To address the issues of insufficient path search capability and local optimum in swarm intelligence algorithms for UAV path planning in battlefield environments，a UAV low-altitude penetration path planning method based on Undirected Random exploration Dung Beetle Optimization (UR-DBO) algorithm is proposed. Firstly，terrain and threat models are established. Then，population diversity of the algorithm is enhanced by using Piecewise map in initialization. An undirected random exploration mechanism is proposed，which aims to make up for the defect of incomplete exploration of the ball dung beetles in DBO algorithm and improve the global optimization ability of the algorithm. Subsequently，the stealing dung beetle can learn from the multi-strategy optimization mechanism of Ali Baba and the Forty Thieves (AFT) algorithm，so that it can dynamically adjust its strategy according to the problem，which is beneficial to the algorithm to jump out of local optimization. Finally，six test functions and two kinds of different terrains are selected for test. The experimental results show that the UR-DBO algorithm has better convergence speed and accuracy than the contrast algorithms，and is more suitable for UAV three-dimensional path planning.