Simulating stochastic pure-pursuit controller rollouts inside the KEASL state-lattice planner produces more conservative trajectories with fewer predicted collisions than baseline planning with expanded obstacle footprints.
IEEE transactions on automatic control 16, 529–552 (1971)
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Incorporating Stochastic Models of Controller Behavior into Kinodynamic Efficiently Adaptive State Lattices for Mobile Robot Motion Planning in Off-Road Environments
Simulating stochastic pure-pursuit controller rollouts inside the KEASL state-lattice planner produces more conservative trajectories with fewer predicted collisions than baseline planning with expanded obstacle footprints.