Fuzzy Control Algorithm for Estimation and Interaction of Dynamic Arm Motion

2020 ◽  
Vol 13 (1) ◽  
pp. 99-104
Author(s):  
Mais A. Al-Sharqi ◽  
Haitham S. Hasan
Keyword(s):  
Temporal Consistency ◽  
Position Information ◽  
Arm Position ◽  
Written Form ◽  
Kalman Filter Algorithm ◽  
Arm Motion ◽  
The Stability ◽  
Fuzzy Control Algorithm ◽  
Fuzzy Predictive Control ◽  
Multiple Display

Background: Significant work has been conducted in the direction of an intelligent interface development through Human-Computer Interaction (HCI). Various forms of information, such as video, audio, or in the written form, have been proposed either separately or in combination. Methods: This paper proposes an interactive contact solution based on the distinct characteristics of contract distribution and the spatial and temporal consistency to establish a multiple display system. Results: The correspondence between the user’s arm position information and the virtual scene was established by utilizing a virtual 3D interactive rectangular parallelepiped. An estimation technique of the arm motion was designed, in conjunction with the employment of the Fuzzy Predictive Control Mamdani Algorithm (FPCMA) using Robust Tracking (RT) for the user’s arm position and for validating the efficiency and accuracy, Kalman filter algorithm (VEA) was used to test the stability. Conclusion: For RT, using FPCMA is 1.21 for 17fps while 0.83 for 35fps. For the output, the VEA accuracy rate is 0.97.

Visual Memory for Objects in Natural Scenes: From Fixations to Object Files

2005 ◽  
Vol 58 (5) ◽  
pp. 931-960 ◽  
Author(s):  
Benjamin W. Tatler ◽  
Iain D. Gilchrist ◽  
Michael F. Land
Keyword(s):  
Real World ◽  
Visual Memory ◽  
Natural Scenes ◽  
Position Information ◽  
Object Property ◽  
Immediate Recall ◽  
Object Files ◽  
Stability Data ◽  
The Stability ◽  
Types Of Information

Object descriptions are extracted and retained across saccades when observers view natural scenes. We investigated whether particular object properties are encoded and the stability of the resulting memories. We tested immediate recall of multiple types of information from real-world scenes and from computer-presented images of the same scenes. The relationship between fixations and properties of object memory was investigated. Position information was encoded and accumulated from multiple fixations. In contrast, identity and colour were encoded but did not require direct fixation and did not accumulate. In the current experiments, participants were unable to recall any information about shape or relative distances between objects. In addition, where information was encoded we found differential patterns of stability. Data from viewing real scenes and images were highly consistent, with stronger effects in the real-world conditions. Our findings imply that object files are not dependent upon the encoding of any particular object property and so are robust to dynamic visual environments.

scholarly journals Two Electrode ECG And EOG System For Monitoring Applications

2019 ◽  
Vol 8 (11) ◽  
pp. 262-265
Keyword(s):  
Reference Electrode ◽  
Cost Effective ◽  
Electrode System ◽  
Ecg Signal ◽  
Biomedical Signal ◽  
Ecg Signals ◽  
Arm Position ◽  
The Stability ◽  
Electrocardiogram Ecg ◽  
Total Gain

Heart and Eye are two vital organs in the human system. By knowing the Electrocardiogram (ECG) and Electro-oculogram (EOG), one will be able to tell the stability of the heart and eye respectively. In this project, we have developed a circuit to pick the ECG and EOG signal using two wet electrodes. Here no reference electrode is used. EOG and ECG signals have been acquired from ten healthy subjects. The ECG signal is obtained from two positions, namely wrist and arm position respectively. The picked-up biomedical signal is recorded and heart rate information is extracted from ECG signal using the biomedical workbench. The result found to be promising and acquired stable EOG and ECG signal from the subjects. The total gain required for the arm position is higher than the wrist position for the ECG signal. The total gain necessary for the EOG signal is higher than the ECG signal since the ECG signal is in the range of millivolts whereas EOG signal in the range of microvolts. This two-electrode system is stable, cost-effective and portable while still maintaining high common-mode rejection ratio (CMRR).

scholarly journals Center of Pressure Feedback for Controlling the Walking Stability Bipedal Robots using Fuzzy Logic Controller

2018 ◽  
Vol 8 (5) ◽  
pp. 3678
Author(s):  
Afrizal Mayub ◽  
Fahmizal Fahmizal
Keyword(s):  
Fuzzy Logic ◽  
Degrees Of Freedom ◽  
Fuzzy Logic Controller ◽  
Center Of Pressure ◽  
Bipedal Robots ◽  
Bipedal Robot ◽  
Position Information ◽  
Pressure Feedback ◽  
The Stability ◽  
Foot Pad

This paper presents a sensor-based stability walk for bipedal robots by using force sensitive resistor (FSR) sensor. To perform walk stability on uneven terrain conditions, FSR sensor is used as feedbacks to evaluate the stability of bipedal robot instead of the center of pressure (CoP). In this work, CoP that was generated from four FSR sensors placed on each foot-pad is used to evaluate the walking stability. The robot CoP position provided an indication of walk stability. The CoP position information was further evaluated with a fuzzy logic controller (FLC) to generate appropriate offset angles to be applied to meet a stable situation. Moreover, in this paper designed a FLC through CoP region's stability and stable compliance control are introduced. Finally, the performances of the proposed methods were verified with 18-degrees of freedom (DOF) kid-size bipedal robot.<br /><br />

Research on Adaptive Kalman Filter for INS

2017 ◽  
Vol 872 ◽  
pp. 316-320
Author(s):  
Kai Xia Wei
Keyword(s):  
Kalman Filter ◽  
Response Speed ◽  
Measured Data ◽  
Specific Method ◽  
Alignment Error ◽  
Initial Alignment ◽  
Adaptive Kalman Filter ◽  
Noise Interference ◽  
Kalman Filter Algorithm ◽  
The Stability

Due to sensor accuracy and noise interference and other reasons, the measured data may be inaccurate or even wrong. This will reduce the accuracy of the filter and the reliability and response speed of the Kalman filter, and even make the Kalman filter lose the stability. In this paper, a new INS initial alignment error model and observation model are derived for the errors in INS initial alignment. The adaptive Kalman filter is built to improve the stability and the accuracy of filter. The specific method is to make the adaptive Kalman filter manage to correct the filter online by getting the observed data. The simulation results show the proposed algorithm improves the accuracy of initial alignment in SINS, and prove the adaptive Kalman filter is effective. The main innovation in this paper is to manage to build the adaptive Kalman filter to modify the filter online by using the observed data. The adaptive Kalman filter algorithm improves the accuracy of the filter.

scholarly journals Pose Prediction of Autonomous Full Tracked Vehicle Based on 3D Sensor

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5120 ◽  
Author(s):  
Tao Ni ◽  
Wenhang Li ◽  
Hongyan Zhang ◽  
Haojie Yang ◽  
Zhifei Kong
Keyword(s):  
Real Time ◽  
Autonomous Vehicles ◽  
Experimental Result ◽  
Pose Prediction ◽  
Tracked Vehicle ◽  
Position Information ◽  
Cloud Data ◽  
Uneven Terrain ◽  
The Stability ◽  
Safety And Stability

Autonomous vehicles can obtain real-time road information using 3D sensors. With road information, vehicles avoid obstacles through real-time path planning to improve their safety and stability. However, most of the research on driverless vehicles have been carried out on urban even driveways, with little consideration of uneven terrain. For an autonomous full tracked vehicle (FTV), the uneven terrain has a great impact on the stability and safety. In this paper, we proposed a method to predict the pose of the FTV based on accurate road elevation information obtained by 3D sensors. If we could predict the pose of the FTV traveling on uneven terrain, we would not only control the active suspension system but also change the driving trajectory to improve the safety and stability. In the first, 3D laser scanners were used to get real-time cloud data points of the terrain for extracting the elevation information of the terrain. Inertial measurement units (IMUs) and GPS are essential to get accurate attitude angle and position information. Then, the dynamics model of the FTV was established to calculate the vehicle’s pose. Finally, the Kalman filter was used to improve the accuracy of the predicted pose. Compared to the traditional method of driverless vehicles, the proposed approach was more suitable for autonomous FTV. The real-world experimental result demonstrated the accuracy and effectiveness of our approach.

scholarly journals Stability and variation in office chants of the Sarum Sanctorale

2018 ◽  
Vol 27 (1) ◽  
pp. 1-26 ◽  
Author(s):  
MATTHEW CHEUNG SALISBURY
Keyword(s):  
Considerable Degree ◽  
Multiple Sources ◽  
Late Medieval ◽  
Written Form ◽  
The Stability ◽  
The Relationship

ABSTRACTThis article uses multiple witnesses of the chants from four offices of the Sanctorale, transcribed from twelve manuscripts and an early printed antiphonal, in order to assess the stability of chants in late medieval sources associated with the liturgical ‘Use of Sarum’. Whilst there is usually a ‘main’ melodic reading or version for each chant, a considerable degree of variation exists among the readings from various witnesses. The data which support this argument allow manuscripts to be linked by networks of shared melodic material, both through melodic readings identical and present in multiple sources, and through divergences from such main versions. These observations help to illuminate something of the diversity of the written melodic tradition, raising wider questions about the relationship between written witness and performed reality, and about the fixity of ‘Sarum Use’, at least as far as it was transmitted in written form.

An Improved Adaptive Algorithm for INS/GPS System

2013 ◽  
Vol 397-400 ◽  
pp. 1606-1610 ◽  
Author(s):  
Li Dong Wang ◽  
Ying Zhao ◽  
Ni Zhang
Keyword(s):  
Adaptive Filtering ◽  
Adaptive Filter ◽  
Adaptive Algorithm ◽  
Initial Conditions ◽  
Forgetting Factor ◽  
Kalman Filter Algorithm ◽  
Simulation Results ◽  
The Stability ◽  
Gps System

In INS/GPS system, the changing of initial conditions and the quality of the data can affect the convergence of the conventional Kalman filter algorithm. Sage-Husa adaptive filter algorithm is adopted in the INS/GPS system in this paper. The effecting of the forgetting factor to the improved Sage-Husa adaptive filter algorithm is studied and the simulation results show that when the forgetting factor taken near 0.97, the adaptive filtering result is best, the stability of the system is guaranteed and the convergent speed of error can be reduced.

scholarly journals REAL TIME SEARCH OF AGRICULTURAL MACHINERY BASED ON MATRIX SEQUENCE SENSOR

2021 ◽  
pp. 425-433
Author(s):  
Jiaxin Zheng ◽  
Yanyu Gao ◽  
Zhengdong Lei ◽  
Changhu Yang ◽  
Chongjin Wang ◽  
...  
Keyword(s):  
Spread Spectrum ◽  
Three Dimensional ◽  
Agricultural Machinery ◽  
Position Information ◽  
Coordinate Position ◽  
Matrix Sequence ◽  
Node Location ◽  
Anchor Nodes ◽  
Kalman Filter Algorithm ◽  
Positioning Algorithm

Omni-directional vision sensor can provide information within the sensor range, and the directional angle of an object can be accurately obtained through omni-directional images. Based on this characteristic, an automatic navigation and positioning system for agricultural machinery is developed, and a three-dimensional positioning algorithm for agricultural wireless sensor networks based on cross particle swarm optimization is proposed. The method mainly includes three stages: convergence node selection, measurement distance correction and node location. Using the idea of crossover operation of genetic algorithm for reference, the diversity of particles is increased, and the influence of ranging error and the number of anchor nodes on positioning results is effectively improved. The location algorithm has the ability of global search. On the positioning node, the symmetric bidirectional ranging algorithm based on LFM (Linear frequency modulation) spread spectrum technology is used to calculate the distance between the positioning node and each beacon node, and the trilateral centroid positioning algorithm is used to calculate the coordinate position information of unknown nodes. Finally, the Kalman filter algorithm is used to superimpose the observed values of the target state to solve the influence of measurement noise on the positioning accuracy.

scholarly journals Cooperative Visual-SLAM System for UAV-Based Target Tracking in GPS-Denied Environments: A Target-Centric Approach

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 813 ◽  
Author(s):  
Juan-Carlos Trujillo ◽  
Rodrigo Munguia ◽  
Sarquis Urzua ◽  
Antoni Grau
Keyword(s):  
Position Estimation ◽  
Lyapunov Theory ◽  
Fundamental Aspect ◽  
Control Laws ◽  
Position Information ◽  
Practical Applications ◽  
Aerial Robots ◽  
Localization And Mapping ◽  
Flight Formation ◽  
The Stability

Autonomous tracking of dynamic targets by the use of Unmanned Aerial Vehicles (UAVs) is a challenging problem that has practical applications in many scenarios. In this context, a fundamental aspect that must be addressed has to do with the position estimation of aerial robots and a target to control the flight formation. For non-cooperative targets, their position must be estimated using the on-board sensors. Moreover, for estimating the position of UAVs, global position information may not always be available (GPS-denied environments). This work presents a cooperative visual-based SLAM (Simultaneous Localization and Mapping) system that allows a team of aerial robots to autonomously follow a non-cooperative target moving freely in a GPS-denied environment. One of the contributions of this work is to propose and investigate the use of a target-centric SLAM configuration to solve the estimation problem that differs from the well-known World-centric and Robot-centric SLAM configurations. In this sense, the proposed approach is supported by theoretical results obtained from an extensive nonlinear observability analysis. Additionally, a control system is proposed for maintaining a stable UAV flight formation with respect to the target as well. In this case, the stability of control laws is proved using the Lyapunov theory. Employing an extensive set of computer simulations, the proposed system demonstrated potentially to outperform other related approaches.

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