###### Abstract  
This academic paper addresses the challenges associated with trajectory planning for affordable and light-weight Unmanned Aerial Vehicle (UAV) swarms, despite limited computing resources and extensive cooperation requirements. Specifically, an imitation-based starling cluster cooperative trajectory planning technique is proposed for a fixed-wing model of a six-degree-of-freedom UAV cluster. To achieve this, dynamic trajectory prediction of the rapid random search tree is utilized to generate a track solution adapted to the terrain environment. Additionally, the Dubins aircraft path solution is applied as it is suitable for executing input track commands by the UAV model. Computational simulations on different cluster sizes show the approach can maintain the cluster state while navigating diverse terrains, with the track solution complying with the UAV's physical model properties. U  
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of birds, are capable of accomplishing complex clustering tasks using simple organs and intellect [10]. Algorithmic models of biocluster collaboration have been developed based on the understanding and modeling of biological behavior, and have proven advantageous in various application scenarios [11]. The distributed architecture of bionic clusters comprised of multiple unmanned aerial vehicles (UAVs) endows them with high fault tolerance and robustness, making them suitable for distributed centerless self-organization [12; 13]. This architecture allows for a multi-UAV system that is highly compatible and scalable. The UAVs simultaneously perform sub-tasks, enabling the splitting and parallel execution of the overall task, thereby enhancing execution efficiency [14]. Distributed UAV cluster collaboration algorithms draw inspiration from biological models where groups of organisms follow parallel behavioral rules but work together to accomplish complex group tasks such as foraging, defense against enemies, and nesting. These biological groups create a coordinated and orderly state that promptly and consistently responds to external interference patterns [15; 16]. By translating the internal principles of biological cluster behavior to cooperative UAV cluster control, it is possible to significantly enhance multi-UAV systems' autonomous decision-making and intelligent planning in complex situations. The bionic cluster model is highly adaptable, flexible, and effective. However, it is limited by the abilities of individual biological organs that make up the cluster. These limitations include a propensity for the cluster to become trapped in a local optimum solution, lengthy model convergence times, and limited individual intelligence that result in stereotypical performance in autonomous decision-making scenarios. Modern UAVs are much more sophisticated than many living creatures, with exquisite computational and perceptual capacities due to years of development. Consequently, the bionic algorithm used should effectively take advantage of the individual UAV's capabilities, and improvements and optimizations for UAV operating circumstances can be developed within the bionic-based model framework. The issue of controlling clusters of UAVs for low-speed, segmented flying duration rotorcraft has been extensively investigated in academic circles. For instance, Bisphagnia University conducted an experiment involving 20 quadrotor UAVs [17], which utilized an optical motion capture system for onboard attitude resolution estimation and control. The system is capable of autonomous operations via an external positioning system and adopts a group organizational structure, wherein centralized planning and control are closely coordinated in small teams. In addition, relatively parallel and independent distributed processing occurs between small teams, allowing the system to use a virtuous cycle to ensure the experiment's success.