Motion Primitives-based Path Planning for Fast and Agile Exploration using Aerial Robots

Mihir Dharmadhikari, Tung Dang, Lukas Solanka, Johannes Loje, Huan Nguyen, Nikhil Khedekar, Kostas Alexis

Abstract: This paper presents a novel path planning strategy for fast and agile exploration using aerial robots. Tailored to the combined need for large-scale exploration of challenging and confined environments, despite the limited endurance of micro aerial vehicles, the proposed planner employs motion primitives to identify admissible paths that search the configuration space, while exploiting the dynamic flight properties of small aerial robots. Utilizing a computationally efficient volumetric representation of the environment, the planner provides fast collision-free and future-safe paths that maximize the expected exploration gain and ensure continuous fast navigation through the unknown environment. The new method is field-verified in a set of deployments relating to subterranean exploration and specifically, in both modern and abandoned underground mines in Northern Nevada utilizing a 0.55m-wide collision-tolerant flying robot exploring with a speed of up to 2m/s and navigating sections with width as small as 0.8m.