Low level control of an omnidirectional mobile robot

This paper presents the modeling and robust low-level control design of a redundant mobile robot with four omnidirectional wheels, the iSense Robotic (iSRob) platform, that was designed to test safe control algorithms. iSRob is a multivariable nonlinear system subject to parameter uncertainties mainly due to friction forces. A multilinear model is proposed to approximate the behavior of the system, and the parameters of these models are estimated from closed-loop experimental data applying Gauss–Newton techniques. A robust control technique, quantitative feedback theory (QFT), is applied to design a proportional–integral (PI) controller for robust low-level control of the iSRob system, being this the main contribution of the paper. The designed controller is implemented, tested, and compared with a gain-scheduling PI-controller based on pole assignment. The experimental results show that robust stability and control effort margins against system uncertainties are satisfied and demonstrate better performance than the other controllers used for comparison.

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Buckybot: A Robot Based on the Geometry of a Truncated Icosahedron

Volume 5B: 38th Mechanisms and Robotics Conference ◽

10.1115/detc2014-35537 ◽

2014 ◽

Author(s):

Michael D. M. Kutzer ◽

Christopher Y. Brown ◽

Gregory S. Chirikjian ◽

Mehran Armand

Keyword(s):

Mobile Robot ◽

Trajectory Planning ◽

Mobile Platform ◽

Control Algorithms ◽

Level Control ◽

Soccer Ball ◽

Low Level ◽

Symmetric Geometry

This paper introduces Buckybot, a novel mobile platform, and investigates its kinematics and preliminary control algorithms. Buckybot is a ground-based platform whose geometry is based on a truncated icosahedron, i.e. a soccer ball with flattened sides. The platform has 20 passive hexagonal faces on which it can stably rest, and 12 rounded pentagonal faces which can be extended linearly to tilt Buckybot. The symmetric geometry of the robot makes it operational in any configuration which is ideal for a variety of deployment scenarios including throwing or dropping. Buckybot currently locomotes using a semi-static tipping gait to move between adjacent hexagonal faces. In this work, we present the design and low-level control of the Buckybot platform, explore the kinematics associated with Buckybot’s method of locomotion, experimentally characterize tipping, and investigate trajectory planning for this new mobile robot. Results demonstrate effective trajectory planning accounting for plan uncertainty.

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An unsupervised neural network for low-level control of a wheeled mobile robot: noise resistance, stability, and hardware implementation

IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) ◽

10.1109/3477.499798 ◽

1996 ◽

Vol 26 (3) ◽

pp. 485-496 ◽

Cited By ~ 16

Author(s):

P. Gaudiano ◽

E. Zalama ◽

J.L. Coronado

Keyword(s):

Neural Network ◽

Mobile Robot ◽

Hardware Implementation ◽

Wheeled Mobile Robot ◽

Level Control ◽

Noise Resistance ◽

Low Level ◽

Unsupervised Neural Network ◽

Resistance Stability

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An Exponential Decreased Kinematic and PID Low Level Control Schemes for an Omni-Wheeled Mobile Robot

10.1109/ieit53149.2021.9587370 ◽

2021 ◽

Author(s):

Indrazno Siradjuddin ◽

Totok Winarno ◽

Muhammad Khairuddin ◽

Mas Nurul Achmadiah ◽

Rendi Pambudi Wicaksono ◽

...

Keyword(s):

Mobile Robot ◽

Wheeled Mobile Robot ◽

Level Control ◽

Low Level ◽

Control Schemes

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A real-time, unsupervised neural network for the low-level control of a mobile robot in a nonstationary environment

Neural Networks ◽

10.1016/0893-6080(94)00063-r ◽

1995 ◽

Vol 8 (1) ◽

pp. 103-123 ◽

Cited By ~ 45

Author(s):

Eduardo Zalama ◽

Paolo Gaudiano ◽

Juan López Coronado

Keyword(s):

Neural Network ◽

Mobile Robot ◽

Real Time ◽

Level Control ◽

Low Level ◽

Unsupervised Neural Network

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FUZZY CONTROL OF DIFFERENTIAL DRIVE MOBILE ROBOT FOR MOVING TARGET TRACKING

Facta Universitatis Series Automatic Control and Robotics ◽

10.22190/fuacr1702083p ◽

2017 ◽

Vol 16 (2) ◽

pp. 83

Author(s):

Emina Petrović ◽

Miloš Simonović ◽

Vlastimir Nikolić

Keyword(s):

Mobile Robot ◽

Moving Objects ◽

Mobile Robotics ◽

Position Control ◽

Hierarchical Control ◽

Circular Path ◽

Level Control ◽

Low Level ◽

Angular Velocities ◽

High Level

Tracking of moving objects, including humans has important role in mobile robotics. In this paper, the hierarchical control structure for target/human tracking consisted of high and low level control was presented. The low level subsystem deals with the control of the linear and angular velocities using multivariable PD controller whose parameters are obtained by Particle swarm optimization. The position control of the mobile robot represents the high level control, where we use two fuzzy logic Mamdani controllers for distance and angle control. In order to test the effectiveness of the proposed control scheme a simulation was performed. Two cases, when the mobile robot pursues a target moving along a circular path and when the mobile robot pursues a target moving along a rectangle path, were simulated.

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Kinematics Study on an Omnidirectional Mobile Robot with MY Wheels

ROBOT ◽

10.3724/sp.j.1218.2012.00144 ◽

2012 ◽

Vol 34 (2) ◽

pp. 144 ◽

Cited By ~ 3

Author(s):

Changlong YE ◽

Huaiyong LI ◽

Shugen MA ◽

Huichao NI

Keyword(s):

Mobile Robot ◽

Omnidirectional Mobile Robot

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Motion Planning and Control of an Omnidirectional Mobile Robot in Dynamic Environments

Robotics ◽

10.3390/robotics10010048 ◽

2021 ◽

Vol 10 (1) ◽

pp. 48

Author(s):

Mahmood Reza Azizi ◽

Alireza Rastegarpanah ◽

Rustam Stolkin

Keyword(s):

Mobile Robot ◽

Collision Avoidance ◽

Motion Control ◽

Equations Of Motion ◽

Dynamic Environments ◽

Discrete State ◽

Omnidirectional Mobile Robot ◽

Planning And Control ◽

Avoidance Strategy ◽

And Performance

Motion control in dynamic environments is one of the most important problems in using mobile robots in collaboration with humans and other robots. In this paper, the motion control of a four-Mecanum-wheeled omnidirectional mobile robot (OMR) in dynamic environments is studied. The robot’s differential equations of motion are extracted using Kane’s method and converted to discrete state space form. A nonlinear model predictive control (NMPC) strategy is designed based on the derived mathematical model to stabilize the robot in desired positions and orientations. As a main contribution of this work, the velocity obstacles (VO) approach is reformulated to be introduced in the NMPC system to avoid the robot from collision with moving and fixed obstacles online. Considering the robot’s physical restrictions, the parameters and functions used in the designed control system and collision avoidance strategy are determined through stability and performance analysis and some criteria are established for calculating the best values of these parameters. The effectiveness of the proposed controller and collision avoidance strategy is evaluated through a series of computer simulations. The simulation results show that the proposed strategy is efficient in stabilizing the robot in the desired configuration and in avoiding collision with obstacles, even in narrow spaces and with complicated arrangements of obstacles.

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