Colony Intelligence for Autonomous Wheeled Robot Path Planning

Abstract

Mobile robot path planning in dynamic environments answers the question of how to find the shortest path from the initial position to its final destination by avoiding any obstacle. This paper is trying to improve known probabilistic sampling-based algorithms for the road map robot planning introducing a hybrid between wave-front planner cell technique, tangent bug algorithm, and ant colony intelligence strategies, thus minimize the heuristic logic dropping ineffective paths to the target. The proposed colony intelligence tangent bug algorithm (CITBA) determines the shortest path taking into account available historical sensor data for the dynamic surroundings inside the landscape and collected from all autonomous robots while travailing.