Unmanned System Operations, Anomalies, and Recovery

Unmanned Aerial Vehicles have been widely used in military operations for the last decade and a half. General Atomics MQ-9 Reaper, also known as Predator B, had been the backbone of the unmanned fleet. The UAV’s most critical component is its navigation system, as, despite the advanced payload, it is still a plane, and a plane’s purpose is to get from point A to point B. The navigation systems of UAVs, especially military ones, are highly redundant and sophisticated, yet they still experience high failure rates (Petritoli, Leccese, & Ciani, 2018). The MQ-9 is directly controlled from a command installation by two operators using line-of-sight communications. The drone also uses the Iridium satellite system as a redundancy in case the line-of-sight is lost, which provides limited control to the operators (Carrigan, Long, Cummings, & Duffner, 2008). The drone also enables its emergency lost-link profile, which makes it loop over pre-determined waypoints at a pre-determined altitude. In a particular crash in 2006, a critical flaw within that system was exposed.

In the case of engine shutdown, the drone turns its electronic systems off to preserve its emergency power supply. Among the first to go is the Iridium satellite connection, which allows operators to exert some control over the machine. The auto-ignition system that would restart the engine is connected to the Iridium satellite system, which means that an engine fault that would require restarting severs the connection that is required to restart the engine. In the case of the 2006 crash in Arizona, several coincidences resulted in a single operator switching the engine off by accident. Because of this, the drone could not retain its attitude, lost its line-of-sight with the operators, lost its satellite connection, could not restart the engine, and crashed.

To prevent failures such as these, the drones should be equipped with on-board measures to detect and report or fix anomalies, especially those stemming from human error. Wang, Chen, Liu, and Peng (2018) describe a particular solution for an embedded intelligent anomaly detection system. An auto-ignition system that was not reliant on satellite or line-of-sight connections would save the Predator B that crashed in Arizona. Such a system could be connected to the EIADS, as could many others, making drones more autonomous and improving their survivability.

References

  1. Carrigan, G., Long, D., Cummings, M. L., & Duffner, J. (2008). Human factors analysis of Predator B crash. Web.
  2. Petritoli, E., Leccese, F., & Ciani, L. (2018). Reliability and Maintenance Analysis of Unmanned Aerial Vehicles. Sensors, 18(9), 3171.
  3. Wang, B., Chen, Y., Liu, D., & Peng, X. (2018). An embedded intelligent system foonlinene anomaly detection of unmanned aerial vehicles. Journal of Intelligent & Fuzzy Systems, 34(6), 3535–3545.

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StudyCorgi. "Unmanned System Operations, Anomalies, and Recovery." January 31, 2022. https://studycorgi.com/unmanned-system-operations-anomalies-and-recovery/.

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StudyCorgi. 2022. "Unmanned System Operations, Anomalies, and Recovery." January 31, 2022. https://studycorgi.com/unmanned-system-operations-anomalies-and-recovery/.

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