OpenUAV: A UAV Testbed for the CPS and Robotics Community

Schmittle, Matt; Lukina, Anna; Vacek, Lukas; Das, Jnaneshwar; Buskirk, Christopher P.; Rees, Stephen A.; Sztipanovits, János; Grosu, Radu; Kumar, Vijay

doi:10.1109/iccps.2018.00021

Cited by 57 publications

(30 citation statements)

References 8 publications

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“…Gazebo [46] is a popular simulator for both indoor and outdoor applications [91]. Gazebo relies on the LiftDrag Plugin to simulate aerodynamic properties, and supports many common sensors such as stereo-cameras and LIDAR.…”

Section: Aerial Roboticsmentioning

confidence: 99%

A Review of Physics Simulators for Robotic Applications

Collins

Chand²,

Vanderkop³

et al. 2021

IEEE Access

162

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The use of simulators in robotics research is widespread, underpinning the majority of recent advances in the field. There are now more options available to researchers than ever before, however navigating through the plethora of choices in search of the right simulator is often non-trivial. Depending on the field of research and the scenario to be simulated there will often be a range of suitable physics simulators from which it is difficult to ascertain the most relevant one. We have compiled a broad review of physics simulators for use within the major fields of robotics research. More specifically, we navigate through key sub-domains and discuss the features, benefits, applications and use-cases of the different simulators categorised by the respective research communities. Our review provides an extensive index of the leading physics simulators applicable to robotics researchers and aims to assist them in choosing the best simulator for their use case.

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Section: Aerial Roboticsmentioning

confidence: 99%

A Review of Physics Simulators for Robotic Applications

Collins

Chand²,

Vanderkop³

et al. 2021

IEEE Access

162

View full text Add to dashboard Cite

show abstract

“…The simulation experiment is carried out on the gazebo [33] simulation platform under the Linux system. The UAV is simulated by the open-source simulation software package Hector_quadrotor [34].…”

Section: A Simulationmentioning

confidence: 99%

UAV Positioning Based on Multi-Sensor Fusion

et al. 2020

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Real-time and stable positioning data is essential for the UAV to perform various tasks. The traditional multi-sensor data fusion algorithm needs to know the measurement noise of sensor data, and even if there are corresponding adaptive methods to estimate the noise, most methods cannot deal with time-varying noise. In addition, traditional fusion algorithms usually are complicated, causing a large amount of calculation. In this paper, a multi-sliding window classification adaptive unscented Kalman filter (MWCAUKF) method with timestamp sort updating was proposed, which can improve the accuracy and stability of positioning. This method consists of three phases. First, according to the timestamp of sensor data, the multi-sensor data are added with fusion filtering in order. Then it estimates the measurement noise of multiple sensors through multiple sliding Windows. Finally, the sensor data classification method is adopted to deal with the filter instability caused by time-varying noise. Both theoretical analysis and experimental results show that this method has a low computational cost, high accuracy, and good stability. INDEX TERMS Multi-sensor fusion, unmanned aerial vehicle, positioning, adaptive Kalman filtering.

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“…RotorS [25] is a modular Micro Aerial Vehicle framework based on Gazebo [26] targeted to tackle higher-level tasks, such as collision avoidance, path planning, and vision-based problems, like Simultaneous Localization and Mapping (SLAM). OpenUAV [27] is a development to avoid the high barrier imposed by the use of flight UAV simulations due to the need for powerful computers and the time required for the initial set up. There are also several examples of papers focused on flight tasks [28].…”

Section: Uav Simulatormentioning

confidence: 99%

VENUE: Virtualized Environment for Multi-UAV Network Emulation

et al. 2019

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Unmanned Aerial Vehicles (UAVs) have progressively been integrated into people lives during the last years. It is quite common now to see UAVs flying in the countryside doing field inspection, in highways for traffic control operations, or above stadiums in sport and music events. It is also common to see spectacular UAV swarm showcases (in most cases they are just performing a choreography) showing the potential of upcoming technologies. This article is focused on multi-UAV scenarios, on the establishment of Flying Ad hoc Networks (FANETs), and on the integration of 5G technologies like Network Function Virtualization (NFV) or Software Defined Networking (SDN). In particular, this article presents a proposal for one of the most common problems that the research and development community has to face at some stage: the validation of the different solutions and deployments. In this area, there is currently a notorious gap between the design phase and the deployment phase, since traditional network simulators are not designed with the constraints imposed by UAVs in mind. Besides, services implementations (that are usually distributed into single-board computers carried as payloads by UAVs) cannot be easily combined with the simulators. VENUE (Virtualized Environment for multi-UAV network emulation) is presented as an experimentation platform that allows testing the integration of multi-UAV FANETs together with network services deployments. VENUE covers from the simulation/emulation phase up to the real equipment integration phase. The validation of the platform is also presented in this article through several UAV use cases that make use of NFV technologies.

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OpenUAV: A UAV Testbed for the CPS and Robotics Community

Cited by 57 publications

References 8 publications

A Review of Physics Simulators for Robotic Applications

A Review of Physics Simulators for Robotic Applications

UAV Positioning Based on Multi-Sensor Fusion

VENUE: Virtualized Environment for Multi-UAV Network Emulation

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