Abstract
This paper presents a path planner solution that makes it possible to autonomously explore underground mines with aerial robots (typically multicopters). In these environments the operations may be limited by many factors like the lack of external navigation signals, the narrow passages and the absence of radio communications. The designed path planner is defined as a simple and highly computationally efficient algorithm that, only relying on a laser imaging detection and ranging (LIDAR) sensor with Simultaneous localization and mapping (SLAM) capability, permits the exploration of a set of single-level mining tunnels. It performs dynamic planning based on exploration vectors, a novel variant of the open sector method with reinforced filtering. The algorithm incorporates global awareness and obstacle avoidance modules. The first one prevents the possibility of getting trapped in a loop, whereas the second one facilitates the navigation along narrow tunnels. The performance of the proposed solution has been tested in different study cases with a Hardware-in-the-loop (HIL) simulator developed for this purpose. In all situations the path planner logic performed as expected and the used routing was optimal. Furthermore, the path efficiency, measured in terms of traveled distance and used time, was high when compared with an ideal reference case. The result is a very fast, real-time, and static memory capable algorithm, which implemented on the proposed architecture presents a feasible solution for the autonomous exploration of underground mines.
Highlights
Aerial robots are an exceptional solution for outdoor exploring and mapping, but in an underground environment they present significant challenges that have delayed them from being widely used
Due to the lack of navigation signals and radio communications, the exploration is performed by equipping the vehicle with a laser imaging detection and ranging (LIDAR) sensor with integrated Simultaneous localization and mapping (SLAM) capabilities
A path planner has been developed considering a dynamic planning approach based on exploration vectors
Summary
Aerial robots are an exceptional solution for outdoor exploring and mapping, but in an underground environment they present significant challenges that have delayed them from being widely used. The research interest in this field is very high due to its large potential for saving costs, and even human lives, when it addresses the exploration of dangerous areas This interest is well illustrated by the Defense Advanced Research Projects Agency (DARPA) Subterranean. Knowledge of subterranean spaces has tremendous value across a number of applications [2], mines are of particular interest for automatic exploration (large number and horizontal size and inherent risks: poisonous gases, roof collapses, water, etc.). When it comes to developing the exploration robot, no single design could be applicable to every conceivable subterranean space, which is why a myriad of different ground vehicles have been
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