Extended Kalman Filter State Estimation for Aerial Continuum Manipulation Systems

Ghorbani, Shahab; Janabi–Sharifi, Farrokh

doi:10.1109/lsens.2022.3190890

Cited by 7 publications

(5 citation statements)

References 17 publications

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“…The Figure 1 depicts a CAD model of a CAAMS with a TDCR. There have been notable endeavours to harness the potential of continuum manipulators for aerial manipulation tasks [31,[38][39][40][41][42][43][44][45][46][47][48]. These works are discussed under the Section 2.…”

Section: Overview Of Continuum Arm Aerial Manipulation System (Caams)mentioning

confidence: 99%

“…This research used Euler-Lagrange method for modelling and experimented the developed control and planning strategies through simulation. Ghorbani and Janabi-Sharifi developed a method for state estimation CAAMSs using deep neural networks (DNN) and extended Kalman filter (EKF) for dual-arm CAAMSs [43].…”

Section: Continuum Arm Aerial Manipulation Systems (Caams)mentioning

confidence: 99%

“…Another prominent area to consider when developing a CAAMS is the method of state estimation and shape sensing for the CMs and CAAMSs [43,80,107]. The CMs need special focus, as they are intended to be utilized to deal with unstructured, cluttered, or difficult to access spaces.…”

Section: Perspectives On Caams Researchmentioning

confidence: 99%

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Tendon-Driven Continuum Robots for Aerial Manipulation—A Survey of Fabrication Methods

Uthayasooriyan,

Vanegas,

Jalali

et al. 2024

Drones

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Aerial manipulators have seen a rapid uptake for multiple applications, including inspection tasks and aerial robot–human interaction in building and construction. Whilst single degree of freedom (DoF) and multiple DoF rigid link manipulators (RLMs) have been extensively discussed in the aerial manipulation literature, continuum manipulators (CMs), often referred to as continuum robots (CRs), have not received the same attention. This survey seeks to summarise the existing works on continuum manipulator-based aerial manipulation research and the most prevalent designs of continuous backbone tendon-driven continuum robots (TDCRs) and multi-link backbone TDCRs, thereby providing a structured set of guidelines for fabricating continuum robots for aerial manipulation. With a history spanning over three decades, dominated by medical applications, CRs are now increasingly being used in other domains like industrial machinery and system inspection, also gaining popularity in aerial manipulation. Fuelled by diverse applications and their associated challenges, researchers have proposed a plethora of design solutions, primarily falling within the realms of concentric tube (CT) designs or tendon-driven designs. Leveraging research works published in the past decade, we place emphasis on the preparation of backbones, support structures, tendons, stiffness control, test procedures, and error considerations. We also present our perspectives and recommendations addressing essential design and fabrication aspects of TDCRs in the context of aerial manipulation, and provide valuable guidance for future research and development endeavours in this dynamic field.

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Section: Overview Of Continuum Arm Aerial Manipulation System (Caams)mentioning

confidence: 99%

Section: Continuum Arm Aerial Manipulation Systems (Caams)mentioning

confidence: 99%

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Tendon-Driven Continuum Robots for Aerial Manipulation—A Survey of Fabrication Methods

Uthayasooriyan,

Vanegas,

Jalali

et al. 2024

Drones

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“…The method to deal with bad data can be divided into two main types, namely methods for looking for bad data in SCADA measurements and methods for looking for bad data with multiple data sources such as PMU [18] . The former method focuses on the use of Lagrange multiplier, hypothesis tests, or robust estimators for analyzing residuals.…”

Section: Introductionmentioning

confidence: 99%

Robust State Estimation Method Based on Mahalanobis Distance Under Non-Gauss Noise

Zhang,

Xu,

Xie

et al. 2024

IEEE Access

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Under non-Gauss noise condition, the performance of traditional state estimation methods based on Gauss measurement noise will be greatly reduced. In order to solve this problem, a robust state estimation method based on Mahalanobis distance under non-Gauss noise is proposed in this paper. First of all, based on the Mahalanobis distance, the calculation method of optimal buffer length for PMU measurements is used, which can unify the SCADA measurements with PMU measurements in the same snapshot. Then, Based on the two-stage processing method, in the first stage, the SCADA measurements are used for filtering by using maximum likelihood estimator to obtain the estimated values, and then the estimated values in the first-stage are combined with PMU measurements as the second-stage measurements for filtering, and finally the final results are obtained. Based on the IEEE-39 buses system and IEEE-118 buses system, under Gaussian noise and non-Gaussian noise, the AEE results of proposed method are very small, and which are all within 10 -3 , numerical tests under different simulation conditions verified the robustness and effectiveness.INDEX TERMS Non-Gauss noise; optimal buffer length; two-stage; maximum likelihood estimator; state estimation.

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“…Ref. [26] combined deep neural networks with extended Kalman filtering to solve the state estimation problem of a bimanual tendon-driven aerial continuum operating system (ACMS), and the performance of the proposed method was demonstrated by simulation results. Ref.…”

Section: Introductionmentioning

confidence: 99%

Image Splicing Compression Algorithm Based on the Extended Kalman Filter for Unmanned Aerial Vehicles Communication

Liang

Zhao

Liu³

et al. 2023

Drones

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The technology of unmanned aerial vehicles (UAVs) is extensively utilized across various domains due to its exceptional mobility, cost-effectiveness, rapid deployment, and flexible configuration. However, its limited storage space and the contradiction between the large amount of image data returned and the limited bandwidth in emergency scenarios necessitate the development of a suitable UAVs image compression technology. In this research, we propose an image splicing compression algorithm based on the extended Kalman filter for UAV communication (SEKF-UC). Firstly, we introduce the concept of image splicing to enhance the compression ratio while ensuring image quality, leveraging the characteristics of deep learning-based compression, which are relatively unaffected by the size of the compressed data. Secondly, to address the issue of slow processing speed of deep neural networks (DNN) caused by the substantial volume of data involved in image splicing, we incorporate an extended Kalman filter module to expedite the process. Simulation experiments show that our proposed algorithm outperforms existing methods in multiple ways, achieving a significant compression ratio improvement, from 2:1 to 25:1, with a marginal reduction of 6.5% in structural similarity (SSIM) compared to the non-spliced approach. Moreover, for deep neural networks (DNN), our method incorporating the extended Kalman filter module achieves the same error level with only 30 iterations—a significant reduction compared to the traditional BP algorithm’s requirement of over 4000 iterations—while improving the average network operation speed by an impressive 89.76%. Additionally, our algorithm excels in image quality, with peak signal-to-noise ratio (PSNR) improving by 92.7% and SSIM by 42.1%, at most, compared to other algorithms. These results establish our algorithm as a highly efficient and effective solution, suitable for various image processing and data compression applications.

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Extended Kalman Filter State Estimation for Aerial Continuum Manipulation Systems

Cited by 7 publications

References 17 publications

Tendon-Driven Continuum Robots for Aerial Manipulation—A Survey of Fabrication Methods

Tendon-Driven Continuum Robots for Aerial Manipulation—A Survey of Fabrication Methods

Robust State Estimation Method Based on Mahalanobis Distance Under Non-Gauss Noise

Image Splicing Compression Algorithm Based on the Extended Kalman Filter for Unmanned Aerial Vehicles Communication

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