From naval operations to ocean science missions, the importance of autonomous vehicles is increasing with the advances in underwater robotics technology. Due to the dynamic and intermittent underwater environment and physical limitations of underwater unmanned vehicle (UUV), feasible and optimal path planning is crucial for autonomous underwater operations. According to different mission, the path planning method of UUV is divided into two categories: the point to point path planning and the complete coverage path planning. The objective of this thesis is to develop and demonstrate an efficient underwater path planning method that is adapted to complicated ocean environment. In this thesis, existing path planning method for the fields of ocean science and robotics are first reviewed, and then local dynamic obstacle avoidance method is proposed to avoid dynamic obstacles. Based on this again, the path planning of UUV in local dynamic environment can be efficiently implemented by adopting rolling window path planning method and local dynamic obstacle avoidance method. This method with the guide point strategy combines global path planning with local dynamic path planning, so that not only the requirements of real-time on-line path planning for UUV are met, the global optimality is also considered. A navigation route for UUV is planned in advance by using priori environmental information based on ant colony algorithm, so it provides the reference information for the selection of guide point. In order to solved the problem of area coverage search, a complete coverage path planning method is proposed by combining ant colony algorithm with biologically inspires neural network. In order to demonstrate underwater path planning method, all of the above ideas and methods developed were tested in simulation experiments.