scholarly journals INTEGRATED NAVIGATION METHOD OF ELECTRIC FORKLIFT BASED ON IMPROVED UKF ALGORITHM

2021 ◽  
pp. 505-515
Author(s):  
Yibo Li ◽  
Shipeng Zhu
Keyword(s):  
Position Estimation ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Integrated Navigation ◽  
Factors Affecting ◽  
Robot Positioning ◽  
Measurement Units ◽  
Robot Position ◽  
And Storage ◽  
Electric Forklift

Aiming at the problem of inaccurate navigation and positioning of electric forklifts in a complex environment with multiple placement racks when carrying stored crops in a warehouse, this paper proposes a combined navigation and positioning system based on information fusion of LiDAR and inertial measurement units. The method proposed in this paper improves the traditional EKF algorithm by introducing factors affecting the prior covariance matrix and changing the weights of processing old and new data in the filtering equation to achieve the desired goal of suppressing system dispersion and to accomplish accurate estimation of the position of electric forklifts in the storage room. The simulation of robot positioning and navigation in indoor environment shows that the improved algorithm improves the position estimation accuracy by about 30% compared with the traditional algorithm, the new algorithm can effectively improve the efficiency of electric forklift for handling and storage, and it can ensure the robustness of robot position estimation.

scholarly journals Simultaneous Localization and Mapping of Mobile Robot Based on Improved RBPF

2018 ◽  
Vol 160 ◽  
pp. 06002
Author(s):  
Jinging Zhang ◽  
Xiaogang Ruan ◽  
Pengfei Dong ◽  
Jing Zhou
Keyword(s):  
Mobile Robot ◽  
Position Estimation ◽  
Estimation Accuracy ◽  
Bacterial Foraging Optimization ◽  
The Real ◽  
Bacterial Foraging ◽  
Operation Speed ◽  
Localization And Mapping ◽  
Bacterial Foraging Algorithm ◽  
Robot Position

The traditional SLAM based on RBPF has the problem of constructing high-precision map which requires large amounts of particles to make the calculation complexity and the phenomenon of particle depletion caused by particle degradation. Aiming at these problems, an improved RBPF particle filter based on adaptive bacterial foraging optimization algorithm and adaptive resampling is proposed for mobile robot SLAM problem. Firstly, the introduction of adaptive bacterial foraging algorithm to RBPF making the distribution of particles before resampling closer to the real situation. Then use the adaptive resampling method makes the newly generated particles closer to the real movement, thereby increasing the robot position estimation accuracy and map creation accuracy. The experimental results show that this method can improve the practicability of the system, reduce the computational complexity, improve the operation speed and get more effective particles while guaranteeing the accuracy of the grid map.

Experimental research of indoor navigation system based on broadcast signals

2020 ◽  
Vol 49 (5) ◽  
pp. 49-57
Author(s):  
A. V. Ksendzuk ◽  
E. A. Surmin ◽  
V. V. Kachesov ◽  
S. O. Zhdanov ◽  
K. S. Shakhalov
Keyword(s):  
Navigation System ◽  
Open Space ◽  
Position Estimation ◽  
Indoor Navigation ◽  
Estimation Accuracy ◽  
Satellite Systems ◽  
Indoor Navigation System ◽  
Point To Point ◽  
Navigation Signals ◽  
Navigation Equipment

Results of an experimental study of a local navigation system based on the processing signals from broadcast sources presented. The results of the development of processing algorithms for point-to-point coordinates estimation of the object are presented. The results of the development of algorithms for trajectories estimation are presented. In performed simulation the possibility of obtaining submeter position estimation accuracy in the proposed system is shown. Development results of the navigation module demonstrator are presented. The results of experimental work in difficult navigation conditions, in the presence of shading, reflections and other factors, are presented. It is shown that the developed navigation module allows in the open space near buildings which partially obscuring the satellite systems signals to obtain accuracy higher than the GNSS navigation equipment. In indoor environment in the absence of satellite navigation signals, the developed module shows positioning accuracy not worse than 1.5 meters and provides a measurement rate 1 Hz and better.

scholarly journals Using Different Combinations of Body-Mounted IMU Sensors to Estimate Speed of Horses—A Machine Learning Approach

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 798
Author(s):  
Hamed Darbandi ◽  
Filipe Serra Bragança ◽  
Berend Jan van der Zwaag ◽  
John Voskamp ◽  
Annik Imogen Gmel ◽  
...  
Keyword(s):  
Machine Learning ◽  
The Body ◽  
Biomechanical Analysis ◽  
Estimation Accuracy ◽  
Speed Estimation ◽  
Motion Patterns ◽  
Positioning Systems ◽  
Machine Learning Approach ◽  
Global Positioning ◽  
Measurement Units

Speed is an essential parameter in biomechanical analysis and general locomotion research. It is possible to estimate the speed using global positioning systems (GPS) or inertial measurement units (IMUs). However, GPS requires a consistent signal connection to satellites, and errors accumulate during IMU signals integration. In an attempt to overcome these issues, we have investigated the possibility of estimating the horse speed by developing machine learning (ML) models using the signals from seven body-mounted IMUs. Since motion patterns extracted from IMU signals are different between breeds and gaits, we trained the models based on data from 40 Icelandic and Franches-Montagnes horses during walk, trot, tölt, pace, and canter. In addition, we studied the estimation accuracy between IMU locations on the body (sacrum, withers, head, and limbs). The models were evaluated per gait and were compared between ML algorithms and IMU location. The model yielded the highest estimation accuracy of speed (RMSE = 0.25 m/s) within equine and most of human speed estimation literature. In conclusion, highly accurate horse speed estimation models, independent of IMU(s) location on-body and gait, were developed using ML.

scholarly journals Online State-of-Charge Estimation Based on the Gas–Liquid Dynamics Model for Li(NiMnCo)O2 Battery

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 324
Author(s):  
Haobin Jiang ◽  
Xijia Chen ◽  
Yifu Liu ◽  
Qian Zhao ◽  
Huanhuan Li ◽  
...  
Keyword(s):  
Stress Test ◽  
Robustness Analysis ◽  
Open Circuit ◽  
Combined Cycle ◽  
State Of Charge ◽  
Constant Current ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Battery Management System ◽  
Liquid Dynamics

Accurately estimating the online state-of-charge (SOC) of the battery is one of the crucial issues of the battery management system. In this paper, the gas–liquid dynamics (GLD) battery model with direct temperature input is selected to model Li(NiMnCo)O2 battery. The extended Kalman Filter (EKF) algorithm is elaborated to couple the offline model and online model to achieve the goal of quickly eliminating initial errors in the online SOC estimation. An implementation of the hybrid pulse power characterization test is performed to identify the offline parameters and determine the open-circuit voltage vs. SOC curve. Apart from the standard cycles including Constant Current cycle, Federal Urban Driving Schedule cycle, Urban Dynamometer Driving Schedule cycle and Dynamic Stress Test cycle, a combined cycle is constructed for experimental validation. Furthermore, the study of the effect of sampling time on estimation accuracy and the robustness analysis of the initial value are carried out. The results demonstrate that the proposed method realizes the accurate estimation of SOC with a maximum mean absolute error at 0.50% in five working conditions and shows strong robustness against the sparse sampling and input error.

scholarly journals Development of a Deep Neural Network Model for Estimating Joint Location of Occupant Indoor Activities for Providing Thermal Comfort

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 696
Author(s):  
Eun Ji Choi ◽  
Jin Woo Moon ◽  
Ji-hoon Han ◽  
Yongseok Yoo
Keyword(s):  
Neural Network ◽  
Thermal Comfort ◽  
Deep Neural Network ◽  
Mean Squared Error ◽  
Thermal Environment ◽  
The Body ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Body Parts ◽  
Joint Location

The type of occupant activities is a significantly important factor to determine indoor thermal comfort; thus, an accurate method to estimate occupant activity needs to be developed. The purpose of this study was to develop a deep neural network (DNN) model for estimating the joint location of diverse human activities, which will be used to provide a comfortable thermal environment. The DNN model was trained with images to estimate 14 joints of a person performing 10 common indoor activities. The DNN contained numerous shortcut connections for efficient training and had two stages of sequential and parallel layers for accurate joint localization. Estimation accuracy was quantified using the mean squared error (MSE) for the estimated joints and the percentage of correct parts (PCP) for the body parts. The results show that the joint MSEs for the head and neck were lowest, and the PCP was highest for the torso. The PCP for individual activities ranged from 0.71 to 0.92, while typing and standing in a relaxed manner were the activities with the highest PCP. Estimation accuracy was higher for relatively still activities and lower for activities involving wide-ranging arm or leg motion. This study thus highlights the potential for the accurate estimation of occupant indoor activities by proposing a novel DNN model. This approach holds significant promise for finding the actual type of occupant activities and for use in target indoor applications related to thermal comfort in buildings.

scholarly journals CONCURRENT MAP BUILDING AND LOCALIZATION ON INDOOR DYNAMIC ENVIRONMENTS

2002 ◽  
Vol 16 (03) ◽  
pp. 361-374 ◽  
Author(s):  
JUAN ANDRADE-CETTO ◽  
ALBERTO SANFELIU
Keyword(s):  
Learning Rule ◽  
Dynamic Environments ◽  
Position Estimation ◽  
Indoor Environments ◽  
Map Building ◽  
Robust Learning ◽  
Map Construction ◽  
Map Updating ◽  
Dynamic Map ◽  
Robot Position

A system that builds and maintains a dynamic map for a mobile robot is presented. A learning rule associated to each observed landmark is used to compute its robustness. The position of the robot during map construction is estimated by combining sensor readings, motion commands, and the current map state by means of an Extended Kalman Filter. The combination of landmark strength validation and Kalman filtering for map updating and robot position estimation allows for robust learning of moderately dynamic indoor environments.

scholarly journals Experimental Comparison between Event and Global Shutter Cameras

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1137
Author(s):  
Ondřej Holešovský ◽  
Radoslav Škoviera ◽  
Václav Hlaváč ◽  
Roman Vítek
Keyword(s):  
High Speed ◽  
Ground Truth ◽  
Motion Blur ◽  
Position Estimation ◽  
Estimation Accuracy ◽  
Experimental Comparison ◽  
Estimation Errors ◽  
Detection Rates ◽  
Ballistic Experiment ◽  
Event Camera

We compare event-cameras with fast (global shutter) frame-cameras experimentally, asking: “What is the application domain, in which an event-camera surpasses a fast frame-camera?” Surprisingly, finding the answer has been difficult. Our methodology was to test event- and frame-cameras on generic computer vision tasks where event-camera advantages should manifest. We used two methods: (1) a controlled, cheap, and easily reproducible experiment (observing a marker on a rotating disk at varying speeds); (2) selecting one challenging practical ballistic experiment (observing a flying bullet having a ground truth provided by an ultra-high-speed expensive frame-camera). The experimental results include sampling/detection rates and position estimation errors as functions of illuminance and motion speed; and the minimum pixel latency of two commercial state-of-the-art event-cameras (ATIS, DVS240). Event-cameras respond more slowly to positive than to negative large and sudden contrast changes. They outperformed a frame-camera in bandwidth efficiency in all our experiments. Both camera types provide comparable position estimation accuracy. The better event-camera was limited by pixel latency when tracking small objects, resulting in motion blur effects. Sensor bandwidth limited the event-camera in object recognition. However, future generations of event-cameras might alleviate bandwidth limitations.

Robust sideslip angle observer of commercial vehicles based on cornering stiffness estimation using neural network

2022 ◽  
pp. 095440702110724
Author(s):  
Chenyu Zhou ◽  
Liangyao Yu ◽  
Yong Li ◽  
Jian Song
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Stability Control ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Commercial Vehicles ◽  
Sideslip Angle ◽  
The Neural Network ◽  
Wide Range ◽  
The Mathematical Model

Accurate estimation of sideslip angle is essential for vehicle stability control. For commercial vehicles, the estimation of sideslip angle is challenging due to severe load transfer and tire nonlinearity. This paper presents a robust sideslip angle observer of commercial vehicles based on identification of tire cornering stiffness. Since tire cornering stiffness of commercial vehicles is greatly affected by tire force and road adhesion coefficient, it cannot be treated as a constant. To estimate the cornering stiffness in real time, the neural network model constructed by Levenberg-Marquardt backpropagation (LMBP) algorithm is employed. LMBP is a fast convergent supervised learning algorithm, which combines the steepest descent method and gauss-newton method, and is widely used in system parameter estimation. LMBP does not rely on the mathematical model of the actual system when building the neural network. Therefore, when the mathematical model is difficult to establish, LMBP can play a very good role. Considering the complexity of tire modeling, this study adopted LMBP algorithm to estimate tire cornering stiffness, which have simplified the tire model and improved the estimation accuracy. Combined with neural network, A time-varying Kalman filter (TVKF) is designed to observe the sideslip angle of commercial vehicles. To validate the feasibility of the proposed estimation algorithm, multiple driving maneuvers under different road surface friction have been carried out. The test results show that the proposed method has better accuracy than the existing algorithm, and it’s robust over a wide range of driving conditions.

On the estimation of optimal state-of-charge trajectory for plug-in hybrid electric buses using trip information

2021 ◽  
pp. 095440702110410
Author(s):  
Naser Sina ◽  
Vahid Esfahanian ◽  
Mohammad Reza Hairi Yazdi
Keyword(s):  
Fuel Economy ◽  
Dynamic Programming Algorithm ◽  
Optimal Solution ◽  
State Of Charge ◽  
Estimation Accuracy ◽  
Programming Algorithm ◽  
Factors Affecting ◽  
Optimal State ◽  
Energy Management Systems ◽  
Hybrid Electric

Plug-in hybrid electric buses are a viable solution to increase the fuel economy. In this framework, precise estimation of optimal state-of-charge trajectory along the upcoming driving cycle appears to play a pivotal role in the way to approach the globally optimal fuel economy. This paper aims to conduct a parametric study on the key factors affecting the estimation of optimal state-of-charge trajectory, including trip information availability and trip segment distance, and to provide a guideline for the design and implementation of predictive energy management systems. To accomplish this, the dynamic programming algorithm is employed to obtain the solution of optimal control problem for the sampled driving cycles in a particular bus route. A large database comprising of driving features of the cycles and the optimal solution is developed which then is used to construct a neural network based estimator for obtaining the optimal state-of-charge trajectory. The main results show promising performance of the proposed method with about 76% reduction in the root mean square error of the estimated trajectory comparing to the linear state-of-charge trajectory assumption. Moreover, the robustness of the estimator is verified through simulation and it is observed that appropriate choice of trip segment distance is vital to improve the estimation accuracy, especially in case of uncertain prediction of trip information.

scholarly journals Contact Analysis of a Dry Friction Clutch System

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Oday I. Abdullah ◽  
Josef Schlattmann
Keyword(s):  
Pressure Distribution ◽  
Contact Stiffness ◽  
Load Condition ◽  
Accurate Estimation ◽  
Factors Affecting ◽  
Clutch System ◽  
System Pressure ◽  
Friction Clutch ◽  
Contact Surfaces ◽  
Dry Friction Clutch

The numerical simulation of the friction clutch system (pressure plate, clutch disc, and flywheel) during the full engagement period (assuming no slipping between contact surfaces) is carried out using finite element method. Two types of load condition considered affect on the clutch elements during the full engagement period are the contact pressure of diaphragm spring and the centrifugal force. The study of the pressure distribution between the contact surfaces and the factors affecting it is one of the fundamentals in the process of designing the friction clutch to obtain accurate estimation of the temperature distribution during the slipping period and the contact stresses during the full engagement period. The investigation covers the effect of the contact stiffness factor FKN on the pressure distribution between contact surfaces, stresses, and penetration. The penalty and augmented Lagrange algorithms have been used to obtain the pressure distribution between contact surfaces. ANSYS13 software has been used to perform the numerical calculation in this paper.

Sign in / Sign up

Export Citation Format

Share Document