ARC Nav -- A 3D Navigation Stack for Autonomous Robots

TL;DR

This paper introduces ARC Nav, a 3D navigation stack for autonomous robots that uses volumetric workspace representation and a novel sampling-based motion planning algorithm to enable navigation in complex terrains.

Contribution

It presents a volumetric navigation stack compatible with aerial and legged robots and a new bi-directional, strategy-switching sampling-based planning algorithm to improve pathfinding efficiency.

Findings

Supports navigation on uneven terrain

Reduces initial path planning time

Finds shorter paths more efficiently

Abstract

Popular navigation stacks implemented on top of open-source frameworks such as ROS(Robot Operating System) and ROS2 represent the robot workspace using a discretized 2D occupancy grid. This method, while requiring less computation, restricts the use of such navigation stacks to wheeled robots navigating on flat surfaces. In this paper, we present a navigation stack that uses a volumetric representation of the robot workspace, and hence can be extended to aerial and legged robots navigating through uneven terrain. Additionally, we present a new sampling-based motion planning algorithm which introduces a bi-directional approach to the Batch Informed Trees (BIT*) motion planning algorithm, whilst wrapping it with a strategy switching approach in order to reduce the initial time taken to find a path, in addition to the time taken to find the shortest path.