# An Improved Pied Kingfisher Optimizer for Maritime UAV Path Planning
## 1 Introduction  
As economic globalization deepens, maritime transportation, a critical component of international trade, has witnessed an increasingly pivotal role in ensuring the safety and efficiency of the global economy. With 3 million square kilometers of maritime territory and 18,000 km of coastline [1], China plays a crucial and indispensable role in the global maritime economy. Therefore, it is imperative for China to ensure stable growth in its ocean economy as it is vital for national development. However, the unpredictability nature of maritime accidents and the complexity associated with rescue operations pose significant challenges to search and rescue missions [2]. In recent years, rapid advancements in UAV technology have emerged as a crucial means to enhance rescue efficiency by expanding search coverage while reducing risks [3]. To this end, in 2023, the Chinese Ministry of Emergency Management and the Ministry of Industry and Information Technology jointly issued the \"Guiding Opinions on Accelerating the Development of Emergency Robots\", which explicitly outlined their goal to advance UAV technology specifically for maritime rescue applications [4]. Furthermore, underlining its commitment to strengthening emergency response capabilities during the \"14 th Five-Year Plan for Emergency Rescue Capacity Building\", China emphasizes the enhancement of UAV capabilities for effective maritime rescue [5]. The release of these policy documents not only underscores China's dedication to improving maritime rescue operations but also provides essential policy support and guidance necessary for the technological advancement and innovation within UAVs used in such scenarios. In the domain of maritime search and rescue, UAVs offer significant advantages over traditional manned vehicles such as rescue vessels and helicopters [6]. They exhibit excellent mobility, reduced operational costs, minimal takeoff and landing time, and eliminate the risk of personnel casualties [7]. These features enable UAVs to locate distressed targets at sea rapidly and efficiently, significantly enhancing the efficiency of search and rescue operations. Furthermore, with the continuous advancements in UAV-related technologies, including positioning, target recognition, communication, and control automation, the success rate of UAV search and rescue missions will further escalate. These advancements not only augment UAV operational capabilities in complex maritime environments but also introduce new possibilities for maritime search and rescue operations. As illustrated in Figure 1, UAVs encounter numerous challenges in maritime rescue missions, primarily due to the intricate maritime environment, the urgent response demands, and the inherent risks throughout rescue operations. The unpredictability of the maritime environment, including severe weather conditions, strong currents, and rough waves, poses significant obstacles to rescue operations and often renders traditional methods ineffective [8]. Furthermore, the perilous conditions involved in rescue missions, such as shipwrecks, fires, and drownings, also substantially amplify the risks associated with these operations. These factors necessitate that UAVs not only consider optimizing speed and energy consumption in path planning but also possess robust autonomous decision-making capabilities to navigate through treacherous marine conditions [9]. Therefore, path planning plays a critical role in ensuring the success of UAV maritime rescue missions and stands as one of the key technologies involved. The principles of algorithms form the basis for classifying path planning techniques into two categories: traditional path planning and AI-based path planning. Traditional methods in this field are typically rooted in classical mathematics and graph theory, such as the Artificial Potential Field (APF) method [10], the A* algorithm [11], and Rapidly-exploring Random Trees (RRT) [12].