Scientific progress is an ongoing process and humans always have new ideas for new inventions. Although, scientific process is cumulative, you must invent the tire before inventing the car. Currently, mankind has past that point and looking towards to new challenges. One of the new ideas is cars that drive themselves, in a more formal and general way, the ‘autonomous ground vehicles’. Autonomous vehicles in city traffic have been a dream for a long time. However, this great idea comes with its own problems. There are many parts of autonomous driving such as scene understanding, path planning. Many methods have been studied and developed to solve these problems. In this paper, a novel path planning algorithm and results of extensive experimentation on it are presented. The experimentation were done in a custom environment consisting of a custom-built vehicle, a desktop computer, a camera and an open source marker library.

Keywords - path planning, autonomous ground vehicles, real-time computing, robotics

[1] U. Ozguner, T. Acarman, and K. Redmill, Autonomous Ground Vehicles, Artech House, 2011.

[2] P. E. Hart, N. J. Nilsson, and B. Raphael, “A formal basis for the heuristic determination of minimum cost paths,” IEEE Transactions on Systems Science and Cybernetics, 1968

[3] L. E. Kavralu, P. Svestka, J. C. Latombe, and M. H. Overmars, “Probabilistic roadmaps for path planning in high-dimensional configuration spaces,” IEEE Transactions on Robotics and Automation, 1996.

[4] S. M. Lavalle, “Rapidly-exploring random trees: A new tool for path planning,” tech. rep., 1998.

[5] M. Fakoor, A. Kosari, and M. Jafarzadeh, “Revision on fuzzy artificial potential field for humanoid path planning in unknown environment,” 2015.

[6] N.D. Richards, M. Sharma, and D. Ward, “Hybrid A*/Automaton Approach to On-Line Path Planning with Obstacle Avoidance,” in AIAA 1st Intelligent Systems Technical Conference, 2004.

[7] Lego. (2009) Mindstorms 2.0. [Online]. Available: https://www.bricklink.com/v2/catalog/catalogitem.page?S=8547-1#T=S

[8] Lego. (2009) 10287 Intelligent NXT Brick. [Online]. Available:https://lego.fandom.com/wiki/10287_Intelligent_NXT_Brick

[9] OpenCV. (2015) Detection of ArUco markers. [Online]. Available:https://docs.opencv.org/3.1.0/d5/dae/tutorial_aruco_detection.html