Abstract
We propose an online single-query sampling-based feedback motion re-planning algorithm using finite-time invariant sets, “funnels". We combine concepts from nonlinear systems analysis, sampling-based motion planning, and graph-search methods to create a single framework that enables feedback motion planning/replanning for general nonlinear dynamical systems in a dynamic workspace. We introduce a novel graph data structure to represent a network of volumetric funnels, enabling the use of quick graph-replanning techniques. The use of incremental search techniques and a pre-computed library of motion-primitives ensure that our method can be used for quick on-the-fly rewiring of controllable motion plans in response to changes in the environment. We validate our approach on a simulated 6DOF quadrotor platform operating in a maze, and random forest environment.
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Acknowledgements
The authors wish to thank the Robot Locomotion Group at MIT for providing an open-source software distribution for computing funnels. This work is supported by the grant N00421-21-1-0001 from Naval Air Systems Command (NAVAIR).
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Jaffar, M.K.M., Otte, M. (2023). PiP-X: Funnel-Based Online Feedback Motion Planning/Replanning in Dynamic Environments. In: LaValle, S.M., O’Kane, J.M., Otte, M., Sadigh, D., Tokekar, P. (eds) Algorithmic Foundations of Robotics XV. WAFR 2022. Springer Proceedings in Advanced Robotics, vol 25. Springer, Cham. https://doi.org/10.1007/978-3-031-21090-7_9
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