A novel path planning approach for plant protection UAV based on DDPG and ILA optimization algorithm

Abstract

With the advancement of agricultural modernization, plant protection UAVs play an increasingly crucial role. However, traditional path planning methods struggle to meet the demands of irregular work areas and autonomous obstacle avoidance during transfers. This research introduces a novel dual-layer planning architecture, which innovatively proposes the synergistic combination of artificial intelligence and optimization algorithms in this field. Specifically, the DDPG algorithm is applied to the path planning between farmlands. By constructing a virtual environment replete with random obstacles based on actual geographical data, the UAV learns the optimal response strategy. Minimizing flight path length and turning amplitude is the objective, and multiple reward mechanisms are devised to accelerate convergence, enabling real-time and efficient obstacle avoidance. For the spraying operations in irregular farmlands, the ILA optimization algorithm is utilized. A trajectory planning model considering the UAV’s heading is established, and optimization criteria are formulated. Through this algorithm, the optimal operation route is determined. Simulation results reveal that in the inter-farmland path planning, compared with the Particle Swarm Optimization Algorithm (PSO) and the Zebra Optimization Algorithm (ZOA), the method based on Deep Deterministic Policy Gradient (DDPG) generates paths with shorter lengths and requires less flight time, and demonstrates excellent adaptability to unknown and dynamic obstacles. In the intra-farmland path planning, the ILA optimization algorithm improves the rebroadcast rate and reduces the turn times by 6.4% and 7.7% respectively compared to the particle swarm optimization algorithm. Overall, the integration of DDPG and ILA optimization algorithms successfully addresses the global path planning challenges of plant protection UAVs in complex agricultural scenarios.