# Graduated Fidelity Lattices for Motion Planning under Uncertainty

## TL;DR

This paper introduces a new motion planning method for mobile robots that uses graduated fidelity state lattices to efficiently find safe, optimal paths under uncertainty by adapting the planning resolution to environmental complexity.

## Contribution

It proposes a novel graduated fidelity lattice approach with an adaptive heuristic for efficient, reliable motion planning under sensing and motion uncertainties.

## Key findings

- Improved planning efficiency with adaptive fidelity
- Reliable collision probability estimation considering robot shape
- Validated results across different scenarios and robot shapes

## Abstract

We present a novel approach for motion planning in mobile robotics under sensing and motion uncertainty based on state lattices with graduated fidelity. The probability of collision is reliably estimated considering the robot shape, and the fidelity adapts to the complexity of the environment, improving the planning efficiency while maintaining the performance. Safe and optimal paths are found with an informed search algorithm, for which a novel multi-resolution heuristic is presented. Results for different scenarios and robot shapes are given, showing the validity of the proposed methods.