Experimental Evaluation of Continuum Deformation with a Five Quadrotor Team

Romano, Matthew A.; Kuevor, Prince; Lukacs, Derek; Marshall, Owen; Stevens, Mia N.; Rastgoftar, Hossein; Cutler, James; Atkins, Ella M.

doi:10.23919/acc.2019.8815266

Cited by 14 publications

(8 citation statements)

References 25 publications

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“…In an obstacle-laden environment, a large scale multi-agent system can safely and aggressively deform using continuum deformation coordination. [24] experimentally evaluated continuum deformation coordination in 2D with a team of 5 quadcopters.…”

Section: Related Workmentioning

confidence: 99%

Drones Practicing Mechanics

Harshvardhan¹,

Rastgoftar²

2022

Preprint

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Mechanics of materials is a classic course of engineering presenting the fundamentals of strain and stress analysis to junior undergraduate students in several engineering majors. So far, material deformation and strain have been only analyzed using theoretical and numerical approaches, and they have been experimentally validated by expensive machines and tools. This paper presents a novel approach for strain and deformation analysis by using quadcopters. We propose to treat quadcopters as finite number of particles of a deformable body and apply the principles of continuum mechanics to illustrate the concept of axial and shear deformation by using quadcopter hardware in a 3-D motion space. The outcome of this work can have significant impact on undergraduate education by filling the gap between in-class learning and hardware realization and experiments, where we introduce new roles for drones as "teachers" providing a great opportunity for practicing theoretical concepts of mechanics in a fruitful and understandable way.

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Section: Related Workmentioning

confidence: 99%

Drones Practicing Mechanics

Harshvardhan¹,

Rastgoftar²

2022

Preprint

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“…Our experimental flight vehicle, shown in Figure 2 , is a multirotor equipped with a gyroscope, accelerometer, and magnetometer. The multirotor used here were introduced in Section IV of [ 39 ] and flies an MPU9250 IMU (gyro, accelerometer, magnetometer) and BMP280 barometer. Since the work in [ 39 ], a second BeagleBone Blue microprocessor, a PNI RM3100 magnetometer, and a DJI Naza-M V2 GPS unit were added to the vehicle.…”

Section: Experimental Setup and Proceduresmentioning

confidence: 99%

“…The multirotor used here were introduced in Section IV of [ 39 ] and flies an MPU9250 IMU (gyro, accelerometer, magnetometer) and BMP280 barometer. Since the work in [ 39 ], a second BeagleBone Blue microprocessor, a PNI RM3100 magnetometer, and a DJI Naza-M V2 GPS unit were added to the vehicle. In addition, the flight controller was changed to the Robotics Cape autopilot ( RC Pilot ) ( RC Pilot was created by StrawsonDesign ( , accessed on 19 September 2021)) open-source flight controller.…”

Section: Experimental Setup and Proceduresmentioning

confidence: 99%

Improving Attitude Estimation Using Gaussian-Process-Regression-Based Magnetic Field Maps

Kuevor

Cutler

Atkins

2021

Sensors

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Magnetometers measure the local magnetic field and are present in most modern inertial measurement units (IMUs). Readings from magnetometers are used to identify Earth’s Magnetic North outdoors, but are often ignored during indoor experiments since the magnetic field does not behave how most expect. This paper presents methods to create, validate, and visualize three-dimensional magnetic field maps to expand the use of magnetic fields as a sensing modality for navigation. The utility of these maps is measured in their ability to accurately represent the magnetic field and to enable dynamic attitude estimation. In experiments with motion capture truth data, a small multicopter with three-axis inertial measurements, including magnetometer, traversed five flight profiles distinctly exciting roll, pitch, and yaw motion to provide interesting trajectories for attitude estimation. Indoor experimental results were compared to those outdoors to emphasize how spatial variation in the magnetic field drives the need for our mapping techniques. Our work presents a new way of visualizing 3D magnetic fields, which allows users to better reason about the magnetic field in their workspace. Next, we show that magnetic field maps generated from coverage patterns are generally more accurate, but training such maps using observations from desired flight paths is sufficient in the vicinity of these paths. All training sets were interpolated using Gaussian process regression (GPR), which yielded maps with <1 μT of error when interpolating between and extrapolating outside of observed locations. Finally, we validated the utility of our GPR-based maps in enabling attitude estimates in regions of high magnetic field spatial variation with experimental data.

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“…Each motor was connected to an AIR 20A Electronic Speed Controller (ESC) controlled by Beaglebone PWM signals. 3D printed propeller guards, originally designed in Romano et al (2019), were attached at the base of each motor and connected by carbon fiber tubes to provide protection for propellers and users. MOCAP was used to determine the 3D location and heading of the quadcopter with targets placed on top of the propeller guards.…”

Section: Hardwarementioning

confidence: 99%

Development, Implementation, and Experimental Outdoor Evaluation of Quadcopter Controllers for Computationally Limited Embedded Systems

Paredes

Sharma

et al. 2021

Preprint

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Quadcopters are increasingly used for applications ranging from hobby to industrial products and services. This paper serves as a tutorial on the design, simulation, implementation, and experimental outdoor testing of digital quadcopter flight controllers, including Explicit Model Predictive Control, Linear Quadratic Regulator, and Proportional Integral Derivative. A quadcopter was flown in an outdoor testing facility and made to track an inclined, circular path at different tangential velocities under ambient wind conditions. Controller performance was evaluated via multiple metrics, such as position tracking error, velocity tracking error, and onboard computation time. Challenges related to the use of computationally limited embedded hardware and flight in an outdoor environment are addressed with proposed solutions.

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Experimental Evaluation of Continuum Deformation with a Five Quadrotor Team

Cited by 14 publications

References 25 publications

Drones Practicing Mechanics

Drones Practicing Mechanics

Improving Attitude Estimation Using Gaussian-Process-Regression-Based Magnetic Field Maps

Development, Implementation, and Experimental Outdoor Evaluation of Quadcopter Controllers for Computationally Limited Embedded Systems

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