scholarly journals Bridging GNSS Outages with IMU and Odometry: A Case Study for Agricultural Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4467
Author(s):  
Eva Reitbauer ◽  
Christoph Schmied
Keyword(s):  
Dynamic Model ◽  
Sensor Fusion ◽  
Wheel Speed ◽  
Reference Trajectory ◽  
Fusion Algorithm ◽  
Agricultural Vehicles ◽  
Tracked Vehicles ◽  
Motion Characteristics ◽  
Error State

Nowadays, many precision farming applications rely on the use of GNSS-RTK. However, when it comes to autonomous agricultural vehicles, GNSS cannot be used as a stand-alone system for positioning. To ensure high availability and robustness of the positioning solution, GNSS-RTK must be fused with additional sensors. This paper presents a novel sensor fusion algorithm tailored to tracked agricultural vehicles. GNSS-RTK, an IMU and wheel speed sensors are fused in an error-state Kalman filter to estimate position and attitude of the vehicle. An odometry model for tracked vehicles is introduced which is used to propagate the filter state. By using both IMU and wheel speed sensors, specific motion characteristics of tracked vehicles such as slippage can be included in the dynamic model. The presented sensor fusion algorithm is tested at a composting site using a tracked compost turner. The sensor measurements are recorded using the Robot Operating System (ROS). To analyze the achievable accuracies for position and attitude of the vehicle, a precise reference trajectory is measured using two robotic total stations. The resulting trajectory of the error-state filter is then compared to the reference trajectory. To analyze how well the proposed error-state filter is suited to bridge GNSS outages, GNSS outages of 30 s are simulated in post-processing. During these outages, the vehicle’s state is propagated using the wheel speed sensors, IMU, and the dynamic model for tracked vehicles. The results show that after 30 s of GNSS outage, the estimated horizontal position of the vehicle still has a sub-decimetre accuracy.

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

A Sensor Fusion Algorithm for Mobile Node Localization

2011 ◽  
Vol 44 (1) ◽  
pp. 11258-11264
Author(s):  
Alessio De Angelis ◽  
Carlo Fischione ◽  
Peter Händel
Keyword(s):  
Sensor Fusion ◽  
Mobile Node ◽  
Node Localization ◽  
Fusion Algorithm

scholarly journals Comparison the Error Rate of Autoregressive Distributed Lag (ARDL) and Vector Autoregressive (VAR) (Case study: Forecast of Export Quantities in DIY)

2018 ◽  
Vol 18 (2) ◽  
pp. 167-177
Author(s):  
Dewi Kusuma Ningrum ◽  
Sugiyarto Surono
Keyword(s):  
Dynamic Model ◽  
Causality Test ◽  
Vector Autoregressive ◽  
Model Stability ◽  
Distributed Lag ◽  
Distributed Lag Model ◽  
Autoregressive Distributed Lag ◽  
Integration Test ◽  
Lag Model

Forecasting is estimating the size or number of something in the future. Regression model that enters current independent variable value, and lagged value is called distributed-lag model, if it enters one or more lagged value, it is called autoregressive. Koyck method is used for dynamic model which the lagged length is unknown, for the known lagged length it is used the Almon method. Vector Autoregressive (VAR) is a method that explains every variable in the model depend on the lag movement from the variable itself and all the others variable. This research aimed to explain the application of Autoregressive distributed-lag model and Vector Autoregressive (VAR) method for the forecasting for export amount in DIY. It takes export amount in DIY and inflation data, kurs, and Indonesias foreign exchange reserve. Forecasting formation: defining Koyck and Almon distributed-lag dynamic model, then the best model is chosen and distribution-lag dynamic forecasting is performed. After that it is performed stationary test, co-integration test, optimal lag examination, granger causality test, parameter estimation, VAR model stability, and performs forecasting with VAR method. The forecasting result shows MAPE value from ARDL method obtained is 0.475812%, while MAPE value from VAR method is 0.464473%. Thus it can be concluded that Vector Autoregressive (VAR) method is more effective to be used in case study of export amount in DIY forecasting. Keywords: Koyck; Almon; Lag; Autoregressive Distributed-Lag; Vector Autoregressive;

scholarly journals New time delay estimation-based virtual decomposition control for n-DoF robot manipulator

2021 ◽  
Vol 10 (3) ◽  
pp. 192
Author(s):  
Hachmia Faqihi ◽  
Khalid Benjelloun ◽  
Maarouf Saad ◽  
Mohammed Benbrahim ◽  
M. Nabil Kabbaj
Keyword(s):  
Time Delay ◽  
Dynamic Model ◽  
Control Strategy ◽  
Robot Manipulator ◽  
Time Delay Estimation ◽  
Lyapunov Theory ◽  
Degree Of Freedom ◽  
Delay Estimation ◽  
The Stability

<p>One of the most efficient approaches to control a multiple degree-of-freedom robot manipulator is the virtual decomposition control (VDC). However, the use of the re- gressor technique in the conventionnal VDC to estimate the unknown and uncertaities parameters present some limitations. In this paper, a new control strategy of n-DoF robot manipulator, refering to reorganizing the equation of the VDC using the time delay estimation (TDE) have been investigated. In the proposed controller, the VDC equations are rearranged using the TDE for unknown dynamic estimations. Hence, the decoupling dynamic model for the manipulator is established. The stability of the overall system is proved based on Lyapunov theory. The effectiveness of the proposed controller is proved via case study performed on 7-DoF robot manipulator and com- pared to the conventionnal Regressor-based VDC according to some evalution criteria. The results carry out the validity and efficiency of the proposed time delay estimation- based virtual decomposition controller (TD-VDC) approach.</p>

scholarly journals Vibration Response and Power Flow Characteristics of a Flexible Manipulator with a Moving Base

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Yufei Liu ◽  
Wei Li ◽  
Xuefeng Yang ◽  
Mengbao Fan ◽  
Yuqiao Wang ◽  
...  
Keyword(s):  
Dynamic Model ◽  
Power Flow ◽  
Flow Characteristics ◽  
Flexible Manipulator ◽  
Rigid Base ◽  
Flexible Beam ◽  
Noticeable Effect ◽  
Moving Base ◽  
Vibration Responses ◽  
Motion Characteristics

Flexible manipulator generally can be modeled as a coupling system with a flexible beam and a rigid moving base. This paper investigates the vibration responses and power flow of a flexible manipulator with a moving base (FMMB). Considering the motion characteristics of the rigid base, the moving base is modeled to have a motion with disturbances, and the dynamic model of the FMMB is established. With the dynamic model, vibration responses of the FMMB for the rigid base having disturbance velocities and accelerations are specifically presented. Subsequently, to investigate the effect of the disturbances on the vibration energy distributions of the FMMB, power flow of the FMMB is exhibited. To verify the dynamic model, an ADAMS physical model of the FMMB is constructed. It reveals that the motion characteristics of the rigid base have a noticeable effect on the vibration responses and power flow of the FMMB and should be considered. The results are significant and contribute to the vibration control of flexible manipulators.

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