Delivering temperature-sensitive goods to remote mountain regions is difficult due to sparse infrastructure and challenging terrain.Unmanned aerial vehicles (UAVs) offer a practical option for fast, last-mile transport in such areas, but ensuring cold-chain reliability during flight remains a problem.We present a UAV-based cold chain logistics framework that integrates a hybrid path-planning approach, combining genetic algorithms with simulated annealing to optimise routes for both energy efficiency and terrain constraints.Onboard IoT sensors continuously record temperature and humidity, allowing real-time intervention if cargo conditions drift from required ranges.Tests with four real-world datasets showed up to a 14% reduction in energy use, faster delivery times, and improved temperature stability compared to baseline planners.These results suggest that combining intelligent routing with in-flight environmental monitoring can make UAV cold-chain delivery more reliable in difficult environments.
Xingbing Fan (Thu,) studied this question.