Late Line-of-Sight Check and Prioritized Trees for Path Planning

Abstract

How to generate a safe and high-quality path for a moving robot is a classical issue. The relative solution is also suitable for computer games and animation. To make the problem simpler, it is common to discretize the continuous planning space into grids, with blocked cells to represent obstacles. However, grid-based path-finding algorithms usually cannot find the truly shortest path because the extending paths are constrained to grid edges. Meanwhile, Line-of-Sight Check (LoS-Check) is an efficient operation to find the shorter any-angle path by relaxing the constraint of grid edges, which has been successfully used in Theta*. Through reducing the number of LoS-Check operations in Theta*, a variant version called LazyTheta* speeds up the planning especially in the 3D environment. We propose Late LoS-Check A* (LLA*) to further reduce the LoS-Check amount. It uses the structure of the prioritized trees to partially update the gvalues of different successors that share the same parent (i.e., the parent of the current vertex waiting to be extended). The sufficient experiments on various benchmark maps show that LLA* costs less execution time than Lazy Theta* while generating shorter paths. If we just delay the LoS-Check, the path planned by LLA* will hardly be shorter than that of Lazy Theta*. Therefore, the key of LLA* is the discriminatory strategy, and we empirically explain the reason why both the path length and execution time of LLA* are shorter than those of Lazy Theta*.

Keywords

• path planning
• line-of-sight
• lazy evaluation
• robotic planning
• path finding