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Found 5 papers, 3 papers with code
PIETRA: Physics-Informed Evidential Learning for Traversing Out-of-Distribution Terrain
Self-supervised learning is a powerful approach for developing traversability models for off-road navigation, but these models often struggle with inputs unseen during training.
CGD: Constraint-Guided Diffusion Policies for UAV Trajectory Planning
Although the resulting NN policies are effective at quickly generating trajectories similar to those from the expert, (1) their output does not explicitly account for dynamic feasibility, and (2) the policies do not accommodate changes in the constraints different from those used during training.
EVORA: Deep Evidential Traversability Learning for Risk-Aware Off-Road Autonomy
For uncertainty quantification, we efficiently model both aleatoric and epistemic uncertainty by learning discrete traction distributions and probability densities of the traction predictor's latent features.
Risk-Aware Off-Road Navigation via a Learned Speed Distribution Map
Motion planning in off-road environments requires reasoning about both the geometry and semantics of the scene (e. g., a robot may be able to drive through soft bushes but not a fallen log).
A Distributed Pipeline for Scalable, Deconflicted Formation Flying
While solutions using onboard localization address the dependency on external infrastructure, the associated coordination strategies typically lack collision avoidance and scalability.
Robotics Multiagent Systems
