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Team SNU's Control Strategies for Enhancing a Robot's Capability: Lessons from the 2015 DARPA Robotics Challenge Finals

Cited 15 time in Web of Science Cited 19 time in Scopus
Authors

Kim, Sanghyun; Kim, Mingon; Lee, Jimin; Hwang, Soonwook; Chae, Joonbo; Park, Beomyeong; Cho, Hyunbum; Sim, Jaehoon; Jung, Jaesug; Lee, Hosang; Shin, Seho; Kim, Minsung; Choi, Wonje; Lee, Yisoo; Park, Sumin; Oh, Jiyong; Lee, Yongjin; Lee, Sangkuk; Lee, Myunggi; Yi, Sangyup; Chang, Kyong-Sok K. C.; Kwak, Nojun; Park, Jaeheung

Issue Date
2017-03
Publisher
John Wiley & Sons Ltd.
Citation
Journal of Field Robotics, Vol.34 No.2, pp.359-380
Abstract
This paper presents the technical approaches used and experimental results obtained by Team SNU (Seoul National University) at the 2015 DARPA Robotics Challenge (DRC) Finals. Team SNU is one of the newly qualified teams, unlike 12 teams who previously participated in the December 2013 DRC Trials. The hardware platform THORMANG, which we used, has been developed by ROBOTIS. THORMANG is one of the smallest robots at the DRC Finals. Based on this platform, we focused on developing software architecture and controllers in order to perform complex tasks in disaster response situations and modifying hardware modules to maximize manipulability. Ensuring stability and modularization are two main keywords in the technical approaches of the architecture. We designed our interface and controllers to achieve a higher robustness level against disaster situations. Moreover, we concentrated on developing our software architecture by integrating a number of modules to eliminate software system complexity and programming errors. With these efforts on the hardware and software, we successfully finished the competition without falling, and we ranked 12th out of 23 teams. This paper is concluded with a number of lessons learned by analyzing the 2015 DRC Finals. (C) 2016 Wiley Periodicals, Inc.
ISSN
1556-4959
URI
https://hdl.handle.net/10371/206756
DOI
https://doi.org/10.1002/rob.21678
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  • Graduate School of Convergence Science & Technology
  • Department of Intelligence and Information
Research Area Feature Selection and Extraction, Object Detection, Object Recognition

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