Print Сite this

Artificial Intelligence. Unmanned Mission Communications

Introduction

The growth of human knowledge in the field of Artificial Intelligence (AI) has led to advancement in the development and deployment of unmanned autonomous systems. There are multiple examples of unmanned autonomous systems and perform a wide range of activities depending on the medium of operation. Some examples include unmanned aerial vehicles, marine robots, unmanned vehicles, service robots, intelligent plants, and space robots (Zhang et al., 2017). These systems are complex as they are created using various technologies related to communication, control, mechanics, and materials (Zhang et al., 2017). The unmanned systems are used in monitoring, collecting data, and helping during the time of disaster or exploring areas that are difficult for a human to visit. Communication networks are essential in facilitating the operations of autonomous systems.

We will write a
custom essay
specifically for you

for only $16.05 $11/page
308 certified writers online
Learn More

The unmanned mission systems are designed using different components based on their desired operations. However, the main components found in most unmanned systems include GPS, sensors, wireless sensor networks, network gateways, data storage and processing, ground control stations, and on-ground robots (Jawhar et al., 2017). Ground control stations are used to remotely monitor unmanned vehicles and provide instructions on areas to explore. Unmanned vehicles collect data using sonar, radar, and environment sensing devices, which are transmitted to the designated ground control using satellite communication systems (Zhou et al., 2015). These components facilitate communications between system-to-system and system-to-infrastructure.

Unmanned Mission Communication Specifics

The differences in physical characteristics of water, earth, and air mean that the communication between the two media varies. Unmanned surface vehicles operate on water. The communication systems for these systems include the wireless communication that transmits to the ground control stations as well as other vehicles around, onboard wired/wireless communications, actuators, GPS, sonars, cameras, and sensors. The unmanned surface vehicles use acoustics communication systems or radiofrequency or wireless networks depending on the transmission distance (Suming & Weicheng, 2019). Acoustics waves are the main carrier for underwater wireless communication because of their low absorption and cover a long distance up to tens of kilometers and low frequency of 10Hz-1MHz (Kaushal & Kaddoum, 2016; Suming & Weicheng, 2019). The acoustic waves are characterized by high latency, time-varying multipath propagation, and frequency-dependent attenuation (Kaushal & Kaddoum, 2016). The multipath propagation leads to a delay in the spread for about 10ms, but it can go up to 100ms. The propagation is usually caused by the reflection, refraction, or interruption from atmospheric ducts, which significantly affects the quality and the speed of communication.

The unmanned surface vehicles also transmit data using radiofrequency. The radio frequency transmission is faster compared to acoustic waves. However, the challenge with the former is higher frequency making the distance of transmission shorter. Therefore, considerations are made between the speed and the range of transmission. According to Kaushal and Kaddoum (2016), radiofrequency waves underwater have a range of tens of Hz to GHz and electromagnetic waves of 30 Hz to 300 Hz. The analysis shows they can propagate up to a distance of 100 m. Possible propagation losses in underwater include refraction and absorption estimated at 60 dB and 27dB respectively (Kaushal & Kaddoum, 2016). In water communication, the longer the propagation path, the higher the path loss. This phenomenon happens because as the distance increases, the signal strengths fade because of absorption and reflection. As a result, communication is very poor, especially when the frequency is high as is the case in radiofrequency.

Communications in unmanned aerial systems are characterized by unique channels compared to autonomous surface vehicles and satellite systems. First, the aerial systems’ communication channels include air-to-air and air-to-ground propagation influenced by the velocity of the vehicle (Khuwaja et al., 2018). Second, unmanned aerial vehicles have excessive spatial and temporal variations that are caused by the changes in unmanned vehicle movements relative to the ground operators. Airframe shadowing interference occurs because of the structural design and rotation of the aerial vehicle (Khuwaja et al., 2018). These unmanned aerial vehicles also make them different from satellite communication because the movements of the latter can increase problems through severe non-stationarity. Additionally, the cost of operating unmanned aerial vehicles is lower compared to the satellites.

The unmanned aerial vehicle propagation channel depends on the antenna orientation, operational environment, channel sounding process, and the propagation scenario. The propagation channel is categorized into two sections. First is payload communications that are categorized further into narrowband or wideband (Khawaja et al., 2019). The narrowband continuous wave signals generate pilot tones at a single carrier creating Doppler effects frequency shift (Khuwaja et al., 2018). On the other hand, wideband measurement systems influence channel impulse response such as the delay spread. Control and non-payload communications are essential for the control of unmanned aerial vehicles. Khawaja et al. (2019) noted that payload aerial communications use unlicensed bands ranging from 900 MHz, 2.4 GHz to 5.8 GHz. The differences in the two mean that narrowband is appropriate for the computation of non-selective fading partners, whereas wideband is applicable in fading channels.

The propagation loss and time delays in unmanned aerial vehicles are influenced by the environment and altitude of the vehicle. In open space or flat terrain, the channel characteristics are influenced by the presence of the buildings. Size, height, and the densities of these buildings determine the strength and obstruction of the signal. Hilly and mountainous regions also disrupt signals through diffraction and reflection forcing the signals to disperse in other areas (Khawaja et al., 2019). Transmission over water surfaces might alter the propagation path because of the diffraction and reflection of the signals. The distance between the unmanned aerial vehicle and the stationary receiver such as the ground control also influences the propagation channels. Longer distances lead to poor signal strength, thus increasing latency and decreasing expected data transmission rate or performance.

Get your
100% original paper
on any topic

done in as little as
3 hours
Learn More

Autonomous vehicles incorporate rotating lasers, cameras, sonar, sensors, and radar to capture information making them aware of their surroundings (Collingwood, 2017). Furthermore, intelligent vehicle systems require cellular or wireless connectivity to share and communicate with other vehicles on real-time traffic updates and hazardous conditions on the road. However, in times of disaster, Internet infrastructure might be destroyed, forcing the unmanned ground vehicles to use rely on mobile ad-hoc communication strategies. For instance, Baumgärtner et al. (2017) suggested the use of mobile cloud-based disruption-tolerant networking essential for messaging and sharing applications. Additionally, the authors suggested the use of a highly adaptive end-to-end communication protocol that finds and exploits communication bridges.

Unmanned ground vehicles can also communicate in short distances using the antenna. Electromagnetic waves are transmitted to an antenna using available wireless networks. There exist path loss because of the reduction in signal strength from the transmitting antenna to the receiving device. The path loss in unmanned ground vehicles is determined using empirical, stochastic, and deterministic models (Bhandari et al., 2016). In unmanned ground systems, the propagation channels systems are influenced by atmospheric absorption, feeder loss, antenna misalignment loss, polarization loss, multipath loss, and diffraction (Bhandari et al., 2016). As a result, the path loss varies in different environments.

In unmanned ground systems, time delays occur for several reasons. First of all, time delays are defined as the latency between when a controller issues a command and receives correct feedback from the vehicle (Lu et al., 2019). Latency occurs because of the limitation on signal transmission speeds and bandwidth. The delays result in negative human performance when controlling the system, which further leads to a loss in productivity. For instance, time delays of 1.5s result in poor performance because they pile up towards the completion time of a project (Lu et al., 2019). Using delay compensation methods is essential in eliminating errors on unmanned vehicles.

Conclusion

The three unmanned systems analyzed in this paper include aqua (surface), aerial, and ground vehicles. These unmanned systems have similar components, such as GPS systems, sensors, actuators, cameras, and wired/wireless networks essential for communicating with other vehicles as well as operators. Propagation channels in the identified mediums are different in that each system transmits information using defined standards. However, path losses in these systems were influenced by almost similar factors, especially in the environment. Areas with tall and multiple buildings had higher latency because of the reflection and diffraction that resulted in signal loss, leading to a high error rate and mediocre performance.

References

Baumgärtner, L., Kohlbrecher, S., Euler, J., Ritter, T., Stute, M., Meurisch, C.,… & Freisleben, B. (2017). Emergency communication in challenged environments via unmanned ground and aerial vehicles. In 2017 IEEE Global Humanitarian Technology Conference (GHTC) (pp. 1-9). Doi: 10.1109/GHTC.2017.8239244

Bhandari, R., Khati, V., Yadav, S. & Kumari, M. (2016). Wireless network for unmanned ground vehicle. International Journal of Research in Advance Engineering, 2(3). Web.

Collingwood, L. (2017). Privacy implications and liability issues of autonomous vehicles. Information & Communications Technology Law, 26(1), 32-45. Doi: 10.1080/13600834.2017.1269871

We will write a custom
essays
specifically
for you!
Get your first paper with
15% OFF
Learn More

Jawhar, I., Mohamed, N., Al-Jaroodi, J., Agrawal, D. P., & Zhang, S. (2017). Communication and networking of UAV-based systems: Classification and associated architectures. Journal of Network and Computer Applications, 84, 93-108. Doi: 10.1016/j.jnca.2017.02.008

Kaushal, H., & Kaddoum, G. (2016). Underwater optical wireless communication. IEEE Access, 4, 1518-1547. Doi: 10.1109/ACCESS.2016.2552538

Khawaja, W., Guvenc, I., Matolak, D. W., Fiebig, U. C., & Schneckenburger, N. (2019). A survey of air-to-ground propagation channel modeling for unmanned aerial vehicles. IEEE Communications Surveys & Tutorials, 21(3), 2361-2391.

Khuwaja, A. A., Chen, Y., Zhao, N., Alouini, M. S., & Dobbins, P. (2018). A survey of channel modeling for UAV communications. IEEE Communications Surveys & Tutorials, 20(4), 2804-2821. Doi: 10.1109/COMST.2018.2856587

Lu, S., Zhang, M. Y., Ersal, T., & Yang, X. J. (2019). Workload management in teleoperation of unmanned ground vehicles: Effects of a delay compensation aid on human operators’ workload and teleoperation performance. International Journal of Human-Computer Interaction, 35(19), 1820-1830. Doi: 10.1080/10447318.2019.1574059

Suming, Q. & Weicheng, C. (2019). An overview on aquatic unmanned aerial vehicles. Annals of Reviews and Research, 5(3). Doi: 10.19080/ARR.2019.05.555663

Zhang, T., Li, Q., Zhang, C. S., Liang, H. W., Li, P., Wang, T. M.,… & Wu, C. (2017). Current trends in the development of intelligent unmanned autonomous systems. Frontiers of Information Technology & Electronic Engineering, 18(1), 68-85. Doi: 10.1631/FITEE.1601650

Zhou, Y., Cheng, N., Lu, N., & Shen, X. S. (2015). Multi-UAV-aided networks: Aerial-ground cooperative vehicular networking architecture. IEEE Vehicular Technology Magazine, 10(4), 36-44. Doi: 10.1109/MVT.2015.2481560

Need a
100% original paper
written from scratch

by professional
specifically for you?
308 certified writers online
Learn More

Cite this paper

Select style

Reference

StudyCorgi. (2022, March 22). Artificial Intelligence. Unmanned Mission Communications. Retrieved from https://studycorgi.com/artificial-intelligence-unmanned-mission-communications/

Reference

StudyCorgi. (2022, March 22). Artificial Intelligence. Unmanned Mission Communications. https://studycorgi.com/artificial-intelligence-unmanned-mission-communications/

Work Cited

"Artificial Intelligence. Unmanned Mission Communications." StudyCorgi, 22 Mar. 2022, studycorgi.com/artificial-intelligence-unmanned-mission-communications/.

* Hyperlink the URL after pasting it to your document

1. StudyCorgi. "Artificial Intelligence. Unmanned Mission Communications." March 22, 2022. https://studycorgi.com/artificial-intelligence-unmanned-mission-communications/.


Bibliography


StudyCorgi. "Artificial Intelligence. Unmanned Mission Communications." March 22, 2022. https://studycorgi.com/artificial-intelligence-unmanned-mission-communications/.

References

StudyCorgi. 2022. "Artificial Intelligence. Unmanned Mission Communications." March 22, 2022. https://studycorgi.com/artificial-intelligence-unmanned-mission-communications/.

References

StudyCorgi. (2022) 'Artificial Intelligence. Unmanned Mission Communications'. 22 March.

This paper was written and submitted to our database by a student to assist your with your own studies. You are free to use it to write your own assignment, however you must reference it properly.

If you are the original creator of this paper and no longer wish to have it published on StudyCorgi, request the removal.