Learned Critical Probabilistic Roadmaps for Robotic Motion Planning
Brian Ichter,Edward Schmerling,Tsang-Wei Edward Lee,Aleksandra Faust,Brian Ichter,Edward Schmerling,Tsang-Wei Edward Lee,Aleksandra Faust
Sampling-based motion planning techniques have emerged as an efficient algorithmic paradigm for solving complex motion planning problems. These approaches use a set of probing samples to construct an implicit graph representation of the robot's state space, allowing arbitrarily accurate representations as the number of samples increases to infinity. In practice, however, solution trajectories only...
