NH-TTC: A gradient-based framework for generalized anticipatory collision avoidance

Bobby Davis, Ioannis Karamouzas, Stephen J. Guy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


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 languageEnglish (US)
Title of host publicationRobotics
Subtitle of host publicationScience and Systems XVI
EditorsMarc Toussaint, Antonio Bicchi, Tucker Hermans
PublisherMIT Press Journals
ISBN (Print)9780992374761
StatePublished - 2020
Event16th Robotics: Science and Systems, RSS 2020 - Virtual, Online
Duration: Jul 12 2020Jul 16 2020

Publication series

NameRobotics: Science and Systems
ISSN (Electronic)2330-765X


Conference16th Robotics: Science and Systems, RSS 2020
CityVirtual, Online

Bibliographical note

Funding Information:
This work was supported in part by the National Science Foundation under Grants IIS-1748541 and CNS-1544887.

Publisher Copyright:
© 2020, MIT Press Journals. All rights reserved.


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