Learning a Lattice Planner Control Set for Autonomous Vehicles

Iaco, Ryan De; Smith, Stephen L.; Czarnecki, Krzysztof

doi:10.48550/arxiv.1903.02044

Cited by 1 publication

(2 citation statements)

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“…Even though planning in lattice spaces has proven to be suitable for many applications, it requires the crafting of a set of motions such that the resulting lattice offers, at least, one suitable solution to the planning problem. Some works in the learning community have addressed this difficult and time-consuming task with data-driven techniques [14]. However, the resulting set of motions still represents a very limited range of the real dynamic capabilities of the robot.…”

Section: A Planning Under Kinodynamic Constraintsmentioning

confidence: 99%

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Online Mapping and Motion Planning Under Uncertainty for Safe Navigation in Unknown Environments

Pairet

Hernández

Carreras

et al. 2022

IEEE Trans. Automat. Sci. Eng.

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Safe autonomous navigation is an essential and challenging problem for robots operating in highly unstructured or completely unknown environments. Under these conditions, not only robotic systems must deal with limited localization information but also their maneuverability is constrained by their dynamics and often suffers from uncertainty. In order to cope with these constraints, this article proposes an uncertainty-based framework for mapping and planning feasible motions online with probabilistic safety guarantees. The proposed approach deals with the motion, probabilistic safety, and online computation constraints by: 1) incrementally mapping the surroundings to build an uncertainty-aware representation of the environment and 2) iteratively (re)planning trajectories to goal that is kinodynamically feasible and probabilistically safe through a multilayered sampling-based planner in the belief space. In-depth empirical analyses illustrate some important properties of this approach, namely: 1) the multilayered planning strategy enables rapid exploration of the high-dimensional belief space while preserving asymptotic optimality and completeness guarantees and 2) the proposed routine for probabilistic collision check-

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Section: A Planning Under Kinodynamic Constraintsmentioning

confidence: 99%

“…traversed by the beam l occ , if v is hit by the beam l ocl , if v is between the hit and sensor range (14) where l free and l occ are constants determined according to the sensor model, and l ocl penalizes occluded zones according to the decaying function…”

Section: B Local Submap As Occupancy Grid Mapmentioning

confidence: 99%