Grid-based angle-constrained path planning

Square grids are commonly used in robotics and game development to model an agent's environment, and well known in Artificial Intelligence heuristic search algorithms (A*, JPS, Theta* etc.) are utilized for grid path planning. A lot of research in this area has been focused so far on finding the shortest paths while in many applications producing smooth paths is preferable. In our work, we study the problem of generating smooth grid paths and concentrate on angle constrained path planning. We put angle constrained path planning problem formally and present a new algorithm of solving it - LIAN. We examine LIAN both theoretically and empirically. On the theoretical side, we prove that LIAN is sound and complete (under well-defined restrictions). On the experimental side, we show that LIAN significantly outperforms competitors in ability to find solutions under tough resource constraints and in computational efficiency.