DTG : Diffusion-based Trajectory Generation for Mapless Global Navigation
CoRR(2024)
摘要
We present a novel end-to-end diffusion-based trajectory generation method,
DTG, for mapless global navigation in challenging outdoor scenarios with
occlusions and unstructured off-road features like grass, buildings, bushes,
etc. Given a distant goal, our approach computes a trajectory that satisfies
the following goals: (1) minimize the travel distance to the goal; (2) maximize
the traversability by choosing paths that do not lie in undesirable areas.
Specifically, we present a novel Conditional RNN(CRNN) for diffusion models to
efficiently generate trajectories. Furthermore, we propose an adaptive training
method that ensures that the diffusion model generates more traversable
trajectories. We evaluate our methods in various outdoor scenes and compare the
performance with other global navigation algorithms on a Husky robot. In
practice, we observe at least a 15
around a 7
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