Development and Experimental Tests of a ROS Multi-agent Structure for Autonomous Surface Vehicles

Conte, G.; Scaradozzi, David; Mannocchi, Daniele; Raspa, Paolo; Panebianco, Luca; Screpanti, Laura

doi:10.1007/s10846-017-0700-9

Cited by 18 publications

(16 citation statements)

References 13 publications

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“…In order to implement this system, some frameworks for swarm control were suggested and designed [37], such as Robotic Operating System (ROS) [38,39]. ROS is a metaoperating system that can run on one or more computers, and it provides several features: hardware abstraction, low-level device control, implementation of commonly used features, transmission of messages between the processes, and the management of the installed packages [40,41]. In other words, ROS is a server-client able to provide an implementation of distributed artificial intelligence algorithms inside the swarm; therefore it will take all the team decisions related to the swarm's actions [42,43].…”

Section: Related Workmentioning

confidence: 99%

Toward a robot swarm protecting a group of migrants

Vaidis

Otis

2020

Intel Serv Robotics

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Different geopolitical conflicts of recent years have led to mass migration of several civilian populations. These migrations take place in militarized zones, indicating real danger contexts for the populations. Indeed, civilians are increasingly targeted during military assaults. Defense and security needs have increased; therefore, there is a need to prioritize the protection of migrants. Very few or no arrangements are available to manage the scale of displacement and the protection of civilians during migration. In order to increase their security during mass migration in an inhospitable territory, this article proposes an assistive system using a team of mobile robots, labeled a rover swarm that is able to provide safety area around the migrants. We suggest a coordination algorithm including CNN and fuzzy logic that allows the swarm to synchronize their movements and provide better sensor coverage of the environment. Implementation is carried out using on a reduced scale rover to enable evaluation of the functionalities of the suggested software architecture and algorithms. Results bring new perspectives to helping and protecting migrants with a swarm that evolves in a complex and dynamic environment.

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Section: Related Workmentioning

confidence: 99%

Toward a robot swarm protecting a group of migrants

Vaidis

Otis

2020

Intel Serv Robotics

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“…Notice that the subsystem division relates mainly to the specific tasks or jobs performed by the hardware. Below this subsystems level, the software components were designed to be organized according to a multi-agent system (MAS) architecture, largely based on the methods detailed in the work of [14]. Using a MAS approach was considered the most convenient option, as it essentially introduces an abstraction layer, which allows optimizing modularity within the system.…”

Section: Hardware Description and Integrationmentioning

confidence: 99%

“…Then, in the case of failure of an agent, as each one presents its own state and decision-making capabilities, it will decide individually how to proceed after the error. An effective demonstration of how to use these capabilities to produce a robotics MAS using the ROS framework [13] can be seen in the work of [14]. In said work, MAS methods described in the work of [15] are used to produce a full solution to manage a system composed of an underwater surveying vehicle deployed from a surface vehicle, all controlled from a ground base station.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of a Virtual Sensor Network for Smart Waste Water Monitoring

Guerra

Bolea

Gámiz³

et al. 2020

Sensors

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Monitoring and analysis of open air basins is a critical task in waste water plant management. These tasks generally require sampling waters at several hard to access points, be it real time with multiparametric sensor probes, or retrieving water samples. Full automation of these processes would require deploying hundreds (if not thousands) of fixed sensors, unless the sensors can be translated. This work proposes the utilization of robotized unmanned aerial vehicle (UAV) platforms to work as a virtual high density sensor network, which could analyze in real time or capture samples depending on the robotic UAV equipment. To check the validity of the concept, an instance of the robotized UAV platform has been fully designed and implemented. A multi-agent system approach has been used (implemented over a Robot Operating System, ROS, middleware layer) to define a software architecture able to deal with the different problems, optimizing modularity of the software; in terms of hardware, the UAV platform has been designed and built, as a sample capturing probe. A description on the main features of the multi-agent system proposed, its architecture, and the behavior of several components is discussed. The experimental validation and performance evaluation of the system components has been performed independently for the sake of safety: autonomous flight performance has been tested on-site; the accuracy of the localization technologies deemed as deployable options has been evaluated in controlled flights; and the viability of the sample capture device designed and built has been experimentally tested.

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“…In addition, in 2017, Klinger et al evaluated the intelligent controller performance through a USV experiment [7]. In 2018, Conte et al developed a ROS of multi-agent structure for the USV, and tested its path planning [8]. Dai et al combined many methods to achieve the UAV formation control [9] and so on.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Navigating Control Based on the Parallel Action-Network ADHDP Method for Unmanned Surface Vessel

Huang

Liu

Wen

et al. 2019

Advances in Materials Science and Engineering

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The feedback PID method was mainly used for the navigating control of an unmanned surface vessel (USV). However, when the intelligent control era is coming now, the USV can be navigated more effectively. According to the USV character in its navigating control, this paper presents a parallel action-network ADHDP method. This method connects an adaptive controller parallel to the action network of the ADHDP. The adaptive controller adopts a RBF neural network approximation based on the Lyapunov stability analysis to ensure the system stability. The simulation results show that the parallel action-network ADHDP method has an adaptive control character and can navigate the USV more accurately and rapidly. In addition, this method can also eliminate the overshoot of the ADHDP controller when navigating the USV in various situations. Therefore, the adaptive stability design can greatly improve the navigating control and effectively overcome the ADHDP algorithm limitation. Thus, this adaptive control can be one of the intelligent ADHDP control methods. Furthermore, this method will be a foundation for the development of an intelligent USV controller.

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Development and Experimental Tests of a ROS Multi-agent Structure for Autonomous Surface Vehicles

Cited by 18 publications

References 13 publications

Toward a robot swarm protecting a group of migrants

Toward a robot swarm protecting a group of migrants

Design and Implementation of a Virtual Sensor Network for Smart Waste Water Monitoring

Adaptive Navigating Control Based on the Parallel Action-Network ADHDP Method for Unmanned Surface Vessel

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