Fueling Glocal: Optimization-Based Path Planning for Indoor Uavs in an Autonomous Exploration Framework

Abstract

Abstract. Exploration is a fundamental problem in robotics that requires robots to navigate through unknown environments to autonomously gather information about their surroundings while executing collision-free paths. In this paper, we propose a method for producing smooth paths during the exploration process in indoor environments using UAVs to improve battery efficiency and enhance the quality of pose estimation. The developed framework is built by merging two approaches that represent the state of the art in the field of autonomous exploration with UAVs. The overall exploration logic is given by GLocal, a paper that introduces an hybrid, i.e. both sampling-based and frontier-based, framework that is able to cope with the issue of odometry drift when exploring indoor environments due to the absence of absolute localization, e.g. through GNSS. The second paper is FUEL, which introduces a frontier-based exploration methodology which computes the drone’s path as an optimized non-uniform B-Spline. The framework described in this paper borrows the optimized B-Spline trajectory generation from FUEL and implements it in GLocal. The presented system is evaluated in two different simulated environments, which show the pros and the cons of such method.