Shortest Path Planning using Ant Colony Optimization (ACO)

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    During my undergraduate studies, I have had experience working on the intelligent robotic path planning system using Ant Colony Optimization (ACO) technique as the research project. Given an aerial image of an area that consists of obstacles, image processing is performed to identify the obstacles and their corresponding corner coordinates, with respect to the origin defined in the area.

    A Voronoi diagram is then constructed using the obstacles’ corners and the area boundaries’ coordinates. After removing the edges of the Voronoi diagram that intercepted with the obstacles, the remaining edges became obstacle-free paths within the area. Given a start and a goal position, the ACO algorithm is applied to find the shortest path between the two positions. If found, the path returned by the path planning system is the shortest and obstacle-free between the start and goal positions.