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

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.

[1]  Bernhard Schölkopf,et al.  Learning with kernels , 2001 .

[2]  Alexander J. Smola,et al.  Learning with kernels , 1998 .

[3]  Surya P. N. Singh,et al.  V-REP: A versatile and scalable robot simulation framework , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[4]  S. Buss Introduction to Inverse Kinematics with Jacobian Transpose , Pseudoinverse and Damped Least Squares methods , 2004 .

[5]  Daniel D. Lee,et al.  Team THOR's adaptive autonomy for disaster response humanoids , 2015, 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids).

[6]  David E. Orin,et al.  Constrained resolved acceleration control for humanoids , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[7]  Twan Koolen,et al.  Team IHMC's Lessons Learned from the DARPA Robotics Challenge Trials , 2015, J. Field Robotics.

[8]  Philippe Lutz,et al.  Force tracking impedance control with unknown environment at the microscale , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[9]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[10]  Siddhartha S. Srinivasa,et al.  CHIMP, the CMU Highly Intelligent Mobile Platform , 2015, J. Field Robotics.

[11]  R.R. Murphy,et al.  Human-robot interaction in USAR technical search: two heads are better than one , 2004, RO-MAN 2004. 13th IEEE International Workshop on Robot and Human Interactive Communication (IEEE Catalog No.04TH8759).

[12]  N. Oda,et al.  An approach of motion compensation for biped walking robots with structural deformation , 2008, 2008 10th IEEE International Workshop on Advanced Motion Control.

[13]  Soonwook Hwang,et al.  Approach of Team SNU to the DARPA Robotics Challenge finals , 2015, 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids).

[14]  Jun-Ho Oh,et al.  Walking Control Algorithm of Biped Humanoid Robot on Uneven and Inclined Floor , 2007, J. Intell. Robotic Syst..

[15]  Satoshi Kagami,et al.  Simultaneous planning of CoM and ZMP based on the preview control method for online walking control , 2011, 2011 11th IEEE-RAS International Conference on Humanoid Robots.

[16]  Anne-Marie Kermarrec,et al.  The many faces of publish/subscribe , 2003, CSUR.

[17]  Jonathon W. Sensinger,et al.  Cycloid vs. harmonic drives for use in high ratio, single stage robotic transmissions , 2012, 2012 IEEE International Conference on Robotics and Automation.

[18]  Kazuhito Yokoi,et al.  Biped walking pattern generation by using preview control of zero-moment point , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[19]  Henrik Hautop Lund,et al.  Design of the ATRON lattice-based self-reconfigurable robot , 2006, Auton. Robots.

[20]  Scott Kuindersma,et al.  An Architecture for Online Affordance‐based Perception and Whole‐body Planning , 2015, J. Field Robotics.

[21]  Michael Thomas Rouleau Design and Evaluation of an Underactuated Robotic Gripper for Manipulation Associated with Disaster Response , 2015 .

[22]  Dennis Hong,et al.  Design of an Underactuated Robotic End-Effector With a Focus on Power Tool Manipulation , 2014 .

[23]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[24]  Jaeheung Park,et al.  Wall cutting strategy for circular hole using humanoid robot , 2015, 2015 12th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI).

[25]  Gabriela Csurka,et al.  Visual categorization with bags of keypoints , 2002, eccv 2004.

[26]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[27]  Rodolfo Faglia,et al.  Harmonic drive transmissions: the effects of their elasticity, clearance and irregularity on the dynamic behaviour of an actual SCARA robot , 1992, Robotica.

[28]  Gregory S. Chirikjian,et al.  Modular Self-Reconfigurable Robot Systems [Grand Challenges of Robotics] , 2007, IEEE Robotics & Automation Magazine.

[29]  Ronny Hartanto,et al.  Reliable, cloud-based communication for multi-robot systems , 2014, 2014 IEEE International Conference on Technologies for Practical Robot Applications (TePRA).

[30]  Shuuji Kajita,et al.  Humanoid robot HRP-2Kai — Improvement of HRP-2 towards disaster response tasks , 2015, 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids).

[31]  Erico Guizzo,et al.  The hard lessons of DARPA's robotics challenge [News] , 2015 .

[32]  Behzad Dariush,et al.  Constrained closed loop inverse kinematics , 2010, 2010 IEEE International Conference on Robotics and Automation.

[33]  Naoki Oda,et al.  Experimental evaluation of vision-based ZMP detection for biped walking robot , 2013, 2013 IEEE International Symposium on Industrial Electronics.

[34]  Mark Yim,et al.  Team THOR's Entry in the DARPA Robotics Challenge Trials 2013 , 2015, J. Field Robotics.