## 3 Experimental Methodology
### Experimental Setup  
The experiment is structured around two distinct operational modes. The first mode is known as the accompanying mode. In this mode, an MAV piloted by a human leads the navigation and the autonomous UAV follows its trajectory seamlessly. The second mode is designated as the automated mode. Here, the human pilot delegates a detection point to the autonomous UAVs, which then autonomously execute the detection. Given the hazardous zones in the experimental setting, a deep reinforcement learning algorithm, i.e., D3 QN, is employed to train the autonomous UAVs. This approach aims to ensure the UAVs' ability to perform surveillance tasks while simultaneously avoiding dangers.  
During the experiment, the positions of the detection points and the hazardous zones are predetermined. Leveraging this predetermined environmental data, the UAVs use the acquired training to continuously optimize their routes to targeted detection points. Understanding that task nuances might vary across different settings, establishing a baseline for the environment becomes a priority. Depending on the scenario, the UAVs hold the responsibility of pinpointing and accomplishing distinct detections, all guided by the strategic directive set by the human operator.  
As shown in Figure 5, our study utilizes a 1 \(\times\) 3 between-subject experimental design. The independent variable under consideration is the difficulty level of the surveillance task, which spans three categories: easy, normal, and hard. Specifically:  
* The easy level includes two detection points and danger zones.
* The normal level includes four detection points and danger zones.
* The hard level includes six detection points and danger zones.  
The dependent variable of the experiment is the participants' trust level in UAVs powered by AI algorithms. Due to the ongoing interaction between the human and the UAV, this trust level experiences dynamic shifts. Drawing from the definition of trust in HRI outlined in Section 2.2, we classify these trust variations into two phases: Static Trust and Dynamic Trust. Static Trust emerges before the establishment of collaboration made by the Trustor based on their cognitive judgment and is denoted as \(T_{0}^{n}\), where n corresponds to task difficulty. Dynamic Trust takes place after the collaboration is established, indicating the degree of trust the Trustor has towards the target trust entity, i.e., the Trustee, and is represented as \(T_{i}^{n}\), where \(i\) stands for the \(i\)th round of collaboration between the Trustor  
Figure 4: Layout of the experimental interface during the surveillance task. Here, green boxes indicate targets that require detection, while the red boxes denote the designated destination points.  
and the Trustee. Consequently, we can calculate the change in trust before and after the \(i\)th round of collaboration in the HRI process as:  
\[\Delta T_{i}^{n}=T_{i}^{n}-T_{i-1}^{n} \tag{1}\]  
where \(i\in\{i\,|\,0,1,2,\,\ldots\}\). A positive \(\Delta T\) value suggests that the HRI boosted trust, whereas a negative value indicates a decline in trust due to the interaction. By evaluating the influence of varying task difficulties on trust shifts pre- and post-HRI, our objective is to elucidate the degree to which task difficulty affects trust levels. Further, we aim to enhance our comprehension of the interplay between task difficulty and trust dynamics.