Grid-based angle-constrained path planning
This work addresses the need for smooth paths over shortest ones in robotics and game development, though it appears incremental as it builds on existing grid-based methods.
The paper tackles the problem of generating smooth paths with angle constraints in grid-based path planning, presenting the LIAN algorithm which is shown to be sound and complete and outperforms analogues in urban outdoor navigation tasks.
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.