In the present day world, a lot of research has been going on in the field of unmanned vehicles, which includes unmanned ground vehicles (UGVs), unmanned aerial vehicles(UAVs) and unmanned water vehicles (UWVs). The UGV technology grows rapidly. Generally UGV is developed for military purpose. Now a days, many universities and research institute are researching in new UGV technology for commercial use such as transportation service. The UGV system generally consists of four parts such as vehicle control system, navigation system, and obstacle detecting system and traffic signal monitoring and detection system. In this project, we have designed and implemented a prototype of UGV using PIC 16F57 microcontroller on board the Board of Education (BOE) development board designed by Parallax Inc, USA. This UGV prototype was used to simulate acts performed by a real car on urban environment which include navigation, obstacle avoidance, collision avoidance at intersection of two roads, traffic signal detection and feed backing the current co-ordinates of the vehicle to the control room for tracking of the vehicle. For implementing the navigation part of the robot, a novel algorithm was adapted which we named as r-θ (r-theta) algorithm which is explained latter in this paper.

1. Seetharaman, Guna., Lakhotia, Arun., and Blasch, Erik Philip, “Unmanned Vehicles Come of Age: The DARPA Grand Challenge”, Computer, Volume 39 Issue 12, December 2006.
2. Dr. Peter Drewes, Brian Satterfield, and Heeten Choxi, "Sensor Technology and UGV Operations –Lessons Learned from the Urban Challenge".
3. DRDO Develops Honda CRV Based Unmanned Ground Vehicle Prototype, www.defenceforumindia.com/indian-army/15046-drdo-develops-honda-crv-based-unmanned-ground.html.
4. Anthony Stentz, “Optimal and Efficient Path Planning for Partially-Known Environments”, Proceedings IEEE International Conference on Robotics and Automation, May 1994.
5. Alex Nash , Craig Toveyand Sven Koenig, “Lazy Theta*: Any-Angle Path Planning and Path Length Analysis in 3D”, National Conference on Artificial Intelligence - AAAI , 2010.
6. Lumelsky, V. J., Stepanov, A. A., “Dynamic Path Planning for a Mobile Automaton with Limited Information on the Environment”, IEEE Transactions on Automatic Control, Vol.
7. Borenstein, J., 1997, "Experimental Results from Internal Odometry Error Correction With the OmniMate Mobile Platform", IEEE Transactions on Robotics and Automation, Vol. 14, No. 6, December 1998, 963-96.
8. Jin-Hyung Park, Chang-sung Jeong, “Real-time Signal Light Detection”, International Journal of Signal Processing, Image Processing and Pattern Recognition, Vol.2, No.2, June 2009.
9. Yun-Chung Chung et al, “A Vision-based Traffic Light Detection System at Intersections”, Journal of Taiwan Normal University: Mathematics, Science & Technology 47(1), 67-86, 2002.

digital map path planning r-θ (r-theta) algorithm UGV