Efficient Hierarchical Any-Angle Path Planning on Multi-Resolution 3D Grids

TL;DR

This paper introduces an efficient hierarchical any-angle path planning method that leverages multi-resolution 3D grids to improve scalability and solution quality in large environments, outperforming existing approaches.

Contribution

The paper presents a novel hierarchical approach that exploits multi-resolution maps for optimal and complete any-angle path planning, addressing scalability issues in large-scale environments.

Findings

Outperforms sampling-based methods in solution quality and speed

Demonstrates effectiveness on real and synthetic environments

Provides an open-source framework for the community

Abstract

Hierarchical, multi-resolution volumetric mapping approaches are widely used to represent large and complex environments as they can efficiently capture their occupancy and connectivity information. Yet widely used path planning methods such as sampling and trajectory optimization do not exploit this explicit connectivity information, and search-based methods such as A* suffer from scalability issues in large-scale high-resolution maps. In many applications, Euclidean shortest paths form the underpinning of the navigation system. For such applications, any-angle planning methods, which find optimal paths by connecting corners of obstacles with straight-line segments, provide a simple and efficient solution. In this paper, we present a method that has the optimality and completeness properties of any-angle planners while overcoming computational tractability issues common to search-based methods by exploiting multi-resolution representations. Extensive experiments on real and synthetic environments demonstrate the proposed approach's solution quality and speed, outperforming even sampling-based methods. The framework is open-sourced to allow the robotics and planning community to build on our research.