Unscented Kalman filter state estimation for manipulating unmanned aerial vehicles

Khamseh, Hossein Bonyan; Ghorbani, Shahab; Janabi–Sharifi, Farrokh

doi:10.1016/j.ast.2019.06.009

Cited by 70 publications

(30 citation statements)

References 26 publications

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“…For a linear-like model, the Complementary filter is efficient concerning noise and the gyro bias estimation. This -order direct and passive filter combined with Three Axis Attitude Determination (TRIAD) scheme is proposed to launch an attitude estimation solution [18]. In the open-source platform [19,20], this kind of filter becomes the feasible method to determine the whole system's dynamics.…”

Section: Problem Statementmentioning

confidence: 99%

Dynamical analysis and validation of motion control by filtering performance for aerial robotic system

Tran¹,

Vo²,

Tu³

et al. 2021

J. vibroeng.

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Although drone appears in different applications, such as environmental inspection, agriculture or transportation, some aspects require more studies to clarify the efficient outcomes. One of them is to investigate the filtering performance such as Kalman and Complementary filters when the autonomous aerial system (AAS) handles its mission. However, it lacks the systematic research about these filters to provide the proper evaluation. Therefore, in this paper, the research topic related to AAS model to indicate the filtering effects in the agricultural application for making an alternative solution is presented. Firstly, the mathematical representation of system model is established in order to describe the dynamical performance and motion constraints. Then, the theory of filter structure is implemented to estimate the system state. The proposed design is validated in both numerical simulation and experiments. The system parameters that are monitored, include angular values of roll, pitch and yaw in three axes, motion parameters and its trajectories. By utilizing various sensing devices such as gyroscope, accelerometer and compass in real-world hardware, the experimental results could evaluate more precise and efficient design. The findings of this study are to (1) propose the model of AAS and proper filters, (2) launch the verified process and calibration, and (3) demonstrate the competitive performance among filters. From these results of our work, it could be clearly seen that the AAS plays an important role in daily applications and the related topics are still attractive.

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Section: Problem Statementmentioning

confidence: 99%

Dynamical analysis and validation of motion control by filtering performance for aerial robotic system

Tran¹,

Vo²,

Tu³

et al. 2021

J. vibroeng.

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show abstract

“…From state estimation perspective, while EKF is the most popular and widely-used state estimation algorithm in UAV applications [106]- [108], its linearization step along with its poor performance for highly nonlinear systems limit its application for MUAVs. Alternatively, Unscented Kalman Filter (UKF) has been formulated in [109], [110] to remedy the limitations of EKF in such applications. However, the improved performance of UKF-based state estimation methods comes, in general, at the cost of high computational complexity [110], [111].…”

Section: State and Parameter Estimationmentioning

confidence: 99%

“…Alternatively, Unscented Kalman Filter (UKF) has been formulated in [109], [110] to remedy the limitations of EKF in such applications. However, the improved performance of UKF-based state estimation methods comes, in general, at the cost of high computational complexity [110], [111]. In [110], scaled spherical UKF was formulated for MUAVs applications, and was shown to be computationally more efficient than conventional UKF, but the latter outperforms in terms of estimation accuracy.…”

Section: State and Parameter Estimationmentioning

confidence: 99%

Estimation and control of a manipulating unmanned aerial vehicle

Khamseh¹

2021

Preprint

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A Manipulating Unmanned Aerial Vehicle (MUAV) is an aerial platform equipped with a mechanism to physically interact with its environment. The interaction is realized by means of robotic arm(s), vehicle body or suspension cable(s) and enables a wide range of novel applications including perching, grasping, pick–and–place, load transportation, etc. However, this is a challenging task as MUAVs are inherently unstable platforms with highly nonlinear and coupled dynamics, often associated with moving parts that result in complex modelling, estimation and control problems. This thesis deals with the problem of estimation and control of MUAVs. First, a comprehensive literature survey covering various aspects of MUAVs such as those related to modelling, estimation and control is presented. In the first approach for MUAV state estimation and control, effects of robotic manipulator on Unmanned Aerial Vehicle (UAV) dynamic equations of motion is treated by adding process noise with unknown noise statistics to conventional UAV dynamic model. With that in mind, state estimating and control of a UAV by means of conventional Kalman filters and their adaptive counterparts are formulated. Having designed Linear Quadratic Regulator (LQR) laws, it is shown that adaptive Kalman filters provide accurate satisfactory estimation and overall control of a UAV, even with simultaneous uncertain process and measurement noise statistics. Next, in order to improve the estimation and overall control performance of the previous approach, full nonlinear and coupled dynamic modelling of a MUAV based on Euler–Lagrange formulation is presented. Then, a General Unscented Kalman Filter (GUKF) is proposed to accomplish full state estimation of a MUAV, along with LQR control laws. Finally, in order to improve the execution time of GUKF, a computationally–efficient UKF known as Scaled Spherical UKF (SSUKF) with estimation and overall control performance comparable to GUKF is formulated. It is shown that both UKF–based algorithms result in satisfactory estimation and setpoint/trajectory tracking of quadcopter UAV and its robotic manipulator, even in scenarios with increased noise level and a period of total outage of sensory data.

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“…In the presence of established methods, the dynamics of AMSs with rigid manipulators (AMS-RMs) have already been analyzed [8][9][10] and a number of control methods ( [7,8,[15][16][17][18][19]) have been proposed. However, AMS-RMs have several issues, highlighted in Table 1.…”

Section: Introductionmentioning

confidence: 99%

“…The focus of this work will be placed on coupled modeling of ACMS dynamics. While coupled modeling of AMS-RMs can be found in the literature ( [16,26]), coupled dynamic modeling of ACMS and its verification remain to be done and constitute the main contribution of this paper.…”

Section: Introductionmentioning

confidence: 99%

Coupled Dynamic Modeling and Control of Aerial Continuum Manipulation Systems

2021

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Aerial continuum manipulation systems (ACMSs) were newly introduced by integrating a continuum robot (CR) into an aerial vehicle to address a few issues of conventional aerial manipulation systems such as safety, dexterity, flexibility and compatibility with objects. Despite the earlier work on decoupled dynamic modeling of ACMSs, their coupled dynamic modeling still remains intact. Nonlinearity and complexity of CR modeling make it difficult to design a coupled ACMS model suitable for practical applications. This paper presents a coupled dynamic modeling for ACMSs based on the Euler–Lagrange formulation to deal with CR and the aerial vehicle as a unified system. For this purpose, a general vertical take-off and landing vehicle equipped with a tendon-driven continuum arm is considered to increase the dexterity and compliance of interactions with the environment. The presented model is independent of the motor’s configuration and tilt angles and can be applied to model any under/fully actuated ACMS. The modeling approach is complemented with a Lyapunov-wise stable adaptive sliding mode control technique to demonstrate the validity of the proposed method for such a complex system. Simulation results in free flight motion scenarios are reported to verify the effectiveness of the proposed modeling and control techniques.

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Unscented Kalman filter state estimation for manipulating unmanned aerial vehicles

Cited by 70 publications

References 26 publications

Dynamical analysis and validation of motion control by filtering performance for aerial robotic system

Dynamical analysis and validation of motion control by filtering performance for aerial robotic system

Estimation and control of a manipulating unmanned aerial vehicle

Coupled Dynamic Modeling and Control of Aerial Continuum Manipulation Systems

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