Integration of Inertial Sensor Data into Control of the Mobile Platform

Pirník, Rastislav; Hruboš, Marián; Nemec, Dušan; Mravec, Tomas; Božek, Pavol

doi:10.1007/978-3-319-46535-7_21

Cited by 37 publications

(31 citation statements)

References 6 publications

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“…Among these techniques, adaptive neural PID controllers are presented as an option due to neural networks characteristic that allows them to adapt themselves to changes in operating conditions and environmental parameters, giving the controller the capability of adapting its parameters online [5][6][7]. Adaptive control techniques are important to solve problems in robotics research, such as control of robot manipulators [8,9], control of mobile robots [10,11] and formation control [12], control of underwater vehicles [13,14], control for teleoperation systems [15] and industrial applications [16,17].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Single Neuron Anti-Windup PID Controller Based on the Extended Kalman Filter Algorithm

et al. 2020

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In this paper, an adaptive single neuron Proportional–Integral–Derivative (PID) controller based on the extended Kalman filter (EKF) training algorithm is proposed. The use of EKF training allows online training with faster learning and convergence speeds than backpropagation training method. Moreover, the propose adaptive PID approach includes a back-calculation anti-windup scheme to deal with windup effects, which is a common problem in PID controllers. The performance of the proposed approach is shown by presenting both simulation and experimental tests, giving results that are comparable to similar and more complex implementations. Tests are performed for a four wheeled omnidirectional mobile robot. Tests show the superiority of the proposed adaptive PID controller over the conventional PID and other adaptive neural PID approaches. Experimental tests are performed on a KUKA® Youbot® omnidirectional platform.

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Section: Introductionmentioning

confidence: 99%

Adaptive Single Neuron Anti-Windup PID Controller Based on the Extended Kalman Filter Algorithm

et al. 2020

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“…Guo et al [ 10 ] suggested supporting the co-localization of unmanned aerial vehicles (UAVs) in a non-GPS environment by adopting an Ultra-Wide Band (UWB)-based EKF localization algorithm. Pirník et al [ 11 ] investigated the performances of inertial devices in controlling wheeled mobile platforms and developed an algorithm to integrate inertial sensor-recorded data into the system control. In this context, they experimentally proved the necessity of the inertial devices to interact with other absolute sensors.…”

Section: Introductionmentioning

confidence: 99%

An Improved Game Theory-Based Cooperative Localization Algorithm for Eliminating the Conflicting Information of Multi-Sensors

Tang

Dou

2020

Sensors

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In this article, an improved game theory-based co-localization algorithm is proposed to precisely and cooperatively locate the multi-robot system in the wireless sensor network and efficiently eliminate the information conflict caused by multi-sensor. Specifically, the extended Kalman filter in the original algorithm is replaced by the unscented Kalman filter in the optimized algorithm, which contributes to lower linearization errors and higher localization precision. Then, the computational complexity is analyzed, and the derivative method is introduced to reduce the extra computation burden brought by the unscented Kalman filter. Subsequently, the stability issue resulting from the derivative method is addressed by introducing the singular value decomposition (SVD). In this context, the optimized algorithm is capable of precisely locating the multi-robot system, while maintaining the stability and not increasing the computational burden. Moreover, as demonstrated by the simulation results, the optimized algorithm has greater localization precision than the original algorithm, while they have similar computational burdens.

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“…Accelerometers in the inertial system provide determining values of linear accelerations for the MR. Numerical integration of acceleration allows passing to speed, and repeated integration allows passing to movement during any time interval [ 9 ]. However, integration leads to the accumulation of error [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Strapdown Inertial Navigation Systems for Positioning Mobile Robots—MEMS Gyroscopes Random Errors Analysis Using Allan Variance Method

Rudyk

Семенов

Kryvinska

et al. 2020

Sensors

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A problem of estimating the movement and orientation of a mobile robot is examined in this paper. The strapdown inertial navigation systems are often engaged to solve this common obstacle. The most important and critically sensitive component of such positioning approximation system is a gyroscope. Thus, we analyze here the random error components of the gyroscope, such as bias instability and random rate walk, as well as those that cause the presence of white and exponentially correlated (Markov) noise and perform an optimization of these parameters. The MEMS gyroscopes of InvenSense MPU-6050 type for each axis of the gyroscope with a sampling frequency of 70 Hz are investigated, as a result, Allan variance graphs and the values of bias instability coefficient and angle random walk for each axis are determined. It was found that in the output signals of the gyroscopes there is no Markov noise and random rate walk, and the X and Z axes are noisier than the Y axis. In the process of inertial measurement unit (IMU) calibration, the correction coefficients are calculated, which allow partial compensating the influence of destabilizing factors and determining the perpendicularity inaccuracy for sensitivity axes, and the conversion coefficients for each axis, which transform the sensor source codes into the measure unit and bias for each axis. The output signals of the calibrated gyroscope are noisy and offset from zero to all axes, so processing accelerometer and gyroscope data by the alpha-beta filter or Kalman filter is required to reduce noise influence.

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Integration of Inertial Sensor Data into Control of the Mobile Platform

Cited by 37 publications

References 6 publications

Adaptive Single Neuron Anti-Windup PID Controller Based on the Extended Kalman Filter Algorithm

Adaptive Single Neuron Anti-Windup PID Controller Based on the Extended Kalman Filter Algorithm

An Improved Game Theory-Based Cooperative Localization Algorithm for Eliminating the Conflicting Information of Multi-Sensors

Strapdown Inertial Navigation Systems for Positioning Mobile Robots—MEMS Gyroscopes Random Errors Analysis Using Allan Variance Method

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