Receding Horizon "Next-Best-View" Planner for 3D Exploration

TLDR

The paper introduces a novel path‑planning algorithm for autonomous exploration of unknown space with aerial robots. The algorithm uses a receding‑horizon next‑best‑view planner that builds an online random tree, selects the branch covering the most unmapped area, and executes only its first edge at each step, enabling real‑time operation on resource‑constrained robots. Simulation and real‑world rotorcraft experiments demonstrate high performance, and complexity analysis shows the planner scales well to large, complex environments.

Abstract

This paper presents a novel path planning algorithm for the autonomous exploration of unknown space using aerial robotic platforms. The proposed planner employs a receding horizon “next-best-view” scheme: In an online computed random tree it finds the best branch, the quality of which is determined by the amount of unmapped space that can be explored. Only the first edge of this branch is executed at every planning step, while repetition of this procedure leads to complete exploration results. The proposed planner is capable of running online, onboard a robot with limited resources. Its high performance is evaluated in detailed simulation studies as well as in a challenging real world experiment using a rotorcraft micro aerial vehicle. Analysis on the computational complexity of the algorithm is provided and its good scaling properties enable the handling of large scale and complex problem setups.