Real-time identification and predictive control of fast mobile robots using global vision sensing

This paper presents a predictive controller for intercepting mobile targets. A global vision system is used to identify fast moving objects and uses a color threshold technique to calculate their position and orientation. The inherent systemic noise in the raw sensor data, as well as vision quantization noise, is smoothed using Kalman filtering before being fed to the controller, and it is shown that this leads to superior accuracy of the controller. The predictive controller is based on the state transition-based control (STBC) technique. As a case study, STBC has been applied to a goalkeeper's behavior in robot soccer which includes interception and clearance of ball. Further evaluation of the controller has been done for shooting the ball toward a target position. The system is examined for both stationary and moving objects. It is shown that predictive filtering of rough sensor data is essential to increase the reliability and accuracy of detection, and thus interception, of fast moving objects.

[1]  Gourab Sen Gupta,et al.  Size/position identification in real-time image processing using run length encoding , 2002, IMTC/2002. Proceedings of the 19th IEEE Instrumentation and Measurement Technology Conference (IEEE Cat. No.00CH37276).

[2]  Hiroaki Kitano,et al.  RoboCup: A Challenge Problem for AI and Robotics , 1997, RoboCup.

[3]  Stergios I. Roumeliotis,et al.  Collective localization: a distributed Kalman filter approach to localization of groups of mobile robots , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[4]  北野 宏明,et al.  RoboCup-97 : robot soccer World Cup I , 1998 .

[5]  Meng,et al.  Intelligent learning technique based-on fuzzy logic for multi-robot path planning , 2001 .

[6]  Gourab Sen Gupta,et al.  State transition based supervisory control for a robot soccer system , 2002, Proceedings First IEEE International Workshop on Electronic Design, Test and Applications '2002.

[7]  Beno Benhabib,et al.  The robotic interception of moving objects in industrial settings: strategy development and experiment , 1998 .

[8]  Bernhard Nebel,et al.  Fast, Accurate, and Robust Self-Localization in the RoboCup Environment , 1999, RoboCup.

[9]  Manuela M. Veloso,et al.  Fast and inexpensive color image segmentation for interactive robots , 2000, Proceedings. 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2000) (Cat. No.00CH37113).

[10]  Jacky Baltes,et al.  Adaptive Path Planner for Highly Dynamic Environments , 2000, RoboCup.

[11]  Rangachar Kasturi,et al.  Machine vision , 1995 .

[12]  Jurjen Caarls,et al.  Fast and Accurate Robot Vision for Vision Based Motion , 2000, RoboCup.

[13]  Sven Behnke,et al.  Robust Real Time Color Tracking , 2000, RoboCup.

[14]  Wolfram Burgard,et al.  Tracking multiple moving objects with a mobile robot , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[15]  Hiroaki Kitano,et al.  RoboCup: A Challenge Problem for AI , 1997, AI Mag..

[16]  Vahab S. Mirrokni,et al.  A Fast Vision System for Middle Size Robots in RoboCup , 2001, RoboCup.

[17]  Matthew Walker,et al.  Evolving cooperative robotic behaviour using distributed genetic programming , 2002, 7th International Conference on Control, Automation, Robotics and Vision, 2002. ICARCV 2002..

[18]  Ben J. A. Kröse,et al.  Auxiliary particle filter robot localization from high-dimensional sensor observations , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[19]  Jacky Baltes,et al.  Practical Camera and Colour Calibration for Large Rooms , 1999, RoboCup.

[20]  Robot Soccer – Sensing, Planning, Strategy and Control, a distributed real time intelligent system approach , 1998 .

[21]  Hamid Haidarian Shahri,et al.  Towards Autonomous Decision Making in Multi-agent Environments Using Fuzzy Logic , 2003, CEEMAS.

[22]  Hong Hao,et al.  Robot path planning using genetic algorithms , 1995, 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century.

[23]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[24]  Alessandro Saffiotti,et al.  Using the Electric Field Approach in the RoboCup Domain , 2001, RoboCup.

[25]  Gourab Sen Gupta,et al.  Distributed Real-time Image Processing for a Dual Camera System , 2001 .

[26]  Pedro García,et al.  Fuzzy Range Sensor Filtering for Reactive Autonomous Robots , 2001 .

[27]  Oliver Obst,et al.  Qualitative Velocity and Ball Interception , 2002, KI.