## 4 Discussion  
This study presented the Performance Evaluation and Review Framework of Robotic Missions (PERFORM), a flexible but rigorous method to validate autonomous vehicles in testbed environments using a unique IT2-FL performance evaluation framework. The use of FL allows for test parameters that are tailored to the user's design requirements and can account for different priorities related to acceptable risks and goals of a given mission. The 3D renderings presented show parameter values for a specific mission across the space of reasonable test results in relation to a performance score output. This, in addition to the decomposition of a mission, \(M\), into tasks, \(T\) (Equation (2)), reduces the number of test runs necessary with the ability to analyze different scenarios taking on a range of values. Translating into both a time and cost savings, this may limit the need to perform a large number of sets of simulations, which can have difficulty modeling the ocean environment accurately, testing sensor perception capabilities, and predicting other hardware issues. Results indicate that these methods aid in direct comparison of path planning algorithms as presented in the case studies with broader applications to other high-level validation test objectives such as autonomous behavior analysis. In viewing the results for Case Study I, the 3-MF system resulted in a negligible difference in scores. The 5-MF system, however, allowed for an easier differentiation of scores due to increased value sensitivity, producing a more substantial difference in value. One should weigh the number of included MFs based on a balance of available test design time and desired score resolution. A quick and simple study could utilize a 3-MF system, while a higher MF system (in this case, a 5-MF system) can be designed if increased complexity is needed. In Case Study II, analyzing behavior regarding balancing risk with efficiency, the PFM/A* path taken to avoid the obstacles received a better score than that of the PRM path. This was predicted as the FIS was intentionally modeled to incentivize conservative decisions by the vehicle in the final performance score. During general implementation, there may be test cases where evaluation parameters will appear to have conflicting goals such as in this scenario--safety vs. efficiency. The user, then, must determine the acceptable risk and hierarchy of performance priorities and reflect this in the modeling of the system. Figure 24: Output set for Case Study III. \begin{table}
\begin{tabular}{c c} \hline \hline
**Path Planning Algorithm** & **Performance Score** \\ \hline PFM/A* & 6.94 \\ \hline PRM & 2.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Performance Score Output for Case Study III. The final scenario, Case Study III, took a stricter approach to a vehicle, receiving a desirable score. As shown in Figure 22, there is a steeper decline to poorer performance values. In some applications, there is a strict cutoff of acceptable performance. The National Oceanic and Atmospheric Administration (NOAA), for instance, has set hydrographic surveying standards that are required to be met for data to be utilized by the organization [40]. With this in mind, the evaluation model has the ability to incorporate these standards in the design of both the MF's and the rule base. PERFORM becomes streamlined once a testbed environment is established and common measurement sensors are incorporated and calibrated. Many testing scenarios can be evaluated with the incorporation of minor changes, which would depend on given specific testing goals. The authors envision a library (expanded upon over time) of testing parameters with the x-axis MF range being the only necessary change based on the specific task under test.