Rapid Vector-based Any-angle Path Planning with Non-convex Obstacles

TL;DR

This paper introduces novel vector-based algorithms for rapid, optimal any-angle path planning in environments with non-convex obstacles, improving efficiency by delaying line-of-sight checks and using innovative methods like the 'best hull' and phantom points.

Contribution

The paper presents new search techniques and algorithms, R2 and R2+, that outperform existing vector-based path planning methods in complex non-convex environments.

Findings

R2 and R2+ outperform other vector-based algorithms in non-convex scenarios.

The 'best hull' method enables monotonic path cost estimates without immediate line-of-sight checks.

A multi-dimensional ray tracer enhances occupancy grid analysis.

Abstract

Vector-based algorithms are novel algorithms in optimal any-angle path planning that are motivated by bug algorithms, bypassing free space by directly conducting line-of-sight checks between two queried points, and searching along obstacle contours if a check collides with an obstacle. The algorithms outperform conventional free-space planners such as A* especially when the queried points are far apart. The thesis presents novel search methods to speed up vector-based algorithms in non-convex obstacles by delaying line-of-sight checks. The "best hull" is a notable method that allows for monotonically increasing path cost estimates even without verifying line-of-sight, utilizing "phantom points" placed on non-convex corners to mimic future turning points. Building upon the methods, the algorithms R2 and R2+ are formulated, which outperform other vector-based algorithms when the optimal path solution is expected to have few turning points. Other novel methods include a novel and versatile multi-dimensional ray tracer for occupancy grids, and a description of the three-dimensional angular sector for future works.