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Unmanned Aerial System applications in construction
Published in Anil Sawhney, Mike Riley, Javier Irizarry, Construction 4.0, 2020
Masoud Gheisari, Dayana Bastos Costa, Javier Irizarry
With manual operation, UAS are under control of operators on the ground via telemetry (Wen and Kang, 2014). Although this method is frequently used, it imposes limitations on maneuverability because the aircraft must always be within the operator’s line of sight. Autonomous aircraft can be used for greater maneuverability, better economy, and reduced risk. There are two forms of autonomous flight frequently employed: UAS following a fixed course set through preprogrammed GPS coordinates.UAS employing sensor techniques and closed-loop feedback.
The Integration of Unmanned Aircraft
Published in Ron Bartsch, International Aviation Law, 2018
The ICAO RPASM defines an ‘autonomous aircraft’ as ‘an unmanned aircraft that does not allow pilot intervention in the management of the flight’. The ICAO RPASM goes on to define an ‘autonomous operation’ as ‘an operation during which a remotely-piloted aircraft is operating without pilot intervention in the management of the flight’, which, significantly, does not preclude the existence of a ‘remote’ pilot.
The Technology Readiness and Acceptance Model as a Predictor of Pilots’ Willingness to Operate in UAM Integrated Airspace
Published in The International Journal of Aerospace Psychology, 2023
Lakshmi Vempati, Paul Myers, Scott R. Winter
This study provides researchers with an extended TRAM model with familiarity, perceived risk, and trust integrated to study pilot willingness to operate an aircraft in the same airspace as UAM. Airspace integration of highly autonomous aircraft poses considerable risks to mixed mode operations. It is essential that factors that could impede the process be evaluated early to implement safe, efficient, and equitable solutions for all stakeholders. The perceived risk of the new technology can often outweigh the benefits. It is imperative that research be continued to evaluate further factors such as operational factors, experience levels, certification types etc., to reduce the risk factors and facilitate the development and employment of new technology, specifically in this case, UAM aircraft.
LTL cross entropy optimisation for quadcopter task orchestration
Published in Cyber-Physical Systems, 2023
Christopher Banks, Samuel Coogan, Magnus Egerstedt
We motivate the application of Algorithm 2 with a firefighting quadcopters scenario. This scenario naturally fits within a discrete planning framework for a multi-agent system due to multiple environment constraints that need to be satisfied within a defined area (e.g. verifying safe regions, checking for water sources, etc.). In addition to this, agents may have internal constraints that need to be satisfied that can be developed as the internal transition system of an agent. The MTAC-E algorithm given a global goal, a finite automata describing the operational environment and individual internal state for a team of agents, optimally plans trajectories for a set of agents given the following problem definition. Example 2. For example, each agent may be a fire-fighting autonomous aircraft capable of collecting water, extinguishing fires and surveying goal locations. These agents are given the following global goal: ‘eventually visit and and always ensure visiting implies ’. Using LTL, this specification can be represented as .
Pilots’ Willingness to Operate in Unmanned Aircraft System Integrated Airspace
Published in The International Journal of Aerospace Psychology, 2021
Lakshmi Vempati, Scott R. Winter, Stephen Rice, Valerie Gawron, John M. Robbins
Several studies have been conducted to gain an understanding of consumers’ willingness to fly in autonomous aircraft. For example, a study conducted by Vance and Malik (2015) investigated decisions that influence passengers’ willingness to fly in autonomous passenger airlines and identified key factors that had a strong positive and negative influence. Likewise, in a separate study, Rice et al. (2015) identified seven predictors of willingness to fly in autonomous aircraft – age, education, familiarity, fear, fun factor, happiness, and wariness. In general, factors that influence willingness to fly vary by circumstance and other elements, such as technology. Global events can also have a strong impact in the near term.