Artificial Bee Colony Algorithm for Smart Car Path Planning in Complex Terrain

Smart cars or autonomous vehicles have garnered significant attention in recent years due to their potential to alleviate traffic congestion, enhance road safety, and improve fuel efficiency. However, effectively navigating through complex terrains requires the implementation of an efficient path planning algorithm. Traditional path planning algorithms often face limitations when confronted with intricate terrains. This study focuses on analyzing the path planning problem for intelligent vehicles in complex terrains by utilizing the optimization evaluation function of the artificial bee colony (ABC) algorithm. Additionally, the impact of turning radius at different speeds is considered during the planning process. The findings indicate that the optimal number of control points varies depending on mission requirements and terrain conditions, necessitating a comparison to obtain the optimal value. Generally, reducing the number of control points facilitates smoother paths, while increasing the number of trajectory control points results in a tendency for the calculated path to bend outward. The research investigates the application of the ABC algorithm for path planning in complex terrains for smart cars. The proposed algorithm exhibits the potential to enhance the navigation and performance of autonomous vehicles in complex terrains, thereby contributing to the development of more efficient and effective path planning algorithms for smart cars.