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The impact of robotics and autonomous systems (RAS) across the conflict spectrum
Published in Ash Rossiter, Robotics, Autonomous Systems and Contemporary International Security, 2020
Many advanced economies have been converting their expertise in private-sector robotics into new military investments for some years. Operationally, military robotic systems have been used primarily in missions where they substitute for inhabited platforms. Many states have used UAVs, for example, in the place of manned aircraft for both surveillance and strike missions. In a similar vein, the US extensively used unmanned ground vehicles (UGVs) in Iraq and Afghanistan (and is still doing so) in EOD (explosive ordinance disposal) roles. To summarize the main motive behind this substitution would be to say that military robotics are a way of putting the focus of warfare closer to the enemy but at the same farther from oneself.7 Or, to put another way, it is the desire to place metal before flesh.
Understanding Human-Machine Teaming through Interdependence Analysis
Published in Michael D. McNeese, Eduardo Salas, Mica R. Endsley, Contemporary Research, 2020
Matthew Johnson, Micael Vignatti, Daniel Duran
The columns in each alternative can represent specific individuals (existing or planned). If the team has more than two members then additional columns can be used, as represented by columns A, B, C, and D in Figure 9.1. For larger teams this can become unwieldy, but categories and roles can be used to keep it manageable. For example, consider a single operator managing four unmanned aerial vehicles (UAVs) and two unmanned ground vehicles (UGVs). Assuming the vehicles are of the same type then categories can be used. The team alternative would be three columns: one human operator, one for UAVs, and one for UGVs. Multiple people are also permitted and can be simplified with roles. Consider extending the previous example to be two such units being managed by a commander. These 15 entities can be captured by four columns: commander, operator, UAV, UGV. There is a limit to the feasibility of extending the table to large teams, but such teams are more like organizations than teams.
A formation cooperative reconnaissance strategy for multi-UGVs in partially unknown environment
Published in Journal of the Chinese Institute of Engineers, 2023
Haojie Zhang, Tiantian Yang, Zhibao Su
UGV is a smart vehicle capable of doing tasks without the need of a human operator. As improving the automation level and vehicle intelligence, it is widely used in military and civilian fields. In recent years, UGV has been deployed to work in hazardous or unpleasant conditions, or carry out tasks which are too risky, difficult, or dull for humans. For example, it is assumed to assist combatants or participate in combat independently in the US Army’s Future Combat Systems program (Czapla and Wrona 2013). In this case, the human soldiers will be replaced in performing monotonous, boring, and dirty missions. However, it may be impossible to be completed by relying on a single UGV for some more complex missions. As compared to a single UGV, the usage of multi-UGVs has many advantages. When multi-UGVs are involved, the task can be decomposed into several sub-tasks that can be handled simultaneously. Therefore, the mission can be achieved much faster than a single UGV, resulting in time reduction of mission execution (Gautam and Mohan 2012). Due to these advantages, multi-UGVs are often applied in complex tactical missions, such as surveillance (Acevedo et al. 2014), search and exploration (Nieto-Granda, Rogers, and Christensen 2014), cooperative reconnaissance (Saptharishi et al. 2002), environmental monitoring (Dunbabin and Marques 2012) and cooperative manipulation (Prasad, Sharma, and Vanualailai 2016). During mission execution, UGVs are required to travel autonomously between different locations and avoid collision of obstacles, which is of great significance to perform the missions more efficiently.
SARSA in extended Kalman Filter for complex urban environments positioning
Published in International Journal of Systems Science, 2021
Chen Chen, Xiang Wu, Yuming Bo, Yuwei Chen, Yurong Liu, Fuad E. Alsaadi
GNSS is a powerful and efficient tool for positioning due to its automation, low cost, and high precision in the open sky environments where the signals are Line-of-Sight (LOS), which means the signals can be received directly without any obstructions. Hence, it is widely used on Unmanned Ground Vehicle (UGV), Unmanned Aerial Vehicle (UAV) and robots (Mu et al., 2017). However, GNSS signals are susceptible to interference. They are usually blocked or reflected due to the obstructions like buildings, trees, and tunnels in complex urban environments. In this condition, the GNSS signals are Non-Line-of-Sight (NLOS) since there are obstructions between the satellite and the receiver (Suzuki et al., 2020; Xu et al., 2020). NLOS signals are considerably attenuated due to the reflection, diffraction, and the loss of penetrating certain obstructions. Besides the signal attenuation, the multipath (MP) transmissions of the NLOS signals will lead to the fading and phase shift of the final received signal (Kubo et al., 2020). As a result, the positioning accuracy of GNSS is not guaranteed in GNSS degraded or denied environments (e.g. urban canyon, dense forest, indoor) (Jiang et al., 2020a; Jiang, Chen, et al., 2020).