Path planning is a key technology for unmanned vehicles to realize autonomous navigation. Whether a safe and smooth travelable path can be quickly planned in a narrow channel determines the efficiency of unmanned vehicles in performing tasks in narrow and complex environments. However, common path planning algorithms usually have the problems of slow convergence speed, long planning time and poor path quality in the narrow channel environment. For this reason, this paper proposes a RRT-Connect algorithm Based on dual-layer guided sampling (DLGS-RRT-Connect) algorithm. First, the guided path is pre-constructed in the narrow channel, and the searching connection strategy is used to guide the random tree to expand along the guided path in the narrow channel, so as to reduce the invalid sampling and improve the exploration efficiency of the algorithm in the narrow channel. Secondly, the algorithm introduces a target bias strategy to reduce the randomness in the sampling process and provide directional guidance for the growth of the random tree, thus further improving the efficiency of path planning. Finally, the simulation results show that compared with the common Goal_bias RRT, Informed-RRT*, and RRT-Connect algorithms, the DLGS-RRT-Connect algorithm proposed in this paper improves the planning success rate in narrow channel environments by 35%, 60%, and 26%, respectively, and reduces the average planning time by 70.62%, 70.62%, 70.65%, and 97.65%, and 63.92%, and the average path length is also reduced by 14.53%, 16.70%, and 18.84%, respectively, which can effectively improve the smoothness and safety of planning paths in narrow environments.