NH-TTC: A gradient-based framework for generalized anticipatory collision avoidance
This addresses collision avoidance for autonomous robots with diverse motion models, representing a novel method for a known bottleneck in robotics.
The authors tackled the problem of anticipatory collision avoidance for autonomous robots with arbitrary motion models by proposing NH-TTC, a gradient-based framework using implicit differentiation and subgradient descent. The method achieved high-quality, collision-free navigation in various scenarios with computation times of only a few milliseconds.
We propose NH-TTC, a general method for fast, anticipatory collision avoidance for autonomous robots having arbitrary equations of motions. Our proposed 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 our method controlling 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.