## 3 Experimental Methodology  
### Experimental Design  
Recent advancements in HRI research demonstrate a variety of experimental approaches, each offering unique contributions to the field. One study utilizes the Gazebo simulation framework, renowned for its realistic physics simulations, to examine various UAV indoor navigation control methods [51]. This approach is particularly effective in environments with complex obstacles, providing critical insights into advanced control algorithms. However, a notable limitation of this approach is the reliance on simulated settings, which may not accurately reflect real-world scenarios. Another experimental approach involves integrating multi-modal interactions, such as speech, touch, and gesture,into UAV ground control stations [52]. This method, employing virtual simulation technology, is designed to enhance operational efficiency and user experience in HRI. Despite its potential, it confronts challenges like increased cognitive load and variable accuracy across different interaction modes. Additionally, studies have embraced the Unity platform for simulating complex HRI scenarios, especially those involving AI-autonomous drone systems [53]. This methodology is valuable for investigating human-robot trust dynamics in contexts that mimic real-world conditions. Nonetheless, Unity-based simulations share a common limitation with Gazebo: the difficulty in fully replicating the unpredictability of actual environments. The selection of an appropriate simulation platform is a critical decision that significantly influences the study's methodology and outcomes. Gazebo, with its seamless integration with the Robot Operating System (ROS), is suited for developing robotic systems that necessitate high-fidelity environmental interactions. However, for the objectives of this particular study, Unity was selected as the more fitting platform, given its specific strengths that align closely with the needs. Unity distinguishes itself with its user-friendly interface, which is instrumental in simplifying the development process. This accessibility is particularly beneficial in HRI research, where the focus often shifts between various aspects of interaction dynamics, requiring a platform that is adaptable and easy to manipulate without extensive programming expertise. Furthermore, Unity's advanced graphical capabilities are essential for creating realistic and immersive simulations, pivotal for validating the experimental findings of this study. The platform's ability to render detailed and visually engaging environments adds a layer of authenticity to the simulated scenarios, enhancing the overall quality and reliability of the research. Additionally, Unity's versatility in accommodating a diverse range of interaction modalities perfectly aligns with the study's aim to explore the complexities of HRI. This adaptability is vital for constructing intricate and comprehensive interaction scenarios, allowing for an in-depth examination of how humans interact with robotic systems under various conditions. In this study, a Unity-based experimental platform was employed to simulate the surveillance task of unmanned aerial vehicles (UAVs) offering a rich canvas for insights. UAVs, as symbols of automation and intelligence, require nuanced interactions with human operators, especially in high-stakes conditions where the degree of trust becomes a key factor for collaboration success [54; 55]. The experimental setup created in Unity simulates a surveillance task involving one human-operated named aerial vehicle (MAV) and four autonomous UAVs using deep reinforcement learning algorithms. This setup, shown in Figure 2, includes varying complexities, such as different numbers of detection points, danger zone ranges, and area densities to be monitored. For instance, a simple task might involve a single-point detection in a low-threat environment, while a complex task could require coordination between MAVs and UAVs to identify multiple points across expansive areas with intermittent danger zones.