The process of path planning plays a crucial role in enabling self-directed movement, particularly for unmanned aerial vehicles. This involves accommodating diverse priorities, such as route length, safety, and energy efficiency. Traditional techniques, including geometric and dynamic programming, have historically been employed to address this challenge. However, recent years have testified to an increasing prevalence of artificial intelligence methodologies such as reinforcement learning. This study introduces a novel approach to offline path planning in static environments, utilizing Q-learning as its foundation. The method optimizes three pivotal factors: path length, safety, and energy consumption. By effectively balancing exploration and exploitation, this technique enables an autonomous agent to efficiently navigate from any initial point to a specified destination on the map. To evaluate the proposed strategy’s effectiveness, extensive simulations are conducted across diverse environments. A comparative analysis with three established strategies showcases the algorithm’s proficiency in generating feasible routes. The user can freely tailor the system’s priorities by modifying each of their weights prior to training. Additionally, scalability tests reveal the algorithm’s swift convergence, achieving stability within just 35 s for larger environments spanning up to 40 × 40 units. To further validate the proposed approach, both simulations and real-world experiments are employed, collectively demonstrating its performance and applicability. • An algorithm that balances path length, safety, and energy in UAV navigation. • Dynamic priority adjustment for training, tailored to different applications. • An optimal policy guiding the agent to its goal, ensuring robustness to disturbances. • Validation through simulations and real-world experiments.

Consumers are increasingly demanding high-quality food, which presents significant challenges for agricultural supply chains. While the majority of research in the agri-food sector has concentrated on optimizing logistics costs and meeting demand by focusing on minimizing the last mile, the complexity of the first mile in the agricultural supply chain has been less explored. Farmers must efficiently manage the harvesting process and the transportation of harvested produce to consolidation centers to ensure the delivery of high-quality products. This paper addresses this research gap by introducing a mixed-integer programming model that leverages vehicle routing problem concepts to optimize the logistics processes involved in transporting harvested products from various fields to a central depot. The primary objective is to minimize total logistics costs associated with visiting different fields during a pick-up round using multiple vehicles. The model has been applied to a case study involving an agricultural cooperative in Greece as part of the European BBTWINS project, which aims to enhance agri-food value chain digitalization for improved resource efficiency. The results demonstrate that organizing the first mile of the agri-food supply chain with a cooled vehicle for pick-up rounds can reduce logistics costs by up to 40% compared to the current practices.