Abstract: In addressing the challenges of slow iteration speed and low path safety in the optimization process of unmanned agricultural machinery path planning under complex environments in smart agriculture, a hybrid ant colony algorithm was proposed, integrating artificial potential fields, quantum behavior and a B-spline-based smoothing strategy. This method introduced artificial potential fields in the early iterations to address the issues of slow iteration speed and balance global optimality. In the mid-term of path optimization, quantum behavior was incorporated to enhance the algorithm’s capability to obtain high-quality solutions by adjusting the information density threshold, improving algorithm state selection probabilities, and avoiding local optima. In the later stages of iteration, the B-spline-based smoothing strategy was integrated to optimize the optimal path and enhance the obstacle avoidance capability of unmanned agricultural machinery. Simulation experiment results demonstrated that the unmanned agricultural machinery based on the hybrid ant colony algorithm showed significantly improved path optimization ability in complex environments. The response speed of path optimization was increased by 73 times, and the distance was reduced by over 11.8% after path optimization.

Key words: smart agriculture, unmanned agricultural machinery, path optimization, hybrid ant colony algorithm, obstacle avoidance, artificial potential fields