Risk assessment is the evaluation of any project for the identification of potential threats to the people associated and after that, analysis and sequential evaluation of impacts, and the risk severity. Mitigation measures are then put in place to ensure that they lessen the severity of the effects or eliminate it in entirety (Allouch, Koubaa, Khalgui, & Abbes, 2019). The assessment of the risks associated with the Unmanned Air System (UAS) will thus involve:
Pre-Flight Risks
One of the risks related to the pre-flight assessment involves a payload that does not meet the system requirements leading to failure of the Ardupilot to control independent navigation (Allouch et al., 2019). Thus, the ground management workstation does not obtain a serial response from the autopilot system and also fails to identify targets together with their characteristics.
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Assessment of Take-off
Risks associated with take-off comprise of the malfunction of the XBEE PRO 900 XSC to configure properly. As a consequence, the XBee Configuration and Test Utility as multi-platform software fail to use the graphical interface in connecting operators to the digital radio frequencies (Kelly, 2017). Therefore, the mounted antenna on the UAS fails to pair before the flight and poses the likely effect of interfering with other devices during take-off and cause accidents.
Evaluation of Transit
The failure of the Ardu-fail-safe system to take control of the flight during stormy weather and consequent malfunctioning of the autonomous functionality could lead to the inhibiting of the safe landing of the UAS system leading to loss or damage to the device.
Mission Execution
During mission execution, conflicts between team members can lead to a lack of motivation necessary for the development team to initiate the implementation of the project (Kliem & Ludin, 2019). The risk might cause intra-communication challenges that might restrict the safe deployment of the UAS under either the safety pilot's control or the autonomous flight.
Assessment of Landing Risks
Misunderstanding of the requirements during bidding might lead to ambiguity in the application of the use of the UAS system and, as a result, cause emergencies where the Ardupilot loses control and fails to land safely regardless of the presence of the Futaba T7C remote control.
Evaluation of Post-Flight Conditions
The assessment of the risks related to the post-flight plans comprises of the UAS failing to fly at the designated maximal height of 400 feet. The consequences of such a threat could lead to the collision between the crewless vehicles with manned crafts.
The Suggested Mitigation Plan
All the risks associated with the unmanned aerial system have different mitigation actions. An example is the sub-standard payload that causes the UAS device to fail on the pre-flight test. The best mitigation action for such a risk is the evaluation of the design problems through revisiting the analysis capacity during the design stage. The development of an integration plan will go a long way in resolving the take-off risks. At the same time, advance confirmation of the functionality of the Ardu-fail-safe system will prevent its likely failure (Sangaiah, Samuel, Li, Abdel-Basset, & Wang, 2018). Training of team leaders and managers will have a significant impact on the resolution of conflicts. At the same time, the delivery of advance drafts from several checkpoints will mitigate any misconception during bidding. The assessment of the design plans to avoid post-flight risks is the best mitigation action during the risk assessment exercise.
References
Allouch, A., Koubaa, A., Khalgui, M., & Abbes, T. (2019). Qualitative and quantitative risk analysis and safety assessment of crewless aerial vehicles missions over the internet. IEEE Access , 7 , 53392-53410.
Kelly, T. A. (2017, April). Risk assessment for application of sensor technologies to overcome the security risks of unmanned systems. In 2017 IEEE International Symposium on Technologies for Homeland Security (HST) (pp. 1-3). IEEE.
Kliem, R. L., & Ludin, I. S. (2019). Reducing project risk . New York: Routledge. pp. 34-56
Sangaiah, A. K., Samuel, O. W., Li, X., Abdel-Basset, M., & Wang, H. (2018). Towards an efficient risk assessment in software projects–Fuzzy reinforcement paradigm. Computers & Electrical Engineering , 71 , 833-846.
