## 1 Introduction  
In recent years, unmanned system technologies have developed rapidly, with UAVs being widely applied in civil and military domains such as logistics transportation [1, 2, 3], emergency rescue [4, 5, 6], telemetry surveillance [7, 8], and reconnaissance-strike missions [9, 10]. UAV swarms, characterized by a low cost, small volume, and large quantity, have demonstrated significant potential advantages across various mission scenarios. Among diverse application scenarios, collaborative search stands as a representative research focus in UAV swarm studies, being commonly encountered in missions including search and rescue, disaster monitoring, and close-range reconnaissance. The collaborative search mission features an unknown environment, multiple variables, and high dynamics, requiring UAV swarms to rapidly cover mission areas and maximize target detection within minimal timeframes. Additional constraints such as energy consumption and no-fly zones may apply depending on specific mission requirements, with varying priorities assigned to these indicators across different scenarios. To ensure that these mission indicators meet mission requirements, it is essential to establish a variable framework describing situation information within mission areas. In numerous mission scenarios, situational variables involved in UAV path-planning algorithms can generally be categorized into two classes: environmental model variables and aircraft platform performance parameters. The description of mission environments predominantly employs variables such as target detection probability, environmental uncertainty, digital pheromones, or analogous indicators. [PERSON] et al. [11] established a digital pheromone framework incorporating secretion, volatilization, and propagation mechanisms, alongside a target detection probability map updated via Bayesian principles. They further transformed the target detection probability map into an environmental uncertainty map using Shannon entropy calculations, serving as the foundation for swarm collaborative search path planning. [PERSON] et al. [12] utilized digital pheromones with only secretion and volatilization mechanisms, which cannot propagate to adjacent grids. Other studies [13; 14] have selectively adopted these variables. A synthesis of the existing literature indicates no strictly unified standard for environmental modeling variables, although common emphases include target distribution patterns and environmental awareness as critical situational aspects. The performance of UAV platforms themselves, including maneuverability and mission payload capabilities, significantly influences search effectiveness. Both [PERSON] et al. [15] and [PERSON] et al. [16] considered the impact of onboard sensor mounting angles and the field of view (FOV) on detection ranges. Beyond sensor parameter quantification, [PERSON] et al. [17] and [PERSON] et al. [18] incorporated remaining battery power into swarm cooperative decision-making algorithms, introducing energy cost indicators to investigate target search problems under energy-constrained conditions. In the selection of collaborative search decision-making algorithms, numerous studies have adopted heuristic optimization algorithms represented by Ant Colony Optimization (ACO) [13; 19], the Genetic Algorithm (GA) [13; 20], and Particle Swarm Optimization (PSO) [21; 22]. These algorithms are easy to implement and fast converging, yet prone to local optima. Neural networks demonstrate strong adaptability in modeling complex systems, hence their frequent application in swarm UAV decision making. Multiple studies employ deep learning combined with reinforcement learning methods [23; 24] to enhance swarm adaptability in complex environments. The aforementioned studies universally discretize mission areas into uniform square grid cells carrying situation information, where UAVs select appropriate grids from their immediate vicinity as waypoints.