Joaquim Filipe

  2. Instituto de Telecomunicações, Lisbon, Portugal

    Ana Fred

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Yang, J., Dymond, P., Jenkin, M. (2014). Path Planning for Tentacle Robots Using Soft Constraints. In: Filipe, J., Fred, A. (eds) Agents and Artificial Intelligence. ICAART 2013. Communications in Computer and Information Science, vol 449. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44440-5_6

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  • DOI: https://doi.org/10.1007/978-3-662-44440-5_6

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-44439-9

  • Online ISBN: 978-3-662-44440-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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