Rapidly-Exploring Random Graph Next-Best View Exploration for Ground Vehicles

TL;DR

This paper presents a novel Rapidly-exploring Random Graph approach for autonomous ground vehicle exploration, significantly improving efficiency in large unknown environments, especially for rescue scenarios in complex indoor and underground spaces.

Contribution

It introduces a new graph-based exploration method with decoupled gain calculation and real-time Next-Best View evaluation, enhancing exploration speed and efficiency over existing algorithms.

Findings

Outperforms state-of-the-art exploration algorithms in simulations.

Speeds up exploration by eliminating wait times for gain calculation.

Effectively navigates large indoor and underground environments.

Abstract

In this paper, a novel approach is introduced which utilizes a Rapidly-exploring Random Graph to improve sampling-based autonomous exploration of unknown environments with unmanned ground vehicles compared to the current state of the art. Its intended usage is in rescue scenarios in large indoor and underground environments with limited teleoperation ability. Local and global sampling are used to improve the exploration efficiency for large environments. Nodes are selected as the next exploration goal based on a gain-cost ratio derived from the assumed 3D map coverage at the particular node and the distance to it. The proposed approach features a continuously-built graph with a decoupled calculation of node gains using a computationally efficient ray tracing method. The Next-Best View is evaluated while the robot is pursuing a goal, which eliminates the need to wait for gain calculation after reaching the previous goal and significantly speeds up the exploration. Furthermore, a grid map is used to determine the traversability between the nodes in the graph while also providing a global plan for navigating towards selected goals. Simulations compare the proposed approach to state-of-the-art exploration algorithms and demonstrate its superior performance.