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

Zhenpeng Zhang, Pengyu Li, Shanglei Chai, Yukang Cui and Yibin Tian ()
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Zhenpeng Zhang: College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China
Pengyu Li: College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China
Shanglei Chai: College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China
Yukang Cui: College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China
Yibin Tian: College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China

Agriculture, 2025, vol. 15, issue 12, 1-25

Abstract: 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.

Keywords: agricultural robots; safety compliance; path planning; obstacle avoidance; genetic algorithm (GA); ant colony optimization (ACO); hidden Markov model (HMM) (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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