Route Planning for Unmanned Maize Detasseling Vehicle Based on a Dual-Route and Dual-Mode Adaptive Ant Colony Optimization
Yu Wang, Yanhui Yang, Yichen Zhang, Lianqi Guo, Longhai Li · Agriculture · 2025
Maize is crucial for food, feed, and industrial materials. The seed purity directly affects yield and quality. Advancements in automation have led to the lightweight unmanned maize detasseling vehicle (UDV). To boost UDV’s efficiency, this paper proposes a dual-route and dual-mode adaptive ant colony optimization (DRDM-AACO) for the detasseling route planning in maize seed production fields with hybrid spatial constraints. A mathematical model is established based on a proposed projection method for male flower nodes. To improve the performance of the ACO, four innovative mechanisms are proposed: a dual-route preference based on the dynamic selection strategy to ensure the integrity of the route topology; a dynamic candidate set with the variable neighborhood search strategy to balance exploration and exploitation; a non-uniform initial pheromone allocation based on the principle of intra-row priority and inter-row inhibition, and direction-constrained adaptive dual-mode pheromone regulation through local penalty and global evaporation strategies to reduce intra-row turnback routes. Comparative experiments showed DRDM-AACO reduced the route by 6.2% compared to ACO variants, verifying its effectiveness. Finally, experiments with various sizes and actual farmland compared DRDM-AACO to other various algorithms. The route was shortened by 32%, confirming its practicality and superiority.