Navigation and Control of Unconventional VTOL UAVs in Forward-Flight With Explicit Wind Velocity Estimation

Cohen, Mitchell; Forbes, James Richard

doi:10.1109/lra.2020.2966406

Cited by 14 publications

(13 citation statements)

References 27 publications

(44 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To ensure robustness to state-estimation error, an IEKF based on [26] is used to provide an estimate of the quadrotor states, which are an element of SE 2 (3), and sensor biases. Biased rate gyro and accelerometer measurements are used as interoceptive prediction sensors, and GPS position and velocity measurements, as well as magnetometer measurements, are used as correction sensors.…”

Section: Monte-carlo Simulation Resultsmentioning

confidence: 99%

Finite-Horizon LQR Control of Quadrotors on $SE_2(3)$

Cohen

Abdulrahim

Forbes

2020

IEEE Robot. Autom. Lett.

Self Cite

View full text Add to dashboard Cite

This paper considers optimal control of a quadrotor unmanned aerial vehicles (UAV) using the discrete-time, finite-horizon, linear quadratic regulator (LQR). The state of a quadrotor UAV is represented as an element of the matrix Lie group of double direct isometries, SE2(3). The nonlinear system is linearized using a left-invariant error about a reference trajectory, leading to an optimal gain sequence that can be calculated offline. The reference trajectory is calculated using the differentially flat properties of the quadrotor. Monte-Carlo simulations demonstrate robustness of the proposed control scheme to parametric uncertainty, state-estimation error, and initial error. Additionally, when compared to an LQR controller that uses a conventional error definition, the proposed controller demonstrates better performance when initial errors are large.

show abstract

Section: Monte-carlo Simulation Resultsmentioning

confidence: 99%

Finite-Horizon LQR Control of Quadrotors on $SE_2(3)$

Cohen

Abdulrahim

Forbes

2020

IEEE Robot. Autom. Lett.

Self Cite

View full text Add to dashboard Cite

show abstract

“…In various implementations related to the IEKF, researchers -including the authors -have proposed to treat IMU biases in the state linearly, that is, complete SE 2 (3) group law with x 1 + x 2 regarding body variables (biases). This is advocated notably in [10][11][12]14,16,[20][21][22]24] and the term "imperfect IEKF" that refers to it, as well as the technique itself, was coined in [17]. We demonstrate in this paper that the proposed group law (10) is a far better option, both theoretically and in practice.…”

Section: A Group Structure For Two-frames Systemsmentioning

confidence: 83%

“…The definition encompasses recently introduced Lie groups: SO(d) + k,0 coincides with SE k (d) introduced for SLAM in [30], named SE k (d) in [18], and exploited in [20][21][22][23][24], see also [35]. Similarly SO(3) + 2,0 coincides with SE 2 (3), introduced in [9,17] for navigation and exploited in [11][12][13][14]22]. In various implementations related to the IEKF, researchers -including the authors -have proposed to treat IMU biases in the state linearly, that is, complete SE 2 (3) group law with x 1 + x 2 regarding body variables (biases).…”

Section: A Group Structure For Two-frames Systemsmentioning

confidence: 98%

“…The discovery that dynamics associated with (unbiased) inertial measurement units (IMU) are group affine in [9] has led to various recent experimental and theoretical successes, e.g. [11][12][13][14][15][16]. 2-IEKF possesses consistency properties when the system is not fully observable as in the problem of Simultaneous Localization and Mapping (SLAM) [17][18][19], as exploited in [20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Geometry of Navigation Problems

Barrau,

Bonnabel

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…A key observation [5] is that the group-affine property only holds for the model of the navigation states and is lost for the combined system when bias is added in this manner. The practical applicability of this approach, named "Imperfect-IEKF" by Barrau [5], has been confirmed by several authors [5], [6], [7]. Recent work [8] on second order systems on Lie-groups has demonstrated that there is a natural symmetry that can be used to model both configuration and velocity states in a single geometry.…”

Section: Introduction and Related Workmentioning

confidence: 92%

Overcoming Bias: Equivariant Filter Design for Biased Attitude Estimation With Online Calibration

Fornasier

Brommer

et al. 2022

IEEE Robot. Autom. Lett.

View full text Add to dashboard Cite

Stochastic filters for on-line state estimation are a core technology for autonomous systems. The performance of such filters is one of the key limiting factors to a system's capability. Both asymptotic behavior (e.g., for regular operation) and transient response (e.g., for fast initialization and reset) of such filters are of crucial importance in guaranteeing robust operation of autonomous systems.This paper introduces a new generic formulation for a gyroscope aided attitude estimator using N direction measurements including both body-frame and reference-frame direction type measurements. The approach is based on an integrated state formulation that incorporates navigation, extrinsic calibration for all direction sensors, and gyroscope bias states in a single equivariant geometric structure. This newly proposed symmetry allows modular addition of different direction measurements and their extrinsic calibration while maintaining the ability to include bias states in the same symmetry. The subsequently proposed filter-based estimator using this symmetry noticeably improves the transient response, and the asymptotic bias and extrinsic calibration estimation compared to state-of-the-art approaches. The estimator is verified in statistically representative simulations and is tested in real-world experiments.

show abstract

Navigation and Control of Unconventional VTOL UAVs in Forward-Flight With Explicit Wind Velocity Estimation

Cited by 14 publications

References 27 publications

Finite-Horizon LQR Control of Quadrotors on $SE_2(3)$

Finite-Horizon LQR Control of Quadrotors on $SE_2(3)$

The Geometry of Navigation Problems

Overcoming Bias: Equivariant Filter Design for Biased Attitude Estimation With Online Calibration

Contact Info

Product

Resources

About