Tunnel and Underground Space (터널과지하공간)

Korean Society for Rock Mechanics and Rock Engineering (한국암반공학회)
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Development of a ROS-Based Autonomous Driving Robot for Underground Mines and Its Waypoint Navigation Experiments

ROS 기반의 지하광산용 자율주행 로봇 개발과 경유지 주행 실험

  • Kim, Heonmoo (Department of Energy Resources Engineering, Pukyong National University) ;
  • Choi, Yosoon (Department of Energy Resources Engineering, Pukyong National University)
  • 김헌무 (부경대학교 에너지자원공학과) ;
  • 최요순 (부경대학교 에너지자원공학과)
  • Received : 2022.06.07
  • Accepted : 2022.06.27
  • Published : 2022.06.30
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Abstract

In this study, we developed a robot operating system (ROS)-based autonomous driving robot that estimates the robot's position in underground mines and drives and returns through multiple waypoints. Autonomous driving robots utilize SLAM (Simultaneous Localization And Mapping) technology to generate global maps of driving routes in advance. Thereafter, the shape of the wall measured through the LiDAR sensor and the global map are matched, and the data are fused through the AMCL (Adaptive Monte Carlo Localization) technique to correct the robot's position. In addition, it recognizes and avoids obstacles ahead through the LiDAR sensor. Using the developed autonomous driving robot, experiments were conducted on indoor experimental sites that simulated the underground mine site. As a result, it was confirmed that the autonomous driving robot sequentially drives through the multiple waypoints, avoids obstacles, and returns stably.

본 연구에서는 지하광산에서 로봇의 위치를 추정하고, 여러 경유지를 거쳐 주행한 후 원위치로 복귀하는 ROS (Robot Operating System) 기반의 자율주행 로봇을 개발하였다. 자율주행 로봇은 SLAM (Simultaneous Localization And Mapping) 기술을 활용하여 주행 경로에 대한 전역 지도를 사전에 생성한다. 이후, 라이다 센서를 통해 측정되는 벽면의 형태와 전역 지도를 매칭하고 AMCL (Adaptive Monte Carlo Localization) 기법을 통해 데이터들을 융합하여 로봇의 위치를 보정한다. 또한, 라이다 센서를 통해 전방 주행환경을 인지하고, 장애물을 회피한다. 개발된 자율주행 로봇을 활용하여 지하광산 현장을 모사한 실내 실험장을 대상으로 주행 실험을 수행하였다. 그 결과, 자율주행 로봇은 다중 지점의 경유지에 대해 순차적으로 주행하고 장애물을 회피하며 안정적으로 복귀하는 것을 확인할 수 있었다.

Keywords

Acknowledgement

본 연구는 2022년도 정부(과학기술정보통신부)의 재원으로 한국연구재단 중견연구지원사업(2021R1A2C1011216)의 지원을 받아 수행되었다. 이에 감사한다.

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