Experimental investigations of a highly maneuverable mobile omniwheel robot

Kilin, Alexander A.; Božek, Pavol; Karavaev, Yury L.; Klekovkin, Anton V.; Shestakov, V A

doi:10.1177/1729881417744570

Cited by 38 publications

(30 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…If the gyroscope signal is taken at discrete time points of the sampling period

, the gyroscope noise can be described as white sequence (discrete white noise)

with the zero average

and the limited variance value

. The error variance determining the rotation angle is

[ 11 , 21 ]. Here, the standard deviation (SD) of the error accumulation from the rotation angle determination was caused by the presence of white noise in the gyroscope output signal.…”

Section: Analysis Of the Gyroscope Random Error Componentsmentioning

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

View full text Add to dashboard Cite

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.

show abstract

“…If the gyroscope signal is taken at discrete time points of the sampling period

, the gyroscope noise can be described as white sequence (discrete white noise)

with the zero average

and the limited variance value

. The error variance determining the rotation angle is

[ 11 , 21 ]. Here, the standard deviation (SD) of the error accumulation from the rotation angle determination was caused by the presence of white noise in the gyroscope output signal.…”

Section: Analysis Of the Gyroscope Random Error Componentsmentioning

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

View full text Add to dashboard Cite

show abstract

“…The selection of the feedback controller parameters is important for improving the motion accuracy of the robot. In this study, these parameters were selected referring to the method in [33] and simple manual adjustment based on the primitive state of the robot while tracking the rose path. To sufficiently evaluate the effectiveness of the proposed robust controller, the non-robust one and robust feedback controller were also evaluated using the same selected appropriate feedback controller parameters shown in Table 4.…”

Section: Simulation Setting and Instructionsmentioning

confidence: 99%

A Robust Feedback Path Tracking Control Algorithm for an Indoor Carrier Robot Considering Energy Optimization

et al. 2019

View full text Add to dashboard Cite

This work develops an indoor carrier robot for people with disabilities, where the precise tracking of designated route is crucial. The parameter uncertainties and disturbances of the robot impose significant challenges for tracking. The present paper first investigates the dynamic of mechanical structure and modeling of actuator motors and constructs a new dynamic model by considering all main parameter uncertainties and disturbances. A novel robust feedback tracking controller considering both the optimization of path tracking and the minimization of the power consumption energy is proposed. It is proved that the tracking errors e and e ˙ satisfy a H∞ performance indicator while the energy consumption is minimum. A simulation example was performed and the results show that this novel algorithm can effectively reduce the tracking error from 0.2 m to 0.006 m while guaranteeing the minimum energy consumption. Furthermore, the effectiveness of the proposed method was validated by experiment compared with the non-robust one.

show abstract

“…Algorithm for trajectory generation for a four-wheeled omnirobot was proposed in [23]. In [15], the experimental tuning of the proportional integral differential (PID) controller was considered to track the trajectory of a four-wheeled omnimobile robot.…”

Section: Introductionmentioning

confidence: 99%

On Global Trajectory Tracking Control for an Omnidirectional Mobile Robot with a Displaced Center of Mass

Andreev¹,

Peregudova²

2020

Nelin. Dinam.

View full text Add to dashboard Cite

This paper addresses the trajectory tracking control design of an omnidirectional mobile robot with a center of mass displaced from the geometrical center of the robot platform. Due to the high maneuverability provided by omniwheels, such robots are widely used in industry to transport loads in narrow spaces. As a rule, the center of mass of the load does not coincide with the geometric center of the robot platform. This makes the trajectory tracking control problem of a robot with a displaced center of mass relevant. In this paper, two controllers are constructed that solve the problem of global trajectory tracking control of the robot. The controllers are designed based on the Lyapunov function method. The main difficulty in applying the Lyapunov function method for the trajectory tracking control problem of the robot is that the time derivative of the Lyapunov function is not definite negative, but only semidefinite negative. Moreover, the LaSalle invariance principle is not applicable in this case since the closed-loop system is a nonautonomous system of differential equations. In this paper, it is shown that the quasi-invariance principle for nonautonomous systems of differential equations is much convenient for the asymptotic stability analysis of the closed-loop system. Firstly, we construct an unbounded state feedback controller such as proportional-derivative term plus feedforward. As a result, the global uniform asymptotic stability property of the origin of the closed-loop system has been proved. Secondly, we find that, if the damping forces of the robot are large enough, then the saturated position output feedback controller solves the global trajectory tracking control problem without velocity measurements. The effectiveness of the proposed controllers has been verified through simulation tests. Namely, a comparative analysis of the bounded controller obtained and the well-known "PD+" like control scheme is carried out. It is shown that the approach proposed in this paper saves energy for control inputs. A. S. Andreev, O. A. PeregudovaBesides, a comparative analysis of the bounded controller and its analogue constructed earlier in a cylindrical phase space is carried out. It is shown that the controller provides lower values for the root mean square error of the position and velocity of the closed-loop system.

show abstract

Experimental investigations of a highly maneuverable mobile omniwheel robot

Cited by 38 publications

References 20 publications

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

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

A Robust Feedback Path Tracking Control Algorithm for an Indoor Carrier Robot Considering Energy Optimization

On Global Trajectory Tracking Control for an Omnidirectional Mobile Robot with a Displaced Center of Mass

Contact Info

Product

Resources

About