Path Planning Based on Probabilistic Roadmap and Ant Colony Optimization

In order to handle the path planning problem in complex environments, a novel method combining probabilistic roadmap, ant colony optimization, and third order Bezier curve has been developed in this study. There are three steps in the suggested procedure. Using a probabilistic roadmap approach, a random map is first created based on the complexity of the surrounding area. This could be accomplished by selecting $\boldsymbol{N}$ nodes randomly in complex static environments, then establishing connections between these nodes according to specific criteria or conditions. The created roadmap offers a significant number of potential path segments that could link the start and final point. The second stage entails choosing a path within the already-built roadmap. The final path between the start point and the goal point will be searched by using ant colony optimization. Then, the third stage employs a Bezier curve to smooth out and shorten the obtained path. Several simulations in different environments demonstrate that the proposed approach guarantee to obtain a short, smooth, and safe path between the start point and the goal point.