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Accomplishing high-level tasks with modular robots

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Abstract

The advantage of modular self-reconfigurable robot systems is their flexibility, but this advantage can only be realized if appropriate configurations (shapes) and behaviors (controlling programs) can be selected for a given task. In this paper, we present an integrated system for addressing high-level tasks with modular robots, and demonstrate that it is capable of accomplishing challenging, multi-part tasks in hardware experiments. The system consists of four tightly integrated components: (1) a high-level mission planner, (2) a large design library spanning a wide set of functionality, (3) a design and simulation tool for populating the library with new configurations and behaviors, and (4) modular robot hardware. This paper builds on earlier work by Jing et al. (in: Robotics: science and systems, 2016), extending the original system to include environmentally adaptive parametric behaviors, which integrate motion planners and feedback controllers with the system.

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Acknowledgements

This work was funded by NSF Grant Nos. CNS-1329620 and CNS-1329692.

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Authors and Affiliations

  1. Cornell University, Ithaca, USA

    Gangyuan Jing & Hadas Kress-Gazit

  2. University of Pennsylvania, Philadelphia, USA

    Tarik Tosun & Mark Yim

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  1. Gangyuan Jing
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  2. Tarik Tosun
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  3. Mark Yim
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  4. Hadas Kress-Gazit
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Correspondence to Tarik Tosun.

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This is one of several papers published in Autonomous Robots comprising the “Special Issue on Robotics Science and Systems”.

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Jing, G., Tosun, T., Yim, M. et al. Accomplishing high-level tasks with modular robots. Auton Robot 42, 1337–1354 (2018). https://doi.org/10.1007/s10514-018-9738-1

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