In the context of the digital twin engineering of large smart hub seaports, port path planning faces more complex challenges, such as efficient logistics scheduling, unmanned transportation, coordination of port automation facilities, and rapid response to complex dynamic environments. Particularly in applications like robotic inspection, how to effectively plan paths, improve inspection efficiency, and ensure that robots complete tasks within their limited energy capacity has become a key issue in the design and realization of digital and intelligent seaport systems. To address these challenges, a path planning algorithm based on an improved Rapidly-exploring Random Tree (RRT) is proposed, considering the complexity and dynamics of the port’s digital twin environment. First, by optimizing the search strategy of the algorithm, the flexibility and adaptability of path planning can be enhanced, allowing it to better accommodate changes in the environment within the digital twin model. Secondly, an appropriate heuristic function is constructed for the digital twin seaport environment, which can effectively accelerate the convergence speed of the algorithm and improve path planning efficiency. Finally, trajectory smoothing techniques are applied to generate executable paths that comply with the robot’s motion constraints, enabling more efficient path planning in practical operations. To validate the feasibility of the proposed method, a combination of virtual and real digital twin environments is used, comparing the path planning results of the improved RRT algorithm with those of the traditional RRT algorithm. Experimental results show that the proposed improved algorithm outperforms the traditional RRT algorithm in terms of sampling frequency, planning time, path length, and smoothness, further validating the feasibility and advantages of this algorithm in the application of intelligent seaport digital twin engineering.
Jiang et al. (Fri,) studied this question.