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Found 4 papers, 3 papers with code
Physics-informed Neural Motion Planning on Constraint Manifolds
Constrained Motion Planning (CMP) aims to find a collision-free path between the given start and goal configurations on the kinematic constraint manifolds.
Progressive Learning for Physics-informed Neural Motion Planning
Neural motion planners (NMPs) demonstrate fast computational speed in finding path solutions but require a huge amount of expert trajectories for learning, thus adding a significant training computational load.
NTFields: Neural Time Fields for Physics-Informed Robot Motion Planning
We evaluate our method in various cluttered 3D environments, including the Gibson dataset, and demonstrate its ability to solve motion planning problems for 4-DOF and 6-DOF robot manipulators where the traditional grid-based Eikonal planners often face the curse of dimensionality.
Robust & Asymptotically Locally Optimal UAV-Trajectory Generation Based on Spline Subdivision
Generating locally optimal UAV-trajectories is challenging due to the non-convex constraints of collision avoidance and actuation limits.
Robotics
