Quadrocopter Hovering Using Position-estimation Information from Inertial Sensors and a High-delay Video System

Bošnak, Matevž; Matko, Drago; Blažič, Sašo

doi:10.1007/s10846-011-9646-5

Cited by 43 publications

(30 citation statements)

References 33 publications

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“…Nemra and Aouf [29] proposed a robust airborne 3D Visual Simultaneous Localization and Mapping (VSLAM) solution based on a stereovision system. Bonak et al [5] implemented a method to hold a quadcopter aircraft in a stable hovering position using artificial marker-based visual information and inertial measurement. But the monocular Simultaneous Localization and Mapping (SLAM) techniques are more popular recently, they has been researched in differnt UAV platforms, e.g.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Monocular Visual-Inertial SLAM-Based Collision Avoidance Strategy for Fail-Safe UAV Using Fuzzy Logic Controllers

Olivares-Méndez

Suarez-Fernandez

et al. 2013

J Intell Robot Syst

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In this paper, we developed a novel Cross-Entropy Optimization (CEO)-based Fuzzy Logic Controller (FLC) for Fail-Safe UAV to expand its collision avoidance capabilities in the GPS-denied environments using Monocular Visual-Inertial SLAM-based strategy. The function of this FLC aims to control the heading of Fail-Safe UAV to avoid the obstacle, e.g. wall, bridge, tree line et al, using its real-time and accurate localization information. In the Matlab Simulink-based training framework, the Scaling Factor (SF) is adjusted according to the collision avoidance task firstly, and then the Membership Function (MF) is tuned based on the optimized Scaling Factor to further improve the control performances. After obtained the optimal SF and MF, 64 % of rules has been reduced (from 125 rules to 45 rules), and a large number of real see-and-avoid tests with a quadcopter have done. The simulation and experiment results show that this new proposed FLC can precisely navigates the Fail-Safe UAV to avoid the obstacle, obtaining better performances compared to only SF optimization-based FLC. To our best knowledge, this is the first work to present the optimized FLC using Cross-Entropy method in both SF and MF optimization, and apply it in the UAV.

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Section: Related Workmentioning

confidence: 99%

“…5 Engel et al [12] has presented an Visual-Inertial SLAM system to enable UAV to precisely fly with different trajectories. 6 Both of them has published their works as the open sources in the Robot Operating System (ROS).…”

Section: Monocular Visual-inertial Slam-based Controlmentioning

confidence: 99%

Monocular Visual-Inertial SLAM-Based Collision Avoidance Strategy for Fail-Safe UAV Using Fuzzy Logic Controllers

Olivares-Méndez

Suarez-Fernandez

et al. 2013

J Intell Robot Syst

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“…• The computations for estimating the position and avoiding collisions should be inexpensive enough to be handled by on-board microcontrollers (as opposed to offloading them to external computers [3], [4]). …”

Section: Introductionmentioning

confidence: 99%

Towards collision avoidance for commodity hardware quadcopters with ultrasound localization

Vedder¹,

Eriksson²,

Skarin³

et al. 2015

2015 International Conference on Unmanned Aircraft Systems (ICUAS)

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Abstract-We present a quadcopter platform built with commodity hardware that is able to do localization in GNSS-denied areas and avoid collisions by using a novel easy-to-setup and inexpensive ultrasoundlocalization system. We address the challenge to accurately estimate the copter's position and not hit any obstacles, including other, moving, quadcopters. The quadcopters avoid collisions by placing contours that represent risk around static and dynamic objects and acting if the risk contours overlap with ones own comfort zone. Position and velocity information is communicated between the copters to make them aware of each other. The shape and size of the risk contours are continuously updated based on the relative speed and distance to the obstacles and the current estimated localization accuracy. Thus, the collision-avoidance system is autonomous and only interferes with human or machine control of the quadcopter if the situation is hazardous. In the development of this platform we used our own simulation system using fault-injection (sensor faults, communication faults) together with automatically-generated tests to identify problematic scenarios for which the localization and risk contour parameters had to be adjusted. In the end, we were able to run thousands of simulations without any collisions, giving us confidence that also many real quadcopters can manoeuvre collision free in spaceconstrained GNSS-denied areas.

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“…When this approach was tested under natural outdoor conditions, its vision-based hovering accuracy was found to range between -2m and 2m. In [17], a monocular camera and computer vision with high delays was used to avoid drift obtained with a simple IMU pose estimation. A camera is also used in [18] as a dual-sensor to achieve a drift-free hover and perform obstacles avoidance on a quadrotor.…”

Section: Introductionmentioning

confidence: 99%

Hovering by Gazing: A Novel Strategy for Implementing Saccadic Flight-Based Navigation in GPS-Denied Environments

Manecy

Marchand

Viollet

2014

International Journal of Advanced Robotic Systems

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Hovering flies are able to stay still in place when hovering above flowers and burst into movement towards a new object of interest (a target). This suggests that sensorimotor control loops implemented onboard could be usefully mimicked for controlling Unmanned Aerial Vehicles (UAVs). In this study, the fundamental head-body movements occurring in free-flying insects was simulated in a sighted twin-engine robot with a mechanical decoupling inserted between its eye (or gaze) and its body. The robot based on this gaze control system achieved robust and accurate hovering performances, without an accelerometer, over a ground target despite a narrow eye field of view (±5 • ). The gaze stabilization strategy validated under Processor-In-the-Loop (PIL) and inspired by three biological Oculomotor Reflexes (ORs) enables the aerial robot to lock its gaze onto a fixed target regardless of its roll angle. In addition, the gaze control mechanism allows the robot to perform short range target to target navigation by triggering an automatic fast "target jump" behaviour based on a saccadic eye movement.

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Quadrocopter Hovering Using Position-estimation Information from Inertial Sensors and a High-delay Video System

Cited by 43 publications

References 33 publications

Monocular Visual-Inertial SLAM-Based Collision Avoidance Strategy for Fail-Safe UAV Using Fuzzy Logic Controllers

Monocular Visual-Inertial SLAM-Based Collision Avoidance Strategy for Fail-Safe UAV Using Fuzzy Logic Controllers

Towards collision avoidance for commodity hardware quadcopters with ultrasound localization

Hovering by Gazing: A Novel Strategy for Implementing Saccadic Flight-Based Navigation in GPS-Denied Environments

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