Traversable Frontiers Based Autonomous Exploration Strategy for Deploying MAVs in Subterranean Environments

Abstract

Ahstract- Exploration and mapping of unknown environments is a fundamental task in applications for autonomous robots. In this article, we present an exploration-planning strategy for deploying autonomous MAVs in completely unknown areas. In exploration, the robot computes the next-best safe look-ahead poses such that by navigating to the future pose, the robot will acquire more information about the environment. The proposed strategy uses a novel frontier selection method that also contributes to the safe navigation of autonomous robots in obstructed areas such as Subterranean caves and mines. In order to compute safe look-ahead poses for the robot, the framework associates costs on a traversable frontier selection with minimal violation heading change from a most unknown direction. The proposed exploration framework is also adaptive to computational resources available on board the robot which means the trade-off between the speed of exploration and the quality of the map can be made. Such capability allows the proposed framework to be deployed in subterranean exploration, mapping as well as in fast search and rescue scenarios irrespective of the type of robot used. The performance of the proposed framework is evaluated in detailed simulation studies with comparisons made against state-of-the-art high-level exploration-planning framework as it will be presented in this article.