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Team SNU’s control strategies to enhancing robot’s capability: Lessons from the DARPA robotics challenge finals 2015

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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; Ahn, Joonwoo; 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
2018
Publisher
Springer Verlag
Citation
Springer Tracts in Advanced Robotics, Vol.121, pp.347-379
Abstract
This paper presents the technical approaches used and experimental results obtained by Team SNU at the DARPA Robotics Challenge (DRC) Finals 2015. Team SNU is one of the newly qualified teams, unlike the 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 have successfully finished the competition without falling and ranked 12th out of 23 teams. This paper is concluded with a number of lessons learned by analyzing the DRC Finals 2015.
ISSN
1610-7438
URI
https://hdl.handle.net/10371/206563
DOI
https://doi.org/10.1007/978-3-319-74666-1_10
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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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