Implementation of a Control Architecture for Networked Vehicle Systems

Pinto, José; Calado, Pedro; Braga, Juan C.; Dias, Paulo Sousa; Martins, Ricardo; Marques, Eduardo Fernandes; Sousa, João Borges de

doi:10.3182/20120410-3-pt-4028.00018

Cited by 38 publications

(22 citation statements)

References 4 publications

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“…An interface that directly couples the SINMOD model provides 3D ocean data that are used to simulate sensor readings, reproducing actual ocean conditions at a given time, depth, and location. The Unified Navigation Environment in DUNE (Pinto et al., ), handles the AUV simulation, and is used for navigation, control, vehicle supervision, communication, and interaction with actuators—DUNE is running onboard the AUV as well. On top of the hierarchy sits the autonomous agent architecture T‐REX (Teleo‐Reactive EXecutive), which synthesizes plans and uses an artificial intelligence based automated planning/execution temporal framework to execute tasks continuously, as sensing and control data are fed to it.…”

Section: Methodsmentioning

confidence: 99%

Information‐driven robotic sampling in the coastal ocean

Fossum

Eidsvik

Ellingsen

et al. 2018

Journal of Field Robotics

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Efficient sampling of coastal ocean processes, especially mechanisms such as upwelling and internal waves and their influence on primary production, is critical for understanding our changing oceans. Coupling robotic sampling with ocean models provides an effective approach to adaptively sample such features. We present methods that capitalize on information from ocean models and in situ measurements, using Gaussian process modeling and objective functions, allowing sampling efforts to be concentrated to regions with high scientific interest. We demonstrate how to combine and correlate marine data from autonomous underwater vehicles, model forecasts, remote sensing satellite, buoy, and ship‐based measurements, as a means to cross‐validate and improve ocean model accuracy, in addition to resolving upper water‐column interactions. Our work is focused on the west coast of Mid‐Norway where significant influx of Atlantic Water produces a rich and complex physical–biological coupling, which is hard to measure and characterize due to the harsh environmental conditions. Results from both simulation and full‐scale sea trials are presented.

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Section: Methodsmentioning

confidence: 99%

Information‐driven robotic sampling in the coastal ocean

Fossum

Eidsvik

Ellingsen

et al. 2018

Journal of Field Robotics

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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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“…Not only can all teams see requests and updates, but they can use multiple pathways to receive the same information, leading to a robust and reliable communication mechanism. c) DUNE: The Uniform Navigation Environment (DUNE) 2 [11] is the embedded software used onboard 1 https://github.com/LSTS/imcour unmanned vehicles, data loggers and MANTA gateways. It is composed of independent tasks that communicate by broadcasting and subscribing to IMC messages.…”

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confidence: 99%

“…The main user interfaces are the Operator Console and the Mission Revision and Analysis tool [11]. Fig.…”

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confidence: 99%

Coordinating UAVs and AUVs for oceanographic field experiments: Challenges and lessons learned

Faria

Pinto

et al. 2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-Obtaining synoptic observations of dynamic ocean phenomena such as fronts, eddies, oxygen minimum zones and blooms has been challenging primarily due to the large spatial scales involved. Traditional methods of observation with manned ships are expensive and, unless the vessel can survey at high-speed, unrealistic. Autonomous underwater vehicles (AUVs) are robotic platforms that have been making steady gains in sampling capabilities and impacting oceanographic observations especially in coastal areas. However, their reach is still limited by operating constraints related to their energy sources. Unmanned aerial vehicles (UAVs) recently introduced in coastal and polar oceanographic experiments have added to the mix in observation strategy and methods. They offer a tantalizing opportunity to bridge such scales in operational oceanography by coordinating with AUVs in the water-column to get in-situ measurements. In this paper, we articulate the principal challenges in operating UAVs with AUVs making synoptic observations for such targeted watercolumn sampling. We do so in the context of autonomous control and operation for networked robotics and describe novel experiments while articulating the key challenges and lessons learned.

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Implementation of a Control Architecture for Networked Vehicle Systems

Cited by 38 publications

References 4 publications

Information‐driven robotic sampling in the coastal ocean

Information‐driven robotic sampling in the coastal ocean

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

Coordinating UAVs and AUVs for oceanographic field experiments: Challenges and lessons learned

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