Towards Autonomous Robot Operation: Path Map Generation of an Unknown Area by a New Trapezoidal Approximation Method Using a Self Guided Vehicle and Shortest Path Calculation by a Proposed SRS Algorithm

This paper deals with the road map generation of an unknown environment by an autonomous vehicle using a proposed trapezoidal approximation. Subsequently a novel shortest path calculation method named smallest road segment (SRS) detection method has been proposed. At first we have generated a blind map of an unknown environment in a computer. Image of the unknown environment is captured by the vehicle and sent to the computer using wireless transmitter module. The image is pre-processed and the detected road boundaries help to update the blind map with road positions. When the complete map having all possible road branches is generated then based on the source and destination point, the shortest path is calculated using the proposed SRS method. This shortest path is forwarded to the vehicle to reach at the destination through the best path available.

[1]  Narendra Ahuja,et al.  Multiscale image segmentation by integrated edge and region detection , 1997, IEEE Trans. Image Process..

[2]  Jean-Philippe Tarel,et al.  Combined dynamic tracking and recognition of curves with application to road detection , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[3]  Giuseppe F. Italiano,et al.  Incremental algorithms for minimal length paths , 1991, SODA '90.

[4]  David E. Boyce,et al.  Termination Detection for Parallel Shortest Path Algorithms , 1998, J. Parallel Distributed Comput..

[5]  Marilyn N. Abrams,et al.  An Intelligent World Model for Autonomous Off-Road Driving , 2001 .

[6]  Larry S. Davis,et al.  Road boundary detection in range imagery for an autonomous robot , 1988, IEEE J. Robotics Autom..

[7]  Elke A. Rundensteiner,et al.  Hierarchical optimization of optimal path finding for transportation applications , 1996, CIKM '96.

[8]  Kanungo Barada Mohanty,et al.  A Fast Edge Detection Algorithm for Road Boundary Extraction under Non-uniform Light Condition , 2007 .

[9]  Richard O. Duda,et al.  Use of the Hough transformation to detect lines and curves in pictures , 1972, CACM.

[10]  Xueyin Lin,et al.  Fast road classification and orientation estimation using omni-view images and neural networks , 1998, IEEE Trans. Image Process..

[11]  Christopher Rasmussen,et al.  Combining laser range, color, and texture cues for autonomous road following , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[12]  Jacob Scharcanski,et al.  Edge detection of color images using directional operators , 1997, IEEE Trans. Circuits Syst. Video Technol..

[13]  Sebastian Thrun,et al.  Adaptive Road Following using Self-Supervised Learning and Reverse Optical Flow , 2005, Robotics: Science and Systems.

[14]  Xiande Liu,et al.  A road boundary detection method for autonomous land vehicle , 2002, Proceedings of the 4th World Congress on Intelligent Control and Automation (Cat. No.02EX527).

[15]  Massimo Bertozzi,et al.  GOLD: a parallel real-time stereo vision system for generic obstacle and lane detection , 1998, IEEE Trans. Image Process..

[16]  Richard A. Haddad,et al.  Adaptive median filters: new algorithms and results , 1995, IEEE Trans. Image Process..

[17]  Michael Luetzeler,et al.  EMS-Vision: combining on- and off-road driving , 2001, SPIE Defense + Commercial Sensing.

[18]  Sebastian Thrun,et al.  A Self-Supervised Terrain Roughness Estimator for Off-Road Autonomous Driving , 2006, UAI.

[19]  Larry S. Davis,et al.  Road Boundary Detection for Autonomous Vehicle Navigation , 1985, Other Conferences.