The LSTS toolchain for networked vehicle systems

Pinto, José; Dias, Paulo Sousa; Martins, Ricardo; Fortuna, João; Marques, Eduardo Fernandes; Sousa, João Borges de

doi:10.1109/oceans-bergen.2013.6608148

Cited by 122 publications

(80 citation statements)

References 4 publications

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“…In addition ADS-B and AIS receivers were included into the system to visualize position of manned vessels and planes in the area of operation. Having such baseline, a LSTS software Toolchain was selected for a role of the common software platform for SA [18]. The toolset allows to visualize vehicles positions and disseminate that information among involved parties both locally and over the Internet.…”

Section: Iiia Telemetrymentioning

confidence: 99%

Coordinated maritime missions of unmanned vehicles — Network architecture and performance analysis

Zolich

Sogrov

Vagsholm

et al. 2017

2017 IEEE International Conference on Communications (ICC)

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Abstract-Multi-vehicle operations using various types of unmanned vehicles (UVs) can increase efficiency of marine data acquisition, reduce the crew risk and lower mission costs. These types of missions are very complex and often involve systems that are not interoperable. From an operational perspective however, some level of integration is necessary. Typically, a common network system architecture and Situation Awareness (SA) platform are required. The architecture allows operators to transfer data between vehicles and their operators, while the SA platform allows to monitor mission progress and react to changes. This paper presents a network system architecture used during an experiment realized in Spring 2016 in Norway. 8 departments from 5 institutions worked together to combine operation of 4 UVs (aerial, surface, underwater), a support vessel and onshore team. The description is followed by a backbone network performance analysis. Several cases are presented, with focus on a transmission between manned vessel and Unmanned Surface Vehicle (USV), including direct connection, and data-relay mechanism via Unmanned Aerial Vehicle (UAV).

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Section: Iiia Telemetrymentioning

confidence: 99%

Coordinated maritime missions of unmanned vehicles — Network architecture and performance analysis

Zolich

Sogrov

Vagsholm

et al. 2017

2017 IEEE International Conference on Communications (ICC)

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“…For more information about the LSTS Toolchain, see e.g. Pinto et al (2012), Pinto et al (2013). For a more detailed description of the architecture for the unmanned aerial system, see Zolich et al (2015).…”

Section: The Lsts Toolchainmentioning

confidence: 99%

Approach Methods for Autonomous Precision Aerial Drop from a Small Unmanned Aerial Vehicle

2017

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Abstract:One of the many basic operations that a fixed-wing unmanned aerial vehicle (UAV) should master autonomously is to deliver an object to a precise position. Among several possible delivery approaches, this paper chooses to focus on releasing the object from the UAV, at a carefully calculated state, and let it fall freely to the selected landing location. In such an operation, the computation of suitable release positions, velocity and attitude is essential. Due to time-varying disturbances like wind, a previously calculated release state may not be suitable once it has been reached by the aircraft. On account of these challenges, this paper suggests a dynamic calculation of the release state with respect to the wind velocity and current state of the UAV, with a sufficient re-optimization frequency. This is tested in simulation and with field tests, and compared with results from a static calculation approach.

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“…To communicate with the autopilot, the messages travel between MATLAB and the ArduPilot though DUNE 4 [12], which is an open source robot framework developed by the Underwater Systems and Technology Laboratory (LSTS) of the University of Porto. Inter-Module Communication Protocol (IMC 5 ) is then used for the messages between MATLAB and DUNE using the IMC Java library.…”

Section: Ardupilotmentioning

confidence: 99%

Autonomous UAV surveillance of a ship's path with MPC for maritime situational awareness

Andrade

Storvold

Johansen

2017

2017 International Conference on Unmanned Aircraft Systems (ICUAS)

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Abstract-Maritime Situational Awareness is crucial in maritime operations to identify threats and to deal with them as soon as possible. These threats can be pirates in shipping operations, icebergs when sailing in the northern sea routes, or even unknown vessels or objects that might be on the ship's path. A solution to identify these threats is the use of UAV's to overfly the ship's planned path. This solution is described in this paper, using an autonomous fixed wing UAV. Based on the provided ship's planned path, the UAV should autonomously map the area close to the ship track. To do that, an optimization problem is solved using Model Predictive Control, where the turn rate for the next time period is optimized. Based on the turn rate, the future path of the UAV is calculated and the waypoints are sent to the autopilot. This application is thoroughly tested using a Software In Loop environment, where an aircraft model is used with the autopilot's simulation. The results show that surveillance performance is improved if the UAV has information about the ship's velocity in addition to position.

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The LSTS toolchain for networked vehicle systems

Cited by 122 publications

References 4 publications

Coordinated maritime missions of unmanned vehicles — Network architecture and performance analysis

Coordinated maritime missions of unmanned vehicles — Network architecture and performance analysis

Approach Methods for Autonomous Precision Aerial Drop from a Small Unmanned Aerial Vehicle

Autonomous UAV surveillance of a ship's path with MPC for maritime situational awareness

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