# Controlling Remotely Operated Vehicles with Deterministic Artificial Intelligence
## 4 Discussion
### Recommendations for Future Research  
This manuscript proposed a controller architecture and tested it for yaw control of an ROV under idealized conditions, namely no disturbances and perfect measurements.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Rise Time (s)** & **Settling Time (s)** & **Overshoot (deg)** & **Error (RMS deg)** \\ \hline First Step & 2.362 & 2.608 & 0.104 & 0.0426 \\ \hline Third Step & 2.413 & 2.425 & 0.083 & 0.0164 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Response performance for the first and third positive 30-degree heading changes.  
This presents many avenues for future work. One area of future work involves expanding the controller testing and validation with the end goal of a control system operating in real-world open ocean conditions. The first step in this is to apply the controller to the ROV's other degrees of freedom: roll, pitch, position and depth. Then performance of these controllers will be assessed under simulated conditions more akin to a real-world operating environment. This will involve adding measurement noise and disturbances such as wind, swell or tether dynamics to the simulated test environment. After demonstrating performance under non-ideal simulated conditions the research will progress to laboratory validation on real vehicle hardware, and finally to assessing the performance of the proposed controller on a \"live\" ROV in open water conditions.  
Another area of future work involves assessing and improving the control architecture itself. The controller's adaptive nature will be tested to determine the controller's robustness to changes in system dynamics, for example from damage to the vehicle or changes in payloads. Alternate trajectory generation schemes will also be explored. These will focus on optimal trajectories that for example minimize energy consumption, and trajectories generated under constraints such as maximum and minimum applied force, or maximum slew rate of actuators that better reflect the actual operating capabilities of the ROV. Previous work such as [26] has evaluated the efficacy of DAI-based controllers against \"classic\" controllers, but an important area of future research will involve comparing this DAI controller against controllers based on other forms of artificial intelligence such as deep learning. Finally, analysis of the computational burden required for this controller should be performed in order to determine minimum required system specifications.  
The cited lineage (e.g., [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], etc.) develops dynamic-based controls in order to instill inherent resilience to dynamic parameter change, and sources of such change include damage, fuel expenditure, and sudden grasping of objects by the remotely operated vehicle. Following this prequel research, the limits of efficacy of the validated approach should be investigated to ascertain the ability to autonomously reject the deleterious effects of such changes. Next, bench-board laboratory experiments should be performed to prepare for validating hardware experiments in the open ocean.  
Conceptualization, S.O. and T.S.; methodology, S.O. and T.S.; software, S.O.; validation, S.O. and T.S.; formal analysis, S.O.; investigation, S.O. and T.S.; resources, T.S.; writing--original draft preparation, S.O. and T.S.; writing--review and editing, S.O. and T.S.; visualization, S.O. and T.S.; supervision, T.S.; project administration, S.O. and T.S.; funding acquisition, T.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
For access to archived datasets analyzed or generated during the study, please contact the corresponding author.  
The authors declare no conflict of interest.