R2: Heuristic Bug-Based Any-angle Path-Planning using Lazy Searches

TL;DR

R2 introduces a heuristic bug-based online path planner that efficiently finds optimal paths in 2D maps with non-convex obstacles, especially excelling in large sparse environments by reducing line-of-sight checks.

Contribution

It presents R2, a new online any-angle path planning algorithm using lazy searches and bug-based heuristics, outperforming traditional planners in certain map conditions without pre-processing.

Findings

R2 is faster than free-space planners on sparse maps.

R2 outperforms Anya on maps with few obstacle contours.

R2 requires no pre-processing and reduces expensive line-of-sight checks.

Abstract

R2 is a novel online any-angle path planner that uses heuristic bug-based or ray casting approaches to find optimal paths in 2D maps with non-convex, polygonal obstacles. R2 is competitive to traditional free-space planners, finding paths quickly if queries have direct line-of-sight. On large sparse maps with few obstacle contours, which are likely to occur in practice, R2 outperforms free-space planners, and can be much faster than state-of-the-art free-space expansion planner Anya. On maps with many contours, Anya performs faster than R2. R2 is built on RayScan, introducing lazy-searches and a source-pledge counter to find successors optimistically on contiguous contours. The novel approach bypasses most successors on jagged contours to reduce expensive line-of-sight checks, therefore requiring no pre-processing to be a competitive online any-angle planner.