Efficient path planning based on A*algorithm and annealing algorithm for Agricultural Unmanned Vehicles
Lingzhi Zhou, Yuqi Chen, Han Xia, Yujin Cao, M.K. Yang, Yuan Yuan, Man Zhou · 2024
This study presents an innovative approach for segmented farmland path planning in uncrewed vehicles, employing an enhanced hybrid path planning algorithm of annealing algorithm and A* algorithm within a Data Envelopment Analysis (DEA) model framework. Tailored for multi-base operations involving pesticide or electricity load-outs, this methodology incorporates a user-centric module for map data processing—a significant breakthrough that addresses this domain's complexities. Direct user engagement with intricate data through this module can substantially increase agricultural operational efficacy. The method's salient features encompass the importation of extant map data, the dynamism of real-time data integration, the preservation of user-driven alterations, and the facilitation of optimized route computation. Our empirical comparisons demonstrate that the multi-base construct considerably diminishes energy consumption and temporal inefficiencies during vehicle downtime, offering progressive insights into strategic agricultural path planning.