Self-localization of a tethered quadcopter using inertial sensors in a GPS-denied environment

Al-Radaideh, Amer; Sun, Liang

doi:10.1109/icuas.2017.7991436

Cited by 15 publications

(10 citation statements)

References 15 publications

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“…Figure 7 shows the noisy sensor measurements and the ones filtered by LPFs. The noisy measurements were directly used by the EKF and the values obtained by an LPF are used in the self-localization approach presented in [30]. The process noise covariance matrix of the EKF was tuned and selected as Q = diag(5 × 10 −3 , 5 × 10 −3 , 5 × 10 −3 ).…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…As for the LPF, it is based on a cutoff frequency set to 2 rad/s. To compare the estimation results obtained from the proposed 4-state EKF and the 3state EKF in [30], we assume that the 3-state model uses the actual cable-tension force from an onboard force sensor, but the 4-state EKF does not have access to the actual cable-tension force in the estimation process.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…Companies have also commercialized tethered drones on the market [27,28]. In our previous work [29,30], we presented both a low pass filter (LPF) and an extended Kalman filter (EKF) to estimate the three-dimensional (3D) location of the drone with respect to a ground platform (see Figure 1) while assuming known cable-tension force. In this paper, we assume the cable-tension force is unknown and we extend our previous work by enabling simultaneously estimation of both the 3D drone location and the cable-tension force, using only the measurements of onboard IMUs and altimeter.…”

Section: Introductionmentioning

confidence: 99%

“…To the best of our knowledge, existing literature [24][25][26][27][28][29][30] for the self-localization and control of tethered drones has assumed known cable-tension force and accurate drone thrust forces, which, however, are nontrivial to measure directly. The cable-tension force is usually assumed to be measured by a force sensor that is connected in series with the tether.…”

Section: Introductionmentioning

confidence: 99%

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Self-Localization of Tethered Drones without a Cable Force Sensor in GPS-Denied Environments

Al-Radaideh¹,

Sun²

2021

Drones

Self Cite

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This paper considers the self-localization of a tethered drone without using a cable-tension force sensor in GPS-denied environments. The original problem is converted to a state-estimation problem, where the cable-tension force and the three-dimensional position of the drone with respect to a ground platform are estimated using an extended Kalman filter (EKF). The proposed approach uses the data reported by the onboard electric motors (i.e., the pulse width modulation (PWM) signals), accelerometers, gyroscopes, and altimeter, embedded in the commercial-of-the-shelf (COTS) inertial measurement units (IMU). A system-identification experiment was conducted to determine the model that computes the drone thrust force using the PWM signals. The proposed approach was compared with an existing work that assumes known cable-tension force. Simulation results show that the proposed approach produces estimates with less than 0.3-m errors when the actual cable-tension force is greater than 1 N.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

Section: Simulation Results and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Self-Localization of Tethered Drones without a Cable Force Sensor in GPS-Denied Environments

Al-Radaideh¹,

Sun²

2021

Drones

Self Cite

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“…The addition of a tether to a UAV gives the potential of additional measurable quantities (rotational position and velocity of the reel, and tension of the tether) upon which non‐GPS based UAV position estimation may be performed. The tether arrival angle at the UAV is measured (Sandino et al, 2015), and/or the tension at the UAV estimated (Al‐Radaideh & Sun, 2017), and incorporated into the state estimation algorithm. Other prior related work has used a non‐taut catenary cable model for position estimation (Galea & Kry, 2017; Kiribayashi et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Autonomous hanging tether management and experimentation for an unmanned air‐surface vehicle team

Talke

Birchmore

Bewley

2022

Journal of Field Robotics

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The mission of an unmanned air vehicle (UAV) tethered to a small unmanned surface vehicle (USV) is considered. As opposed to the majority of existing tethered UAV work, which assumes a taut tether, this paper addresses the challenge of tether management for a slack, hanging tether in a dynamic ocean environment up to sea state 4 on the Douglas scale. A prototype smart reel system for a UAV–USV team was designed and experimentally validated in controlled and uncontrolled dynamic environments. A reference model for tether control based on static catenary cable theory was extended and shown to be valid through the entire operational envelop. A Kalman filter was developed to fuse slow (4 Hz) differential GPS (dGPS) relative position measurements with fast (100 Hz) inertial measurements, to output a 100 Hz estimate of relative position, relative velocity, and inertial sensor bias. A relative velocity‐based gain‐scheduled proportional‐derivative (PD) controller was developed, and demonstrated robustness to heave, pitch, and roll motion of the USV. Experimental testing with a UAV surrogate showed successful decoupling of heave motion from the UAV altitude in a controlled indoor environment. Taut tether control, in contrast, exhibited 12 times more tether tension at the UAV compared to the controller proposed here. The performance of the tuned controller with feedback from the Kalman filter exhibited minimal deviation from static catenary cable theory during the dynamic experiments. Indoor flight testing showed the developed reference model and controller essentially decoupled the dynamic motion of the USV from that of the UAV; the measured deflections of the UAV altitude and position essentially matched that of untethered flight. Outdoor flights validated the effectiveness of the controller in an unknown dynamic environment, for a larger relative position between the UAV and USV, using the dGPS Kalman filter solution to measure relative position. Initial experimental results look promising for continued on‐water testing of hanging‐tether UAV/USV teams.

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Structures and Deployments of a Flying Network Using Tethered Multicopters for Emergencies

Dinh

Vishnevsky

Larionov

et al. 2020

Distributed Computer and Communication Networks

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Self-localization of a tethered quadcopter using inertial sensors in a GPS-denied environment

Cited by 15 publications

References 15 publications

Self-Localization of Tethered Drones without a Cable Force Sensor in GPS-Denied Environments

Self-Localization of Tethered Drones without a Cable Force Sensor in GPS-Denied Environments

Autonomous hanging tether management and experimentation for an unmanned air‐surface vehicle team

Structures and Deployments of a Flying Network Using Tethered Multicopters for Emergencies

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