We propose NH-TTC, a general method for fast, anticipatory collision avoidance for autonomous robots with arbitrary equations of motions. Our approach exploits implicit differentiation and subgradient descent to locally optimize the non-convex and non-smooth cost functions that arise from planning over the anticipated future positions of nearby obstacles. The result is a flexible framework capable of supporting high-quality, collision-free navigation with a wide variety of robot motion models in various challenging scenarios. We show results for different navigating tasks, with various numbers of agents (with and without reciprocity), on both physical differential drive robots, and simulated robots with different motion models and kinematic and dynamic constraints, including acceleration-controlled agents, differential-drive agents, and smooth car-like agents. The resulting paths are high quality and collision-free, while needing only a few milliseconds of computation as part of an integrated sense-plan-act navigation loop. For a video of further results and reference code, please see the corresponding webpage: http://motion.cs.umn.edu/r/NH-TTC/
|Original language||English (US)|
|Title of host publication||Robotics|
|Subtitle of host publication||Science and Systems XVI|
|Editors||Marc Toussaint, Antonio Bicchi, Tucker Hermans|
|Publisher||MIT Press Journals|
|State||Published - 2020|
|Event||16th Robotics: Science and Systems, RSS 2020 - Virtual, Online|
Duration: Jul 12 2020 → Jul 16 2020
|Name||Robotics: Science and Systems|
|Conference||16th Robotics: Science and Systems, RSS 2020|
|Period||7/12/20 → 7/16/20|
Bibliographical noteFunding Information:
This work was supported in part by the National Science Foundation under Grants IIS-1748541 and CNS-1544887.
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