Grid-based angle-constrained path planning
  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.
