Optimizing unmanned farm paths to improve farm management efficiency and resource utilization is a hot research topic in the field of mobile robot navigation. An improved sparrow search algorithm ( ISSA) incorporating improved Q-learning ( IQL) algorithm was designed to address the problems of low search efficiency and smooth paths that can easily fall into local optimization of traditional sparrow search algorithm (SSA) and reinforcement learning algorithm. ISSA incorporating the improved IQL algorithm was designed for global path planning of mobile robots in combination with Bessel curves. Firstly, a multi-strategy initialization of the population was used at the beginning of the algorithm, combining the IQL algorithm with Logistic chaos mapping and Latin hypercube sampling (LHS) methods to provide excellent and diverse initial solutions for the population;secondly, a linear dynamic inertia weight adjustment method was introduced into the finder position updating to balance the algorithm’s global search capability and local exploitation capability, and improve the convergence speed of the algorithm;then, the reverse learning strategy was introduced into the vigilant to further explore the unexplored area and prevent falling into the local optimal solution;finally, the path was smoothed by combining obstacle avoidance algorithms and Bessel curves to eliminate the problems of traveling paths too close to obstacles and unsmooth paths. The effectiveness and superiority of ISSA algorithm was verified through comparative simulation tests on Matlab platform. The experimental results showed that the ISSA algorithm effectively combined the self-learning characteristics of the IQL algorithm and the powerful search capability of the SSA algorithm, which significantly improved the efficiency of global path optimization and generated smoother paths in both the grid simulation environment and the field scenario. In the field scenario, the ISSA algorithm reduced the path planning time by 64.43% and 9.94% , and the average value of the shortest path length by 8.3% and 12% , respectively, compared with the SSA and ACO algorithms, which provided a high-quality path planning solution for the unmanned farm robots to work accurately and efficiently.