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A General Region-Based Framework for Collaborative Planning

Part of the Springer Proceedings in Advanced Robotics book series (SPAR,volume 3)

Abstract

Sampling-based planning is a common method for solving motion planning problems. However, this paradigm falters in difficult scenarios, such as narrow passages. In contrast, humans can frequently identify these challenges and can sometimes propose an approximate solution. A recent method called Region Steering takes advantage of this intuition by allowing a user to define regions in the workspace to weight the search space for probabilistic roadmap planners. In this work, we extend Region Steering into a generalized Region-Based framework that is suitable for any sampling-based planning approach. We explore three variants of our framework for graph-based, tree-based, and hybrid planning methods. We evaluate these variants in simulations as a proof of concept. Our results demonstrate the benefits of our framework in reducing overall planning time.

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Acknowledgements

We would like to thank Brennen Taylor, a student at Texas A&M University, Ariana Ramirez, a student at Jimmy Carter Early College high school in La Joya, TX, USA, and Jonathon Colbert, a student at A&M Consolidated high school in College Station, TX, USA, for their participation in a summer research study on this topic in 2014.

This research supported in part by NSF awards CNS-0551685, CCF-0833199, CCF-1423111, CCF-0830753, IIS-0916053, IIS-0917266, EFRI-1240483, RI-1217991, by NIH NCI R25 CA090301-11.

Jory Denny contributed to this research as a Ph.D. student at Texas A&M University. During this time, he was supported in part by an NSF Graduate Research Fellowship.

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Denny, J., Sandström, R., Amato, N.M. (2018). A General Region-Based Framework for Collaborative Planning. In: Bicchi, A., Burgard, W. (eds) Robotics Research. Springer Proceedings in Advanced Robotics, vol 3. Springer, Cham. https://doi.org/10.1007/978-3-319-60916-4_32

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  • DOI: https://doi.org/10.1007/978-3-319-60916-4_32

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