Path Planner for Autonomous Exploration of Underground Mines by Aerial Vehicles

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.

Sensors and System for Vehicle Navigation

“…Rubio-Sierra, C. et al [16] presented a path planning solution that enables the autonomous exploration of underground mines using aerial robots (typically multicopters). The presented path planner was defined as a simple and highly computationally efficient algorithm that relied only on a laser-based sensor with simultaneous localization and mapping capabilities.…”

Deformation detection of mine tunnel based on VSLAM 3D dense point cloud slice

“…SLAM technology has been developed for more than 30 years, from 2D mapping and positioning navigation to 3D scene reconstruction, and it has been widely used in mobile robots and UAV and has gradually entered the field of mine applications in recent years. Rubio-Sierra et al 10 developed a lidar SLAM technology for aerial robots to explore mines independently. Ren et al 11 applied the improved filtering algorithm to the SLAM simulation experiment, which realized fuzzy adjustments to the observed noise and built a 2D tunnel map.…”

Autonomous Vehicle Path Planning Based on Driver Characteristics Identification and Improved Artificial Potential Field

“…As a research hotspot in autonomous driving technology, there are many algorithms in path planning, including the A* algorithm [13,14], Rapidly-exploring Random Tree (RRT) [15,16], Artificial Potential Field (APF) [17][18][19] and some other path planning methods for collision avoidance problems [20][21][22][23], et al…”

“…On this basis, a new navigation approach was developed, achieving an outstanding performance in unknown dense environments. In [23], a path planner solution that makes it possible to autonomously explore underground mines with aerial robots (typically multicopters) is presented. The designed path planner is defined as a simple and highly computationally efficient algorithm which, by 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.…”