DGA-ACO: Enhanced Dynamic Genetic Algorithm—Ant Colony Optimization Path Planning for Agribots

Recent advancements in agricultural mobile robots (agribots) have enabled the execution of critical tasks such as crop inspection, precision spraying, and selective harvesting. While agribots show significant potential, conventional path-planning algorithms suffer from three limitations: (1) inadequate dynamic obstacle avoidance, which may compromise operational safety, (2) premature convergence to local optima, and (3) excessive energy consumption due to suboptimal trajectories. To overcome these challenges, this study proposes an enhanced Dynamic Genetic Algorithm—Ant Colony Optimization (DGA-ACO) framework. It integrates a 2D risk-penalty mapping model with dynamic obstacle avoidance mechanisms, improves max–min ant system pheromone allocation through adaptive crossover-mutation operators, and incorporates a hidden Markov model for accurately forecasting obstacle trajectories. A multi-objective fitness function simultaneously optimizes path length, energy efficiency, and safety metrics, while genetic operators prevent algorithmic stagnation. Simulations in different scenarios show that DGA-ACO outperforms Dijkstra, A*, genetic algorithm, ant colony optimization, and other state-of-the-art methods. It achieves shortened path lengths and improved motion smoothness while achieving a certain degree of dynamic obstacle avoidance in the global path-planning process.