Vision-based Safe Local Motion on a Humanoid Robot

Humanoid soccer robots are increasingly becom- ing more autonomous as sophisticated approaches are being developed for challenges in vision, motion and team coordi- nation. The stated goal of the RoboCup initiative is to beat the human soccer champion team by 2050 (1). In order to achieve this goal, it is essential to enable the robot to fully utilize the information extracted from the available sensors. In the standard platform league of RoboCup (2), one major challenge is the ability to detect and avoid the mobile obstacles i.e. the other robots on the field. This paper presents an image gradient-based scheme to efficiently and reliably characterize the obstacles in the environment. In addition, information extracted from color images and range sensors is incorporated to build a robust obstacle model. Furthermore, a potential field- based method is used to navigate safely in the presence of obstacles. All algorithms are implemented and tested on the Aldebaran Nao (3) robot platform.

[1]  Hiroaki Kitano,et al.  RoboCup: The Robot World Cup Initiative , 1997, AGENTS '97.

[2]  Todd Hester,et al.  UT Austin Villa 2008: Standing On Two Legs , 2008 .

[3]  James J. Little,et al.  Global localization using distinctive visual features , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[4]  Manuela M. Veloso,et al.  Teaching multi-robot coordination using demonstration of communication and state sharing , 2008, AAMAS.

[5]  Sebastian Thrun,et al.  Probabilistic robotics , 2002, CACM.

[6]  Oussama Khatib,et al.  Real-Time Obstacle Avoidance for Manipulators and Mobile Robots , 1985, Autonomous Robot Vehicles.

[7]  Peter Stone,et al.  Practical Vision-Based Monte Carlo Localization on a Legged Robot , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[8]  Vincent Lepetit,et al.  Fast Keypoint Recognition in Ten Lines of Code , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Jerry E. Pratt,et al.  Learning Capture Points for humanoid push recovery , 2007, 2007 7th IEEE-RAS International Conference on Humanoid Robots.

[10]  Hitoshi Matsubara,et al.  RoboCup 2008: Robot Soccer World Cup XII [papers from the 12th annual RoboCup International Symposium, Suzhou, China, July 15-18, 2008] , 2009, Robot Soccer World Cup.

[11]  Mohan Sridharan,et al.  Autonomous information fusion for robust obstacle localization on a humanoid robot , 2009, 2009 6th Latin American Robotics Symposium (LARS 2009).

[12]  Luc Van Gool,et al.  Edinburgh Research Explorer Simultaneous Object Recognition and Segmentation by Image Exploration , 2022 .

[13]  Michael A. Goodrich,et al.  Human-Robot Interaction: A Survey , 2008, Found. Trends Hum. Comput. Interact..

[14]  Thomas Röfer,et al.  German Team 2007 : the German National RoboCup Team , 2004 .

[15]  Sean Luke,et al.  Cooperative Multi-Agent Learning: The State of the Art , 2005, Autonomous Agents and Multi-Agent Systems.

[16]  Andrew Zisserman,et al.  Multi-view Matching for Unordered Image Sets, or "How Do I Organize My Holiday Snaps?" , 2002, ECCV.

[17]  Sebastian Thrun,et al.  Stanley: The robot that won the DARPA Grand Challenge , 2006, J. Field Robotics.

[18]  Masahiro Fujita,et al.  Real-Time Path Planning for Humanoid Robot Navigation , 2005, IJCAI.

[19]  Peter Stone,et al.  Improving particle filter performance using SSE instructions , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[20]  Cordelia Schmid,et al.  Scale & Affine Invariant Interest Point Detectors , 2004, International Journal of Computer Vision.

[21]  Jerry Pratt,et al.  Design of a bipedal walking robot , 2008, SPIE Defense + Commercial Sensing.

[22]  Jiri Matas,et al.  Robust wide-baseline stereo from maximally stable extremal regions , 2004, Image Vis. Comput..

[23]  Joelle Pineau,et al.  Towards robotic assistants in nursing homes: Challenges and results , 2003, Robotics Auton. Syst..

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

[25]  Benjamin Kuipers,et al.  Detecting obstacles and drop-offs using stereo and motion cues for safe local motion , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.