# Improved DQN for Dynamic Obstacle Avoidance and Ship Path Planning
## 2 Related Work  
Research on intelligent collision avoidance of ships can be divided into actual ship research and virtual simulation. As a modern means of transportation, ships have high costs and high energy consumption. Therefore, conducting intelligent collision avoidance experiments with accurate ships in natural channels is costly and inefficient. It also faces possible safety problems caused by uncontrollable experimental conditions. If the navigation operation is slightly careless, it can cause serious safety concerns and substantial economic losses. Moreover, the actual ship is under the complex interference of external wind and waves, and the test scenarios and results have strong randomness and poor repeatability [16]. Therefore, this paper uses the virtual simulation software to simulate the ship navigation scene, build the shipping agent, restore the ship navigation state, and train the ship collision avoidance intelligence through the simulation experiment.  
The ship's motion is based on navigation control, including positioning. Nevertheless, path planning is an essential aspect of navigation. Path planning aims to find an optimal path that meets the requirement of not intersecting with any obstacle from beginning to end in a specific environment. The path generated by ship path planning plays a navigation role in its movement, guiding the ship to start from the current point, avoid obstacles and reach the target point. The navigation control module includes global path planning and local path planning. [PERSON] et al. [17] proposed a deterministic method called multiple sub-target artificial potential field (MTAPF). MTAPF belongs to the local path planning algorithm and refers to the globally optimal path generated through an improved heuristic \(A_{*}\) algorithm. This algorithm divides the optimal path into multiple sub-target points, forming a sequence of sub-target points, using the improved \(A_{s}\) algorithm to obtain the optimal path, using the MTAPF algorithm for local path planning, and adopting a priority strategy to prevent collisions in local path planning. [PERSON] et al. (2018) proposed a real-time and deterministic path-planning method for autonomous ships in complex and dynamic navigation environments. The improved artificial potential field method includes improved repulsive potential field function and corresponding virtual forces to solve the collision avoidance problem of dynamic targets and static obstacles, including emergencies. [PERSON] et al. (2019) proposed a dynamic path-planning algorithm based on the A-star algorithm and ship navigation rules. The improved algorithm's dynamic search mechanism, considering time factors, aims to avoid collisions when moving obstacles are known. The function of global path planning is to establish a viable path from the starting point to the target point according to the existing electronic map. Local path planning, also known as local obstacle avoidance, refers to perceiving unknown obstacles in ship operation and redefining the local path to bypass the obstacles and move towards the target point. When the global and local path planning algorithms work together, the ship can complete the autonomous motion from the starting point to the target point. As a core function, autonomous navigation is the key to realizing ship perception and motion. Path planning and positioning are essential to the ship navigation control system and constitute effective ways to achieve ship autonomy and intelligence. The simulation of ship collision avoidance path planning is divided into two aspects. The first is to simulate the environment model of areas with moving obstacles and accessible moving areas. The second step is to select the appropriate path search algorithm according to the established environment model to achieve fast and real-time path planning.