A Multi-UAS Cooperative Mission Over Non-Segregated Civil Areas

Boccalatte, Marco; Brogi, F.; Catalfamo, F.; Maddaluno, Stefania; Martino, Michele Di; Mellano, Valter; Prin, Paolo Rosazza; Solitro, F.; Torasso, Pietro; Torta, Gianluca

doi:10.1007/s10846-012-9706-5

Cited by 6 publications

(10 citation statements)

References 12 publications

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“…The adaptation can involve extension of the GCS functionalities to video stream handling, sensor data visualization, control over auxiliary onboard devices, cooperation with terrestrial system. Future work may also be based on the conception of multi UAV systems, extending the capabilities of the presented ground control station for monitoring and controlling several of them (Boccalatte et al, 2013;Zheng et al, 2004;Dydek et al, 2013;Garcia et al, 2010;Sahingoz et al 2014). The maximum number of different UAVs that can be supervised is limited by the operators workload capabilities and the design of ground control station.…”

Section: Discussionmentioning

confidence: 99%

A Ground Control Station for the UAV Flight Simulator

Romaniuk

Gosiewski

Ambroziak

2016

Acta Mechanica Et Automatica

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In the paper implementation of a ground control station for UAV flight simulator is shown. The ground control station software is in cooperation with flight simulator, displaying various aircraft flight parameters. The software is programmed in C++ language and utilizes the windows forms for implementing graphical content. One of the main aims of the design of the application was to simplify the interface, simultaneously maintaining the functionality and the eligibility. A mission can be planned and monitored using the implemented map control supported by waypoint list.

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Section: Discussionmentioning

confidence: 99%

A Ground Control Station for the UAV Flight Simulator

Romaniuk

Gosiewski

Ambroziak

2016

Acta Mechanica Et Automatica

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“…Multi-UAV operations, characterized by the control of multiple UAVs by a centralized system comprising one or multiple human operators, have emerged as a topic of interest. Multi-UAV technology has the potential to bring about a paradigm shift in current UAV operations by offering a myriad of new operational possibilities in terms of mission and aircraft design, leading to enhanced operational safety, efficiency, and effectiveness [1,2]. Novel algorithms, frameworks, and systems are being conceived, and the implementation of these concepts will in turn drive the adoption of higher levels of automation and autonomy to support such an operational shift [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

“…To support the implementation of adaptive automation in aerospace systems, the concept of cognitive human-machine interfaces and interactions (CHMI 2 ) had been previously proposed by the authors, revolving around closed-loop human-machine systems that sense and adapt to the human operator's cognitive states. In this paper, the CHMI 2 concept is applied to investigate the use of adaptative automation in a one-to-many (OTM) UAS in a bushfire detection scenario, where a single human operator is responsible for coordinating multiple UAV platforms to search for and localize bushfires over a large area.…”

Section: Introductionmentioning

confidence: 99%

“…The paper presents the mission design and concept of operations (CONOPS), along with the system implementation and an overview of the associated HMI functionality. Preliminary results from the human-in-the-loop (HITL) evaluation activities are also presented, focusing on the offline training of the operator cognitive model in the CHMI 2 system, as well as the use of the model to drive online adaptation of the automation levels in system functions.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Human-Robot Interactions for Multiple Unmanned Aerial Vehicles

et al. 2021

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Advances in unmanned aircraft systems (UAS) have paved the way for progressively higher levels of intelligence and autonomy, supporting new modes of operation, such as the one-to-many (OTM) concept, where a single human operator is responsible for monitoring and coordinating the tasks of multiple unmanned aerial vehicles (UAVs). This paper presents the development and evaluation of cognitive human-machine interfaces and interactions (CHMI2) supporting adaptive automation in OTM applications. A CHMI2 system comprises a network of neurophysiological sensors and machine-learning based models for inferring user cognitive states, as well as the adaptation engine containing a set of transition logics for control/display functions and discrete autonomy levels. Models of the user’s cognitive states are trained on past performance and neurophysiological data during an offline calibration phase, and subsequently used in the online adaptation phase for real-time inference of these cognitive states. To investigate adaptive automation in OTM applications, a scenario involving bushfire detection was developed where a single human operator is responsible for tasking multiple UAV platforms to search for and localize bushfires over a wide area. We present the architecture and design of the UAS simulation environment that was developed, together with various human-machine interface (HMI) formats and functions, to evaluate the CHMI2 system’s feasibility through human-in-the-loop (HITL) experiments. The CHMI2 module was subsequently integrated into the simulation environment, providing the sensing, inference, and adaptation capabilities needed to realise adaptive automation. HITL experiments were performed to verify the CHMI2 module’s functionalities in the offline calibration and online adaptation phases. In particular, results from the online adaptation phase showed that the system was able to support real-time inference and human-machine interface and interaction (HMI2) adaptation. However, the accuracy of the inferred workload was variable across the different participants (with a root mean squared error (RMSE) ranging from 0.2 to 0.6), partly due to the reduced number of neurophysiological features available as real-time inputs and also due to limited training stages in the offline calibration phase. To improve the performance of the system, future work will investigate the use of alternative machine learning techniques, additional neurophysiological input features, and a more extensive training stage.

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“…Furthermore, life-cycle costs of Unmanned Air Systems (UAS) are lower than those of comparable manned aircrafts [1]. …”

Section: Introductionmentioning

confidence: 99%

The Exploitation of Data from Remote and Human Sensors for Environment Monitoring in the SMAT Project

Meo

Roglia

Bottino

2012

Sensors

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In this paper, we outline the functionalities of a system that integrates and controls a fleet of Unmanned Aircraft Vehicles (UAVs). UAVs have a set of payload sensors employed for territorial surveillance, whose outputs are stored in the system and analysed by the data exploitation functions at different levels. In particular, we detail the second level data exploitation function whose aim is to improve the sensors data interpretation in the post-mission activities. It is concerned with the mosaicking of the aerial images and the cartography enrichment by human sensors—the social media users. We also describe the software architecture for the development of a mash-up (the integration of information and functionalities coming from the Web) and the possibility of using human sensors in the monitoring of the territory, a field in which, traditionally, the involved sensors were only the hardware ones.

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A Multi-UAS Cooperative Mission Over Non-Segregated Civil Areas

Abstract: Abstract-

Cited by 6 publications

References 12 publications

A Ground Control Station for the UAV Flight Simulator

A Ground Control Station for the UAV Flight Simulator

Adaptive Human-Robot Interactions for Multiple Unmanned Aerial Vehicles

The Exploitation of Data from Remote and Human Sensors for Environment Monitoring in the SMAT Project

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