Dynamic System Identification, and Control for a Cost-Effective and Open-Source Multi-rotor MAV

Sa, Inkyu; Kamel, Mina; Khanna, Raghav; Popović, Marija; Nieto, Juan; Siegwart, Roland

doi:10.1007/978-3-319-67361-5_39

Cited by 20 publications

(24 citation statements)

References 20 publications

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“…We show our IPP strategy running in real-time on a DJI Matrice 100 [25]. The experiments are conducted in an empty 2 × 2 m indoor environment with a maximum altitude of 2 m and the Vicon motion capture system for state estimation ( Fig.…”

Section: Methodsmentioning

confidence: 99%

Multiresolution mapping and informative path planning for UAV-based terrain monitoring

Popović

Vidal-Calleja

Hitz

et al. 2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-Unmanned aerial vehicles (UAVs) can offer timely and cost-effective delivery of high-quality sensing data. However, deciding when and where to take measurements in complex environments remains an open challenge. To address this issue, we introduce a new multiresolution mapping approach for informative path planning in terrain monitoring using UAVs. Our strategy exploits the spatial correlation encoded in a Gaussian Process model as a prior for Bayesian data fusion with probabilistic sensors. This allows us to incorporate altitudedependent sensor models for aerial imaging and perform constant-time measurement updates. The resulting maps are used to plan information-rich trajectories in continuous 3-D space through a combination of grid search and evolutionary optimization. We evaluate our framework on the application of agricultural biomass monitoring. Extensive simulations show that our planner performs better than existing methods, with mean error reductions of up to 45% compared to traditional "lawnmower" coverage. We demonstrate proof of concept using a multirotor to map color in different environments.

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Section: Methodsmentioning

confidence: 99%

Multiresolution mapping and informative path planning for UAV-based terrain monitoring

Popović

Vidal-Calleja

Hitz

et al. 2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…There is another research on the outdoor performance of a commercial MUAV without RTK system, where the three-dimensional stability of Matrice 100 in the presence of wind (3.6 -7.4 m/s) is reported for three specific routines: hover (4.5 cm RMS), step response (26 cm RMS) and path tracking (10 cm RMS) (Sa et al 2017), which correspond to the routines of interest of this work. Table 5 shows a summary of the stability results.…”

Section: Muav Flight Performancementioning

confidence: 99%

Implementation of MUAV as reference source for GLAO systems

García

Cuevas

2019

Exp Astron

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We propose an alternative method to generate an artificial reference source for a Ground Layer Adaptive Optics system airborne on a Multirotor Unmanned Aerial Vehicle. Turbulence profiles from the Ground Layer at the National Astronomical Observatory in San Pedro Martir were analyzed to establish the requirements in luminosity, altitude, and flight stability of an artificial source. We found the source must be at least 800 m above the observatory's surface and follow a fixed trajectory to emulate the apparent movement of the stars with a stability of 1.54 cm in time intervals smaller than 18 ms. We establish some commercial and customized MUAVs can nearly accomplish this task.

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“…In this section, we summary our recent work [34] of MAV dynamic systems identification and describe nonlinear Model Predictive Control (nMPC).…”

Section: Dynamic Systems Identification and Non-linear Model Predictimentioning

confidence: 99%

“…After this process, we use classic system identification techniques given input and output without time delay estimation option and estimated dynamic systems are presented in [34].…”

Section: Dynamic Systems Identificationmentioning

confidence: 99%

Build Your Own Visual-Inertial Drone: A Cost-Effective and Open-Source Autonomous Drone

Kamel

Burri

et al. 2018

IEEE Robot. Automat. Mag.

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This paper describes an approach to building a cost-effective and research grade visualinertial odometry aided vertical taking-off and landing (VTOL) platform. We utilize an off-the-shelf visual-inertial sensor, an onboard computer, and a quadrotor platform that are factory-calibrated and mass-produced, thereby sharing similar hardware and sensor specifications (e.g., mass, dimensions, intrinsic and extrinsic of camera-IMU systems, and signal-to-noise ratio). We then perform system calibration and identification enabling the use of our visual-inertial odometry, multi-sensor fusion, and model predictive control frameworks with the off-the-shelf products. This approach partially circumvents the tedious parameter tuning procedures required to build a full system. The complete system is evaluated extensively both indoors using a motion capture system and outdoors using a laser tracker while performing hover and step responses, and trajectory following tasks in the presence of external wind disturbances. We achieve root-mean-square (RMS) pose errors of 0.036 m with respect to reference hover trajectories. We also conduct relatively long distance (≈180 m) experiments on a farm site, demonstrating 0.82% drift error of the total flight distance. This paper conveys the insights we acquired about the platform and sensor module and offers open-source code with tutorial documentation to the community.

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Dynamic System Identification, and Control for a Cost-Effective and Open-Source Multi-rotor MAV

Cited by 20 publications

References 20 publications

Multiresolution mapping and informative path planning for UAV-based terrain monitoring

Multiresolution mapping and informative path planning for UAV-based terrain monitoring

Implementation of MUAV as reference source for GLAO systems

Build Your Own Visual-Inertial Drone: A Cost-Effective and Open-Source Autonomous Drone

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