Open AccessAutonomous Wall-building and Firefighting: Team NimbRo’s UGV Solution for MBZIRC 2020
Author(s) -
Christian Lenz,
Jan Quenzel,
Arul Selvam Periyasamy,
Jan Razlaw,
Andre Rochow,
Malte Splietker,
Michael Schreiber,
Max Schwarz,
Finn Süberkrüb,
Sven Behnke
Publication year - 2022
Publication title -
field robotics
Language(s) - English
Resource type - Journals
ISSN - 2771-3989
DOI - 10.55417/fr.2022003
Subject(s) - robot , firefighting , robotics , artificial intelligence , testbed , computer science , human–computer interaction , planner , simulation , benchmark (surveying) , real time computing , computer vision , systems engineering , embedded system , engineering , computer network , chemistry , organic chemistry , geodesy , geography
Autonomous robotic systems for various applications including transport, mobile manipulation, and disaster response are becoming more and more complex. Evaluating and analyzing such systems is challenging. Robotic competitions are designed to benchmark complete robotic systems on complex state-of-the-art tasks. Participants compete in defined scenarios under equal conditions. We present our UGV solution developed for the Mohamed Bin Zayed International Robotics Challenge 2020. Our hardware and software components to address the challenge tasks of wall-building and firefighting are integrated into a fully autonomous system. The robot consists of a wheeled, omnidirectional base, a 6 DoF manipulator arm equipped with a magnetic gripper, a highly efficient storage system to transport box-shaped objects, and a water-spraying system to extinguish fires. The robot perceives its environment using 3D LiDAR, as well as RGB and thermal camera-based perception modules and is capable of picking box-shaped objects and constructing a pre-defined wall structure. Its sensor modules also facilitate detecting and localizing heat sources to extinguish potential fires. A high-level planner coordinates and applies the robot’s skills to complete the Challenge tasks. We analyze and discuss our successful participation during the MBZIRC 2020 finals, present further experiments, and provide insights to our lessons learned.