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We propose NH-TTC, a general method for fast, anticipatory collisionavoidance for autonomous robots having arbitrary equations of motions. Ourproposed approach exploits implicit differentiation and subgradient descent tolocally optimize the non-convex and non-smooth cost functions that arise fromplanning over the anticipated future positions of nearby obstacles. The resultis a flexible framework capable of supporting high-quality, collision-freenavigation with a wide variety of robot motion models in various challengingscenarios. We show results for different navigating tasks, with our methodcontrolling various numbers of agents (with and without reciprocity), on bothphysical differential drive robots, and simulated robots with different motionmodels and kinematic and dynamic constraints, including acceleration-controlledagents, differential-drive agents, and smooth car-like agents. The resultingpaths are high quality and collision-free, while needing only a fewmilliseconds of computation as part of an integrated sense-plan-act navigationloop.

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