scholarly journals Sensor Fusion Algorithm Using a Model-Based Kalman Filter for the Position and Attitude Estimation of Precision Aerial Delivery Systems

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5227
Author(s):  
Raul A. Garcia-Huerta ◽  
Luis E. González-Jiménez ◽  
Ivan E. Villalon-Turrubiates
Keyword(s):  
Kalman Filter ◽  
Sensor Fusion ◽  
Linear Models ◽  
Flight Dynamics ◽  
Delivery Systems ◽  
Fusion Algorithm ◽  
Model Based ◽  
Wide Range ◽  
Estimation Scheme ◽  
Double Integrator

In this research, we focus on the use of Unmanned Aerial Vehicles (UAVs) for the delivery of payloads and navigation towards safe-landing zones, specifically on the modeling of flight dynamics of lightweight vehicles denoted Precision Aerial Delivery Systems (PADSs). While a wide range of nonlinear models has been developed and tested on high-end applications considering various degrees of freedom (DOF), linear models suitable for low-cost applications have not been explored thoroughly. In this study, we propose and compare two linear models, a linearized version of a 6-DOF model specifically developed for micro-lightweight systems, and an alternative model based on a double integrator. Both linear models are implemented with a sensor fusion algorithm using a Kalman filter to estimate the position and attitude of PADSs, and their performance is compared to a nonlinear 6-DOF model. Simulation results demonstrate that both models, when incorporated into a Kalman filter estimation scheme, can determine the flight dynamics of PADSs during smooth flights. While it is validated that the double integrator model can adequately operate under the proposed estimation scheme for up to small acceleration changes, the linearized model proves to be capable of reproducing the nonlinear model characteristics even during moderately steep turns.

scholarly journals Ship-to-Ship State Observer Using Sensor Fusion and the Extended Kalman Filter

2019 ◽  
Vol 141 (4) ◽  
Author(s):  
Sondre Sanden Tørdal ◽  
Geir Hovland
Keyword(s):  
Kalman Filter ◽  
Sensor Fusion ◽  
Extended Kalman Filter ◽  
Relative Motion ◽  
Degrees Of Freedom ◽  
Load Transfer ◽  
Real Life ◽  
Fusion Algorithm ◽  
Six Degrees Of Freedom ◽  
Motion Estimate

In this paper, a solution for estimating the relative position and orientation between two ships in six degrees-of-freedom (6DOF) using sensor fusion and an extended Kalman filter (EKF) approach is presented. Two different sensor types, based on time-of-flight and inertial measurement principles, were combined to create a reliable and redundant estimate of the relative motion between the ships. An accurate and reliable relative motion estimate is expected to be a key enabler for future ship-to-ship operations, such as autonomous load transfer and handling. The proposed sensor fusion algorithm was tested with real sensors (two motion reference units (MRS) and a laser tracker) and an experimental setup consisting of two Stewart platforms in the Norwegian Motion Laboratory, which represents an approximate scale of 1:10 when compared to real-life ship-to-ship operations.

A Comparison of SLAM Prediction Densities Using the Kolmogorov Smirnov Statistic

2016 ◽  
Vol 04 (04) ◽  
pp. 245-254
Author(s):  
Akshay Rao ◽  
Wang Han ◽  
P. G. C. N. Senarathne
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Sensor Fusion ◽  
Unscented Kalman Filter ◽  
Fusion Algorithm ◽  
Central Difference ◽  
Localization Algorithms ◽  
Kolmogorov Smirnov ◽  
Prediction Density ◽  
Smirnov Statistic

Accurate pose and trajectory estimates, are necessary components of autonomous robot navigation system. A wide variety of Simultaneous Localization and Mapping (SLAM) and localization algorithms have been developed by the robotics community to cater to this requirement. Some of the sensor fusion algorithms employed by SLAM and localization algorithms include the particle filter, Gaussian Particle Filter, the Extended Kalman Filter, the Unscented Kalman Filter, and the Central Difference Kalman Filter. To guarantee a rapid convergence of the state estimate to the ground truth, the prediction density of the sensor fusion algorithm must be as close to the true vehicle prediction density as possible. This paper presents a Kolmogorov–Smirnov statistic-based method to compare the prediction densities of the algorithms listed above. The algorithms are compared using simulations of noisy inputs provided to an autonomous robotic vehicle, and the obtained results are analyzed. The results are then validated using data obtained from a robot moving in controlled trajectories similar to the simulations.

scholarly journals Enhanced location tracking in sensor fusion-assisted virtual reality micro-manipulation environments

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0261933
Author(s):  
John David Prieto Prada ◽  
Jintaek Im ◽  
Hyondong Oh ◽  
Cheol Song
Keyword(s):  
Virtual Reality ◽  
Kalman Filter ◽  
Sensor Fusion ◽  
Human Subjects ◽  
Great Accuracy ◽  
Small Scale ◽  
Location Tracking ◽  
Fusion Algorithm ◽  
Measuring Device ◽  
Positioning Errors

Virtual reality (VR) technology plays a significant role in many biomedical applications. These VR scenarios increase the valuable experience of tasks requiring great accuracy with human subjects. Unfortunately, commercial VR controllers have large positioning errors in a micro-manipulation task. Here, we propose a VR-based framework along with a sensor fusion algorithm to improve the microposition tracking performance of a microsurgical tool. To the best of our knowledge, this is the first application of Kalman filter in a millimeter scale VR environment, by using the position data between the VR controller and an inertial measuring device. This study builds and tests two cases: (1) without sensor fusion tracking and (2) location tracking with active sensor fusion. The static and dynamic experiments demonstrate that the Kalman filter can provide greater precision during micro-manipulation in small scale VR scenarios.

Asynchronous Multi-Sensor Fusion Algorithm Based on the Steady-State Kalman Filter

2014 ◽  
Vol 490-491 ◽  
pp. 781-788
Author(s):  
Hui Ma ◽  
Xian Fei Liu
Keyword(s):  
Kalman Filter ◽  
Steady State ◽  
Sensor Fusion ◽  
Estimation Error ◽  
Minimum Mean Square Error ◽  
Fusion Center ◽  
Fusion Algorithm ◽  
Error Covariance ◽  
Optimal Information ◽  
Optimal Fusion

The paper studies an asynchronous multi-sensor fusion problem based a kind of asynchronous multi-sensor dynamic system. Firstly, this paper presents a centralized fusion algorithm based on the Kalman filter without ignoring the correlation between process noise and augmented measurement noise. It is optimal in minimum mean square error. Then using the steady-state Kalman filter to estimate and fuse. Secondly, in the condition that the local sensor estimation error is associated, a distributed fusion algorithm is given by utilizing S.L. Sun optimal information fusion criterion in minimum error covariance matrix trace at fusion center. In distributed algorithm, the value transmitting to the fusion center is determined by the local sensor estimation based on the steady-state Kalman filter and one step predictive value. Since both optimal fusion algorithm standards are different, so the fusion precision will vary. Finally the effectiveness of the algorithm is verified by computer simulation.

Development and Evaluation of Sustained Release Lipid Nanocarriers for Curcumin

2020 ◽  
Vol 5 (3) ◽  
pp. 224-235
Author(s):  
Harshal A. Pawar ◽  
Bhagyashree D. Bhangale
Keyword(s):  
Drug Delivery ◽  
Sustained Release ◽  
Drug Delivery Systems ◽  
Biological Activities ◽  
Curcuma Longa ◽  
Research Work ◽  
Elimination Rate ◽  
Delivery Systems ◽  
Physicochemical Stability ◽  
Wide Range

Background: Lipid based excipients have increased acceptance nowadays in the development of novel drug delivery systems in order to improve their pharmacokinetic profiles. Drugs encapsulated in lipids have enhanced stability due to the protection they experience in the lipid core of these nano-formulations. Phytosomes are newly discovered drug delivery systems and novel botanical formulation to produce lipophilic molecular complex which imparts stability, increases absorption and bioavailability of phytoconstituent. Curcumin, obtained from turmeric (Curcuma longa), has a wide range of biological activities. The poor solubility and wettability of curcumin are responsible for poor dissolution and this, in turn, results in poor bioavailability. To overcome these limitations, the curcumin-loaded nano phytosomes were developed to improve its physicochemical stability and bioavailability. Objective: The objective of the present research work was to develop nano-phytosomes of curcumin to improve its physicochemical stability and bioavailability. Methods: Curcumin-loaded nano phytosomes were prepared by using phospholipid Phospholipon 90 H using a modified solvent evaporation method. The developed curcumin nano phytosomes were evaluated by particle size analyzer and differential scanning calorimetry (DSC). Results: Results indicated that phytosomes prepared using curcumin and lipid in the ratio of 1:2 show good entrapment efficiency. The obtained curcumin phytosomes were spherical in shape with a size less than 100 nm. The prepared nano phytosomal formulation of curcumin showed promising potential as an antioxidant. Conclusion: The phytosomal complex showed sustained release of curcumin from vesicles. The sustained release of curcumin from phytosome may improve its absorption and lowers the elimination rate with an increase in bioavailability.

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