Graduated Fidelity Lattices for Motion Planning under Uncertainty
This work addresses motion planning under uncertainty for mobile robotics, presenting an incremental improvement in efficiency and safety.
The paper tackles motion planning under uncertainty by introducing graduated fidelity lattices that adapt to environmental complexity, improving planning efficiency while maintaining performance, with results validated across various scenarios and robot shapes.
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.