A Framework for Simulation and Testing of UAVs in Cooperative Scenarios

Mancini, Adriano; Cesetti, A.; Iualè, A.; Frontoni, Emanuele; Zingaretti, Primo; Longhi, Sauro

doi:10.1007/s10846-008-9268-8

Cited by 17 publications

(4 citation statements)

References 15 publications

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“…In case of indoor environments the integration of Laser Range Finder or camera could significantly improve the overall performance (Cesetti et al (2010); Ascani et al (2008)). Future works will be steered to extend the set of sensors integrating visual information based on SIFT/SURF feature detection also for Simultaneous Localization and Mapping (Khoshelham et al (2010)) in multi-robot scenarios (Mancini et al (2009)) and to evaluate this technology also for the pedestrian localization.…”

Section: Resultsmentioning

confidence: 99%

A Biased Extended Kalman Filter for Indoor Localization of a Mobile Agent using Low-Cost IMU and UWB Wireless Sensor Network

Benini

Mancini

Marinelli

et al. 2012

IFAC Proceedings Volumes

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Section: Resultsmentioning

confidence: 99%

A Biased Extended Kalman Filter for Indoor Localization of a Mobile Agent using Low-Cost IMU and UWB Wireless Sensor Network

Benini

Mancini

Marinelli

et al. 2012

IFAC Proceedings Volumes

Self Cite

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“…Firstly, this studies simulation fails to consider the effects of atmospheric pressure, wind, turbulence, gravity or UAV dynamics on the drones. Considering these factors has been viewed as an important part of the realism of UAV flight simulations [32] and therefore the findings of this project may be less applicable to real-world scenarios. Secondly, this study applied restrictions on the D* Lite implementation (see Section 2.6.2) for the algorithm to be applied to the real-time simulation.…”

Section: Limitationsmentioning

confidence: 99%

Dynamic Pathfinding for a Swarm Intelligence Based UAV Control Model Using Particle Swarm Optimisation

Pyke¹,

Stark

2021

Front. Appl. Math. Stat.

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In recent years unmanned aerial vehicles (UAVs) have become smaller, cheaper, and more efficient, enabling the use of multiple autonomous drones where previously a single, human-operated drone would have been used. This likely includes crisis response and search and rescue missions. These systems will need a method of navigating unknown and dynamic environments. Typically, this would require an incremental heuristic search algorithm, however, these algorithms become increasingly computationally and memory intensive as the environment size increases. This paper used two different Swarm Intelligence (SI) algorithms: Particle Swarm Optimisation and Reynolds flocking to propose an overall system for controlling and navigating groups of autonomous drones through unknown and dynamic environments. This paper proposes Particle Swarm Optimisation Pathfinding (PSOP): a dynamic, cooperative algorithm; and, Drone Flock Control (DFC): a modular model for controlling systems of agents, in 3D environments, such that collisions are minimised. Using the Unity game engine, a real-time application, simulation environment, and data collection apparatus were developed and the performances of DFC-controlled drones—navigating with either the PSOP algorithm or a D* Lite implementation—were compared. The simulations do not consider UAV dynamics. The drones were tasked with navigating to a given target position in environments of varying size and quantitative data on pathfinding performance, computational and memory performance, and usability were collected. Using this data, the advantages of PSO-based pathfinding were demonstrated. PSOP was shown to be more memory efficient, more successful in the creation of high quality, accurate paths, more usable and as computationally efficient as a typical incremental heuristic search algorithm when used as part of a SI-based drone control model. This study demonstrated the capabilities of SI approaches as a means of controlling multi-agent UAV systems in a simple simulation environment. Future research may look to apply the DFC model, with the PSOP algorithm, to more advanced simulations which considered environment factors like atmospheric pressure and turbulence, or to real-world UAVs in a controlled environment.

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“…By exploiting our experience in the development of frameworks for mobile robot simulation and test [6], [7], [8] and in real-time and embedded systems [9], [10], we set our framework at a higher level of abstraction compared to the most common solutions in designing real-time firmware for embedded testing platforms. In particular, two main characteristics of our framework are: i) the software architecture is organized in layers that reflects different levels of abstraction; ii) each software layer is based on an open source operating system and therefore it does not show the same restrictions of commercial systems.…”

Section: Introductionmentioning

confidence: 99%

A real-time reliability and durability testing framework

Massi

Morganti

Claudi

et al. 2014

2014 IEEE/ASME 10th International Conference on Mechatronic and Embedded Systems and Applications (MESA)

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This paper presents a methodological framework for designing testing and measurement systems fully integrated with the enterprise information system. In comparison with the most common solutions for designing embedded testing platforms the proposed framework sets itself at a higher level of abstraction. The proposed framework allows getting different, programmable test benches that can run in parallel, and it does not restrict the choice of hardware, sensors and actuators, as it happens with commercial development systems for the same kind of machines. The framework is conceived to be used on embedded boards equipped with the GNU/Linux operating system and with at least one network interface. By using open data formats, the framework provides an easy way to exchange data with enterprise information systems, thus assuring interoperability with different IT solutions. The paper includes the description of a cooker hood testing system designed and implemented with this framework, and which highlights the advantages of the proposed development method.

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A Framework for Simulation and Testing of UAVs in Cooperative Scenarios

Cited by 17 publications

References 15 publications

A Biased Extended Kalman Filter for Indoor Localization of a Mobile Agent using Low-Cost IMU and UWB Wireless Sensor Network

A Biased Extended Kalman Filter for Indoor Localization of a Mobile Agent using Low-Cost IMU and UWB Wireless Sensor Network

Dynamic Pathfinding for a Swarm Intelligence Based UAV Control Model Using Particle Swarm Optimisation

A real-time reliability and durability testing framework

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