2D Lidar-Based SLAM and Path Planning for Indoor Rescue Using Mobile Robots

  • Xuexi Zhang
    School of Automation, Guangdong University of Technology, and Guangdong Key Laboratory of IoT Information Technology, Guangzhou 510006, China
  • Jiajun Lai
    School of Automation, Guangdong University of Technology, and Guangdong Key Laboratory of IoT Information Technology, Guangzhou 510006, China
  • Dongliang Xu
    School of Automation, Guangdong University of Technology, and Guangdong Key Laboratory of IoT Information Technology, Guangzhou 510006, China
  • Huaijun Li
    School of Automobile and Engineering Machinery, Guangdong Communication Polytechnic, No. 789, Tianyuan Road, Tianhe District, Guangzhou 510630, China
  • Minyue Fu
    School of Electrical Engineering and Computer Science, The University of Newcastle, University Drive, Callaghan, 2308 NSW, Australia

説明

<jats:p>As the basic system of the rescue robot, the SLAM system largely determines whether the rescue robot can complete the rescue mission. Although the current 2D Lidar-based SLAM algorithm, including its application in indoor rescue environment, has achieved much success, the evaluation of SLAM algorithms combined with path planning for indoor rescue has rarely been studied. This paper studies mapping and path planning for mobile robots in an indoor rescue environment. Combined with path planning algorithm, this paper analyzes the applicability of three SLAM algorithms (GMapping algorithm, Hector-SLAM algorithm, and Cartographer algorithm) in indoor rescue environment. Real-time path planning is studied to test the mapping results. To balance path optimality and obstacle avoidance, <jats:inline-formula> <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msup> <mrow> <mi>A</mi> </mrow> <mrow> <mi>∗</mi> </mrow> </msup> </math> </jats:inline-formula> algorithm is used for global path planning, and DWA algorithm is adopted for local path planning. Experimental results validate the SLAM and path planning algorithms in simulated, emulated, and competition rescue environments, respectively. Finally, the results of this paper may facilitate researchers quickly and clearly selecting appropriate algorithms to build SLAM systems according to their own demands.</jats:p>

収録刊行物

被引用文献 (1)*注記

もっと見る

詳細情報 詳細情報について

問題の指摘

ページトップへ