Determination of Motor Torque for Power-Assist Electric Bicycles Using Observer-Based Sensor Fusion

2018 ◽  
Vol 140 (7) ◽  
Author(s):  
Chun-Feng Huang ◽  
Bang-Hao Dai ◽  
T.-J. Yeh
Keyword(s):  
Sensor Fusion ◽  
Kinematic Model ◽  
Slope Angle ◽  
Measurement Unit ◽  
Time Varying ◽  
Fusion Algorithm ◽  
Estimation Errors ◽  
Motor Torque ◽  
Observer Model ◽  
Electric Bicycles

This paper proposes a sensor fusion algorithm to determine the motor torque for power-assist electric bicycles. Instead of using torque sensors to directly measure the pedaling torque, outputs from a wheel encoder and a six-axis inertial measurement unit (IMU) are processed by the fusion algorithm to estimate the slope angle of the road and the longitudinal acceleration of the bicycle for conducting mass compensation, gravity compensation, and friction compensation. The compensations allow the ride of the electric bicycle on hills to be as effortless as the ride of a plain bicycle on the level ground regardless of the weight increase by the battery and the motor. The sensor fusion algorithm is basically an observer constructed on the kinematic model which describes the time-varying characteristics of the gravity vector observed from a frame moving with the bicycle. By exploiting the structure of the observer model, convergence of the estimation errors can be easily achieved by selecting two constant, subgain matrices in spite of the time-varying characteristics of the model. The validity of the sensor fusion is verified by both numerical simulations and experiments on a prototype bicycle.

scholarly journals Evolutionary Optimization Strategy for Indoor Position Estimation Using Smartphones

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 618
Author(s):  
Jan Grottke ◽  
Jörg Blankenbach
Keyword(s):  
Particle Filter ◽  
Sensor Fusion ◽  
Local Area Network ◽  
Information Source ◽  
Position Estimation ◽  
Measurement Unit ◽  
Area Network ◽  
Fusion Algorithm ◽  
Individual Measurement ◽  
Grid Based

Due to their distinctive presence in everyday life and the variety of available built-in sensors, smartphones have become the focus of recent indoor localization research. Hence, this paper describes a novel smartphone-based sensor fusion algorithm. It combines the relative inertial measurement unit (IMU) based movements of the pedestrian dead reckoning with the absolute fingerprinting-based position estimations of Wireless Local Area Network (WLAN), Bluetooth (Bluetooth Low Energy—BLE), and magnetic field anomalies as well as a building model in real time. Thus, a step-based position estimation without knowledge of any start position was achieved. For this, a grid-based particle filter and a Bayesian filter approach were combined. Furthermore, various optimization methods were compared to weigh the different information sources within the sensor fusion algorithm, thus achieving high position accuracy. Although a particle filter was used, no particles move due to a novel grid-based particle interpretation. Here, the particles’ probability values change with every new information source and every stepwise iteration via a probability-map-based approach. By adjusting the weights of the individual measurement methods compared to a knowledge-based reference, the mean and the maximum position error were reduced by 31%, the RMSE by 34%, and the 95-percentile positioning errors by 52%.

scholarly journals Extension of the Rigid-Constraint Method for the Heuristic Suboptimal Parameter Tuning to Ten Sensor Fusion Algorithms Using Inertial and Magnetic Sensing

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6307
Author(s):  
Marco Caruso ◽  
Angelo Maria Sabatini ◽  
Marco Knaflitz ◽  
Ugo Della Croce ◽  
Andrea Cereatti
Keyword(s):  
Sensor Fusion ◽  
Parameter Tuning ◽  
Optical Data ◽  
Measurement Unit ◽  
Average Error ◽  
Fusion Algorithm ◽  
Multiple Parameters ◽  
Rigid Constraint ◽  
Parameter Values ◽  
Constraint Method

The orientation of a magneto-inertial measurement unit can be estimated using a sensor fusion algorithm (SFA). However, orientation accuracy is greatly affected by the choice of the SFA parameter values which represents one of the most critical steps. A commonly adopted approach is to fine-tune parameter values to minimize the difference between estimated and true orientation. However, this can only be implemented within the laboratory setting by requiring the use of a concurrent gold-standard technology. To overcome this limitation, a Rigid-Constraint Method (RCM) was proposed to estimate suboptimal parameter values without relying on any orientation reference. The RCM method effectiveness was successfully tested on a single-parameter SFA, with an average error increase with respect to the optimal of 1.5 deg. In this work, the applicability of the RCM was evaluated on 10 popular SFAs with multiple parameters under different experimental scenarios. The average residual between the optimal and suboptimal errors amounted to 0.6 deg with a maximum of 3.7 deg. These encouraging results suggest the possibility to properly tune a generic SFA on different scenarios without using any reference. The synchronized dataset also including the optical data and the SFA codes are available online.

Multimodal Sensor Fusion for Indoor Occupancy Determination

2015 ◽  
Vol 764-765 ◽  
pp. 1319-1323
Author(s):  
Rong Shue Hsiao ◽  
Ding Bing Lin ◽  
Hsin Piao Lin ◽  
Jin Wang Zhou
Keyword(s):  
Bayesian Networks ◽  
Sensor Fusion ◽  
Dynamic Bayesian Networks ◽  
Sensor Nodes ◽  
Detection Accuracy ◽  
Multisensor Fusion ◽  
Fusion Algorithm ◽  
Improve Energy Efficiency ◽  
Occupancy Detection ◽  
Automation Systems

Pyroelectric infrared (PIR) sensors can detect the presence of human without the need to carry any device, which are widely used for human presence detection in home/office automation systems in order to improve energy efficiency. However, PIR detection is based on the movement of occupants. For occupancy detection, PIR sensors have inherent limitation when occupants remain relatively still. Multisensor fusion technology takes advantage of redundant, complementary, or more timely information from different modal sensors, which is considered an effective approach for solving the uncertainty and unreliability problems of sensing. In this paper, we proposed a simple multimodal sensor fusion algorithm, which is very suitable to be manipulated by the sensor nodes of wireless sensor networks. The inference algorithm was evaluated for the sensor detection accuracy and compared to the multisensor fusion using dynamic Bayesian networks. The experimental results showed that a detection accuracy of 97% in room occupancy can be achieved. The accuracy of occupancy detection is very close to that of the dynamic Bayesian networks.

scholarly journals Evaluation of Matrix Square Root Operations for UKF within a UAV GPS/INS Sensor Fusion Application

2011 ◽  
Vol 2011 ◽  
pp. 1-11 ◽  
Author(s):  
Matthew Rhudy ◽  
Yu Gu ◽  
Jason Gross ◽  
Marcello R. Napolitano
Keyword(s):  
Sensor Fusion ◽  
Unscented Kalman Filter ◽  
Attitude Estimation ◽  
Square Root ◽  
Fusion Algorithm ◽  
Matrix Square Root ◽  
Global Positioning ◽  
The Matrix ◽  
Aerial Vehicle ◽  
Diagonalization Method

Using an Unscented Kalman Filter (UKF) as the nonlinear estimator within a Global Positioning System/Inertial Navigation System (GPS/INS) sensor fusion algorithm for attitude estimation, various methods of calculating the matrix square root were discussed and compared. Specifically, the diagonalization method, Schur method, Cholesky method, and five different iterative methods were compared. Additionally, a different method of handling the matrix square root requirement, the square-root UKF (SR-UKF), was evaluated. The different matrix square root calculations were compared based on computational requirements and the sensor fusion attitude estimation performance, which was evaluated using flight data from an Unmanned Aerial Vehicle (UAV). The roll and pitch angle estimates were compared with independently measured values from a high quality mechanical vertical gyroscope. This manuscript represents the first comprehensive analysis of the matrix square root calculations in the context of UKF. From this analysis, it was determined that the best overall matrix square root calculation for UKF applications in terms of performance and execution time is the Cholesky method.

A Multi-Sensor Fusion Algorithm for Pedestrian Navigation Using Factor Graphs

2021 ◽  
Author(s):  
Langping An ◽  
Xianfei Pan ◽  
Ze Chen ◽  
Mang Wang ◽  
Zheming Tu ◽  
...  
Keyword(s):  
Sensor Fusion ◽  
Factor Graphs ◽  
Fusion Algorithm ◽  
Pedestrian Navigation

scholarly journals Integration of GPS, Monocular Vision, and High Definition (HD) Map for Accurate Vehicle Localization

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3270 ◽  
Author(s):  
Hao Cai ◽  
Zhaozheng Hu ◽  
Gang Huang ◽  
Dunyao Zhu ◽  
Xiaocong Su
Keyword(s):  
Kalman Filter ◽  
Low Cost ◽  
Kinematic Model ◽  
Intelligent Vehicles ◽  
Monocular Vision ◽  
Measurement Unit ◽  
High Definition ◽  
Position Information ◽  
Vehicle Localization ◽  
Lateral Distance

Self-localization is a crucial task for intelligent vehicles. Existing localization methods usually require high-cost IMU (Inertial Measurement Unit) or expensive LiDAR sensors (e.g., Velodyne HDL-64E). In this paper, we propose a low-cost yet accurate localization solution by using a custom-level GPS receiver and a low-cost camera with the support of HD map. Unlike existing HD map-based methods, which usually requires unique landmarks within the sensed range, the proposed method utilizes common lane lines for vehicle localization by using Kalman filter to fuse the GPS, monocular vision, and HD map for more accurate vehicle localization. In the Kalman filter framework, the observations consist of two parts. One is the raw GPS coordinate. The other is the lateral distance between the vehicle and the lane, which is computed from the monocular camera. The HD map plays the role of providing reference position information and correlating the local lateral distance from the vision and the GPS coordinates so as to formulate a linear Kalman filter. In the prediction step, we propose using a data-driven motion model rather than a Kinematic model, which is more adaptive and flexible. The proposed method has been tested with both simulation data and real data collected in the field. The results demonstrate that the localization errors from the proposed method are less than half or even one-third of the original GPS positioning errors by using low cost sensors with HD map support. Experimental results also demonstrate that the integration of the proposed method into existing ones can greatly enhance the localization results.

Sign in / Sign up

Export Citation Format

Share Document