## 1 Introduction  
The construction of large cruise ships involves a huge amount of work, and the requirements for on-time delivery are extremely strict. One of the difficulties of building cruise ships compared to ordinary ships is the large amount of cabin construction work [1]. To overcome the shortcomings, such as crossover of cabin installation processes, harsh construction environment on board, crowded workforce, and poor safety management [2, 3], prefabricated manufactured modular cabin unit (PMCU) technology has been adopted by most cruise ship manufacturers. Unlike the traditional cabin building process, the PMCU assembly process is transferred to the workshop [4]. The assembled PMCUs are transported to the target locations for installation by workers on deck using specified devices, as shown in Figure 1[5, 6]. Usually, before the cabin pushing operation begins, workers will make a preliminary design of the route based on the drawings. However, in the complex deck construction environment, the initial design route could not fully consider all the details of the cabin pushing process. For the complex construction environment on deck, reasonable path planning can effectively improve the efficiency of the PMCU pushing. However, the lack of detailed path planning now makes it difficult for PMCUs to reach the target location smoothly, as spatial blocking and collisions often occur, which have a great impact on continuous production operations. Therefore, it is important to study the path planning problem of the cruise ship PMCU pushing operation process to ensure that the PMCU can reach the designated installation position on deck smoothly and support the continuous production of PMCU pushing. Further, it also provides support for the future application of intelligent transportation equipment in the installation process of PMCUs. Path planning is a prerequisite for cabin pushing operations to reach the target position and perform the installation, which is to find a collision-free path from the starting point to the target point in the environment with obstacles according to some evaluation criteria [7]. The existing path planning methods primarily include: 1. Heuristic rule-based graph search algorithms, such as the Dijkstra algorithm [8] and A* algorithm [9; 10; 11]; 2. Probability-based path planning methods, such as the Rapidly-exploring Random Tree algorithm (RRT) [12; 13], Optimal Rapidly-exploring Random Tree algorithm (RRT*) [14; 15], and Probabilistic Roadmap Method (PRM) [16]; 3. Algorithms based on the assumed gravitational field, such as the Artificial Potential Field method (APF) [17; 18]; 4. Meta-heuristic algorithms, such as the Grey Wolf Optimization algorithm (GWO) [19; 20], Particle Swarm Optimization algorithm (PSO) [21; 22], etc. ; 5. Path planning algorithm based on reinforcement learning (RL) [23; 24; 25]. Most of the above algorithms usually treat the object as a point, considering only the position and ignoring the pose and kinematic constraints during the movement. The calculated paths are usually focused on the shortest path length and cannot be used directly for mobile execution, requiring post-trajectory optimization. The path planning method can satisfy the collision-free constraint of PMCU geometric center, but it cannot guarantee that the outer contour also satisfies the collision-free constraint. Therefore, regarding PMCUs as a point and ignoring their external contour, the algorithm is obviously unable to support the obstacle avoidance constraints in the process of cabin pushing. Also, the trajectory optimized according to the planned path cannot fully satisfy the actual kinematic constraints. Some of the improvement methods that consider the outer contour collision are mainly two methods; one is the obstacle expansion method [26] and the other is the spatial corridor method [27], which are applicable to the scenarios that have a large passable space.