Grid-based angle-constrained path planning

TL;DR

This paper introduces LIAN, a new algorithm for generating smooth, angle-constrained paths on grid maps, demonstrating its theoretical soundness, completeness, and superior performance in urban outdoor navigation scenarios.

Contribution

The paper presents LIAN, a novel algorithm specifically designed for angle-constrained path planning on grids, with theoretical analysis and empirical validation showing its advantages.

Findings

LIAN is sound and complete under certain conditions.

LIAN outperforms existing algorithms in urban outdoor navigation tasks.

The algorithm effectively generates smooth paths with angle constraints.

Abstract

Square grids are commonly used in robotics and game development as spatial models and well known in AI community heuristic search algorithms (such as A*, JPS, Theta* etc.) are widely used for path planning on grids. A lot of research is concentrated on finding the shortest (in geometrical sense) paths while in many applications finding smooth paths (rather than the shortest ones but containing sharp turns) is preferable. In this paper we study the problem of generating smooth paths and concentrate on angle constrained path planning. We put angle-constrained path planning problem formally and present a new algorithm tailored to solve it - LIAN. We examine LIAN both theoretically and empirically. We show that it is sound and complete (under some restrictions). We also show that LIAN outperforms the analogues when solving numerous path planning tasks within urban outdoor navigation scenarios.