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Improved path-finding algorithm for robot soccers

This paper is devoted to the design of improved path finding algorithm that will find the near optimal path in a short time. The improved iterative RRT with path smoothing algorithm is developed and implemented in RoboCup Small Size robots. Through simulation it was shown that this algorithm can efficiently find desirable and near optimal solutions in short time. | Journal of Automation and Control Engineering Vol. 3, No. 5, October 2015 Improved Path-Finding Algorithm for Robot Soccers Rahib H. Abiyev, Nurullah Akkaya, Ersin Aytac, Irfan Günsel, and Ahmet Çağman Applied Artificial Intelligence Research Centre, Near East University, Lefkosa, North Cyprus, Turkey Email: {rahib.abiyev, nurullah}@neu.edu.tr motion it can be faced with other robots or obstacles, and execution is often associated with uncertainty. In most cases the environment is characterised with uncertainty, fast-changing dynamic areas with many moving obstacles. For a collision free motion to the goal, the path planning has to be associated with a local obstacle handling that involves obstacle detection and obstacle avoidance. There have been developed numbers of methodologies for robot navigation using path planning and obstacle avoidance. The most commonly used are the methods based on the use of Artificial Potential Fields (APF) [1], Vector Field Histogram (VFH) Technique [2], VFH+ Technique [3], Dynamic Window Approach [4], Agoraphilic Algorithm [5], Rule Based Methods [6], A star [7], fuzzy navigation [8], [9] etc The use of the above methods for path finding requires a lot of time and the finding of this path will not complete in reasonable time for real-time operation. The search of the path in a dynamic environment is important. In this paper the quick and feasible path finding problem is taken in hand. Rapidly-exploring Random Trees (RRTs) algorithm developed by Kuffner and LaValle can be efficiently used for robot navigation in a dynamic environment [10]. RRT Connect works by creating trees starting at the start and the goal configurations. The trees each explore space around them and also advance towards each other through the use of a greedy heuristic. This efficiently solves the path planning problem, even in high dimensions it results in shorter time than the search methods given above. But in some cases the RRTs does not find a short feasible