Appendix for the Motion Primitives-based Path Planning for Fast and Agile Exploration Method

TL;DR

This paper enhances a motion-primitives path planning method for aerial robots by adding a global planning layer, enabling efficient large-scale exploration, safe return, and demonstrated through simulations and experiments.

Contribution

It introduces a global planning layer to an existing motion-primitives exploration method, improving large-scale exploration and safety features.

Findings

Successful integration of global planning for large environments

Demonstrated improved exploration efficiency in simulations

Validated safety and return-to-home capabilities through experiments

Abstract

This manuscript presents enhancements on our motion-primitives exploration path planning method for agile exploration using aerial robots. The method now further integrates a global planning layer to facilitate reliable large-scale exploration. The implemented bifurcation between local and global planning allows for efficient exploration combined with the ability to plan within very large environments, while also ensuring safe and timely return-to-home. A new set of simulation studies and experimental results are presented to demonstrate the new improvements and enhancements. The method is available open source as a Robot Operating System (ROS) package.