CrazyS: A Software-in-the-Loop Simulation Platform for the Crazyflie 2.0 Nano-Quadcopter

Silano, Giuseppe; Iannelli, Luigi

doi:10.1007/978-3-030-20190-6_4

Cited by 17 publications

(10 citation statements)

References 45 publications

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“…Finally, the video at [62] shows the advantages of using a modular architecture for the platform. The video shows how the whole system architecture can be tested under various conditions simply changing the scenario 10 . As shown in the video, halfway through the simulation (26 s) increasing the speed of the car causes the drone to tilt excessively moving to instability.…”

Section: Numerical Resultsmentioning

confidence: 99%

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MAT-Fly: An Educational Platform for Simulating Unmanned Aerial Vehicles Aimed to Detect and Track Moving Objects

Silano

Iannelli

2021

IEEE Access

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The main motivation of this work is to propose a simulation approach for a specific task within the Unmanned Aerial Vehicle (UAV) field, i.e., the visual detection and tracking of arbitrary moving objects. In particular, it is described MAT-Fly, a numerical simulation platform for multi-rotor aircraft characterized by the ease of use and control development. The platform is based on Matlab ® and the MathWorks ™ Virtual Reality (VR) and Computer Vision System (CVS) toolboxes that work together to simulate the behavior of a quad-rotor while tracking a car that moves along a nontrivial path. The VR toolbox has been chosen due to the familiarity that students have with Matlab and because it does not require a notable effort by the user for the learning and development phase thanks to its simple structure. The overall architecture is quite modular so that each block can be easily replaced with others simplifying the code reuse and the platform customization. Some simple testbeds are presented to show the validity of the approach and how the platform works. The simulator is released as open-source, making it possible to go through any part of the system, and available for educational purposes. INDEX TERMS educational, Matlab/Simulink, image-based visual servoing, trajectory control, vision detection and tracking, software-in-the-loop, unmanned aerial vehicles, multi-rotor

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Section: Numerical Resultsmentioning

confidence: 99%

“…The simulator is fully compatible with each further release of Matlab 9. The drone flies in an eye-in-hand configuration, i.e., tilts around the axis direct affects the camera orientation 10. The vr_octavia scenario was considered.VOLUME 4, 2016…”

mentioning

confidence: 99%

MAT-Fly: An Educational Platform for Simulating Unmanned Aerial Vehicles Aimed to Detect and Track Moving Objects

Silano

Iannelli

2021

IEEE Access

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“…To reduce the number of particles, they used LIDAR observations and actuation commands to estimate the pose of the drone. Recent studies on Crazyflie applications used simulated environments, frameworks and platforms (Giernacki et al, 2017;Nithya et al, 2019;Silano et al, 2020;Raja et al, 2021;Duisterhof et al, 2021). Paliotta et al (2021) utilized multiple microdrones (Crazyflie 2.1) to build a network to localize first responders indoors.…”

Section: Related Workmentioning

confidence: 99%

Micro and Macro Quadcopter Drones for Indoor Mapping to Support Disaster Management

Karam

Nex

Karlsson

et al. 2022

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The use of drones to explore indoor spaces has gained attention and popularity for disaster management and indoor navigation applications. In this paper we present the operations and mapping techniques of two drones that are different in terms of size, the sensors deployed, and the positioning and mapping techniques used. The first drone is a low-cost commercial quadcopter microdrone, a Crazyflie, while the second drone is a relatively expensive research quadcopter macrodrone, called MAX. We investigated their feasibility in mapping areas where satellite positioning is not available, such as indoor spaces. We compared the point clouds obtained by a multi-ranger deck, a multi-layer LIDAR scanner and a stereo camera, and assessed each against ground truth obtained with a terrestrial laser scanner. Results showed that both drones are capable of mapping relatively cluttered indoor environments and can provide point clouds that are sufficient for a quick exploration. Furthermore, the LIDAR scanner-based system can handle a relatively large office environment with an accumulated drift less than 0.02% (1 cm) on the Z-axis and 0.77% (50 cm) on the X and Y axes over a length trajectory of about 65 m. Despite the limited features of the sensor configuration of the Crazyflie, its performance is promising for mapping indoor spaces, given the relatively low deviation from the ground truth: cloud-to-cloud distances measured were generally less than 20 cm.

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“…The nodes are processes, and each node has a task associated with it, such as sending a Mavlink command to control the UAV trajectory and permitting reading messages from the UAV to discern its status in flight, using a simulation mode referred to as software in the loop (SITL). This simulator provides the ability to run different vehicles, such as a plane, copter, or rover, without a need for any microcontrollers or hardware [94]. In addition, two PX4 autopilot rotary wing UAVs, the 3DR Iris and Typhoon UAV, were chosen because they have good maneuverability and are more suitable for small-scale CPP missions.…”

Section: Simulation and Validation Platformmentioning

confidence: 99%

Coverage Path Planning with Semantic Segmentation for UAV in PV Plants

et al. 2021

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Solar energy is one of the most strategic energy sources for the world’s economic development. This has caused the number of solar photovoltaic plants to increase around the world; consequently, they are installed in places where their access and manual inspection are arduous and risky tasks. Recently, the inspection of photovoltaic plants has been conducted with the use of unmanned aerial vehicles (UAV). Although the inspection with UAVs can be completed with a drone operator, where the UAV flight path is purely manual or utilizes a previously generated flight path through a ground control station (GCS). However, the path generated in the GCS has many restrictions that the operator must supply. Due to these restrictions, we present a novel way to develop a flight path automatically with coverage path planning (CPP) methods. Using a DL server to segment the region of interest (RoI) within each of the predefined PV plant images, three CPP methods were also considered and their performances were assessed with metrics. The UAV energy consumption performance in each of the CPP methods was assessed using two different UAVs and standard metrics. Six experiments were performed by varying the CPP width, and the consumption metrics were recorded in each experiment. According to the results, the most effective and efficient methods are the exact cellular decomposition boustrophedon and grid-based wavefront coverage, depending on the CPP width and the area of the PV plant. Finally, a relationship was established between the size of the photovoltaic plant area and the best UAV to perform the inspection with the appropriate CPP width. This could be an important result for low-cost inspection with UAVs, without high-resolution cameras on the UAV board, and in small plants.

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CrazyS: A Software-in-the-Loop Simulation Platform for the Crazyflie 2.0 Nano-Quadcopter

Cited by 17 publications

References 45 publications

MAT-Fly: An Educational Platform for Simulating Unmanned Aerial Vehicles Aimed to Detect and Track Moving Objects

MAT-Fly: An Educational Platform for Simulating Unmanned Aerial Vehicles Aimed to Detect and Track Moving Objects

Micro and Macro Quadcopter Drones for Indoor Mapping to Support Disaster Management

Coverage Path Planning with Semantic Segmentation for UAV in PV Plants

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