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Unmanned Control Aerial Systems Review


Unmanned vehicles have grown in popularity, especially because of their versatility in use. These autonomous vehicles are used in multiple areas such as monitoring disaster areas, an inspection of projects and infrastructure, military operations, and spraying of agricultural chemicals, among others. The unmanned vehicles are also equipped with cameras, sensors, GPS, and communication equipment essential in sending and receiving instructions from the human controllers and other navigation purposes. The focus of this research is on the control systems of the unmanned vehicles, including the equipment, how time delays influence the manual control methods, the source of time delays, components and technologies. The unmanned vehicles identified for this discussion are unmanned aerial vehicles, unmanned surface vessels, and unmanned ground vehicles.

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Unmanned Aerial Vehicles (UAV)

The UAV or drones are designed to fly in areas inaccessible to humans. The UAVs are equipped with sensors, actuators, light carbon bodies, rotors, peripherals, and in-flight controllers designed to automate basic flight maneuvers and modes (Emel’yanov et al., 2016). These drones are equipped with intelligent control systems that are capable of performing non-trivial, intelligent tasks essential for flight, such as planning, coalition formation, and goal prioritization. For instance, the UAVs have radio essential for wireless communication with remote control or the ground control system (GCS). A study by Saleem et al. (2015, pp.3) noted that UAVs operate on the IEEE S-Band, IEEE L-Band, and Industrial, Scientific and Medical band, as well as wireless networks. Additionally, they can operate on licensed or unlicensed spectrums without interfering with the primary users, which is intended to address the spectrum scarcity problem.

The automatic control of the UAVs is done using various software technologies. For instance, a study by Atoev et al. (2017) explored the use of ArduPilot Mega (APM) 2.8 software used to control fixed-wing aircraft and multi-rotor helicopters, including the traditional helicopters. This software is fully capable of autonomous stabilization, takeoff, and landing, and two-way telemetry and inflight commands using MAVLink (Micro Air Vehicle Link Communication) protocol. MAVLink protocol is essential in drones because it facilitates bidirectional communications between the GCS and the UAV. Messages sent between the drone and GCS have an ID that ensures they reach the intended recipient.

Latency in communication between the two channels might result in delayed control. The study by Atoev et al. (2017) noted that latency varies depending on the range of the UAV from the control room. The farther the UAV from the GCS, the higher the latency. A delayed response can have devastating impacts on the drone and people. For example, Lu et al. (2017) reported that 417 drone accidents were experienced in the United States. Therefore, strategic measures have been put in place to ensure safety when operating UAVs. For instance, drones are built to detect abnormal sounds, abnormal rotations, and infrared imaging is used to detect overheating (Lu et al., 2017). These mechanisms are implemented using algorithms, and they are meant to control the UAV in case the connection is lost. The study by Saleem et al. (2015) proposed the use of directional antenna in drones as a way of reducing signal hop and latency, which significantly reduces signal interference. It helps in maintaining communication over a long distance. The implementation of closed-loop mechanisms also boosts the stability and safety of the UAVs.

Unmanned Surface Vehicles (USV)

The USVs are equally important as UAVs because of their wide range of applications, including surveillance, spill collection, exploration task, marine survey, transportation, and placing nets, among others. However, the performance of these vessels is contingent on the effectiveness of control methods and components applied in maintaining navigation. In normal systems, the steering machine controls the rudder angle to maintain the course of the vessel. Similarly, Azzeri et al. (2015) noted that the USV system for keeping course relies on feedback from a gyrocompass that measures the heading. Several automatic systems have been in use since the 1970s when the gain scheduling adoptive autopilots were introduced. These systems usually changed the speed of the vehicles. New and robust systems were introduced that rely on feedback from controllers and artificial intelligence (Azzeri et al., 2015). These control strategies help in maintaining the direction and location of the USV.

The control systems are equipped with complex components that ensure USVs steer in the direction commanded. Klinger et al. (2016) gave an overview of WAM-V USV14, an advanced twin-hull pontoon-style vessel. The guidance, control, and navigation components of the vessel contain a single-board computer, a motherboard, inertial measurement unit, tilt-compensated digital compass, pulse-width-modulation signal generator, radio-frequency transceiver, and GPS. These components are housed in a water-resistant plastic box. The most important components on the USV are inertial measurement units, GPS, and sensors as they measure the position and orientation of the vessel and relay the information to the single-board computer that logs maneuvering data. The radio-frequency transceiver is essential in enabling the human to control the vehicle using a remote control or autonomous navigation.

Latency is a common problem in USVs, and it is influenced by the distance between human controllers and the vessel, as well as the bandwidth of the connection. Higher latency means that the signals are weak and might lead to delay in communication and commands. These delays could lead to a collision. Azzeri et al. (2015, pp.3) noted that one way of eliminating collision is to compensate for the error in position and reduce as much as possible. This approach uses various techniques such as optimal control that contains differential equations describing variable that minimizes the cost function. Adaptive and intelligent control approaches are also applied in USVs. In USV, closed-loop controllers are more preferred because they allow for the changes in the displacement and vehicle resistance linked to the recovery of the vessel payload.

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Unmanned Ground Vehicles (UGV)

UGVs are popular, especially with the continued growth of Tesla. These vehicles use sensors to localize their position within the environment. According to De Simone and Guida (2018), the data collected from the environment is passed onto the onboard controllers, where it is processed. Based on the instructions or commands assigned, the controllers manage the actuators. For example, Kaltenegger et al. (2016) indicated a race car fitted with an Arduino Uno microcontroller, which was useful in controlling the reading sensors and interpreting the data in a useful way. When data was interpreted, it was passed onto a motion controller that controlled the steering of the vehicle. These controls help in ensuring the car maintains the desired path.

The control signals might be sent from a close range or a distant location, a choice influenced by the strength of wireless communication. To facilitate communication, cameras are mounted on vehicles to help in providing the human controllers with live images of the environment (Bonadies et al., 2016). The GPS systems are used to determine the position of the vehicle, and the controllers decide the navigation goal—the quality and cost of the systems used influence the reliability of the autonomous vehicles.

There are various types of sensors used in UGVs. The Light Detection and Ranging (LIDAR) uses a rotating mirror to reflect light from objects or surfaces, which helps in determining the location of the distance between the two (Babak et al., 2017). Identifying the distance helps in determining the speed of the vehicle, and could apply brakes when the obstacle is identified. The Radar (Radio Detection and Ranging) uses electromagnetic waves to send echoes that detect and track objects. The inertial measurement unit is used in UGV to determine linear and angular accelerations (Babak et al., 2017), which is essential in improving the accuracy of GPS. The sensors are a useful way for the UGV to determine the location and avoid obstacles on the road.


Control of autonomous vehicles is crucial because it determines the safety or unsafety of the systems. These vehicles have grown over the years with the advancement in technology. As observed in this research, the three types of autonomous vehicles contain sensors, GPS, and microcontrollers/computers that are used to process the signals and determine the location and navigational objectives. Most of these vehicles also rely on wireless networks for communication. Additionally, the distance between the ground controllers and the autonomous vehicle influences the signal strength, which in turn affects the accuracy of the control commands. Finally, good closed-loop systems are viewed as effective because they reduce external disturbances and increase the accuracy of the vehicles.


Atoev, S., Kwon, K. R., Lee, S. H., & Moon, K. S. (2017). Data analysis of the MAVLink communication protocol. In 2017 International Conference on Information Science and Communications Technologies (ICISCT) (pp. 1-3). Web.

Azzeri, M. N., Adnan, F. A., & Zain, M. M. (2015). Review of course keeping control system for unmanned surface vehicle. Jurnal Teknologi, 74(5), 11-20. Web.

Babak, S. J., Hussain, S. A., Karakas, B., & Cetin, S. (2017). Control of autonomous ground vehicles: a brief technical review. IOP Conference Series: Materials Science and Engineering (Vol. 224, No. 1, p. 012029). IOP Publishing.

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Bonadies, S., Lefcourt, A., & Gadsden, S. A. (2016). A survey of unmanned ground vehicles with applications to agricultural and environmental sensing. Autonomous air and ground sensing systems for agricultural optimization and phenotyping (Vol. 9866, p. 98660Q). International Society for Optics and Photonics.

De Simone, M. C., & Guida, D. (2018). Identification and control of a Unmanned Ground Vehicle by using Arduino. UPB Sci. Bull. Ser. D, 80, 141-154. Web.

Emel’yanov, S., Makarov, D., Panov, A. I., & Yakovlev, K. (2016). Multilayer cognitive architecture for UAV control. Cognitive Systems Research, 39, 58-72. Web.

Kaltenegger, B. E., Binder, B., & Bader, M. (2016). Controlling and Tracking an Unmanned Ground Vehicle with Ackermanndrive. Proceedings of the Austrian Robotics Workshop (ARW-16), Wels, Austria.

Klinger, W. B., Bertaska, I. R., von Ellenrieder, K. D., & Dhanak, M. R. (2016). Control of an unmanned surface vehicle with uncertain displacement and drag. IEEE Journal of Oceanic Engineering, 42(2), 458-476. Web.

Lu, H., Li, Y., Mu, S., Wang, D., Kim, H., & Serikawa, S. (2017). Motor anomaly detection for unmanned aerial vehicles using reinforcement learning. IEEE Internet of Things Journal, 5(4), 2315-2322. Web.

Saleem, Y., Rehmani, M. H., & Zeadally, S. (2015). Integration of cognitive radio technology with unmanned aerial vehicles: issues, opportunities, and future research challenges. Journal of Network and Computer Applications, 50, 15-31. Web.

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