Holonomic and Omnidirectional Locomotion Systems for Wheeled Mobile Robots: A Review

Safar, Muhammad Juhairi Aziz

doi:10.11113/jt.v77.6799

Cited by 9 publications

(4 citation statements)

References 30 publications

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“…The traveling direction of the conventional vehicle is fixed relative to the vehicle body, but OWV can move in any direction. According to (1) and 3, the driving force of the OWV should satisfy the following equation:…”

Section: Owv Driving Strategymentioning

confidence: 99%

“…Vehicles equipped with omnidirectional wheels, which can immediately move in an arbitrary direction, including the right, left, or diagonal direction, have the potential to solve the problem of limited mobility [1]- [2]. Vehicles with omnidirectional wheels can move efficiently even in a limited space compared with vehicles with conventional wheels [3]- [5].…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Traveling Strategies for Driving Omni-Wheeled Vehicle Around a Corner

et al. 2020

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Vehicles equipped with omnidirectional wheels can move in any direction, which means that many driving strategies are available, even for a simple task like turning a corner. However, the characteristics of each driving strategy have not yet been explored to detect what kind of strategy is suitable for what path conditions for what reasons. This study aims to clarify and compare the phenomena that occur in the possible least-time driving strategies for an omni-wheeled vehicle while it turns a corner. Three traveling strategies are proposed according to the order of rotation and turning motion, for which the possible least-time patterns are presented. Simulations are conducted to analyze the advantages of each pattern and compare the three traveling strategies for various path and corner configurations. The results show that the strategy in which the vehicle rotates during or after turning motion costs the least time. When both the path width and corner angle are small, the vehicle should turn with a large radius to maintain a high velocity. In contrast, the vehicle should turn with a small radius when the corner angle is large, even if this requires deceleration.

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Section: Owv Driving Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis of Traveling Strategies for Driving Omni-Wheeled Vehicle Around a Corner

et al. 2020

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“…Los robots omnidireccionales con ruedas mecanum (RORMs) se utilizan en diferentes áreas como la industria de almacén, navegación militar, rescate, entre otros (Teknologi et al, 2015)Algunas de las ventajas que otorgan la integración de las ruedas mecanum son: la maniobrabilidad, habilidad de reorientación y la capacidad de moverse en espacios reducidos (Alakshendra y Chiddarwar, 2017). No obstante, debido a que pueden presentarse fallas en el sistema de posicionamiento; fallas por nivel de batería; fallas en sensores o en los motores , la comunidad científica ha tenido interés en diagnosticar fallas en los RORMs.…”

Section: Introductionunclassified

Estimación de fallas en actuadores de un robot móvil con ruedas mecanum

Luna-Aguilar,

Gasga-García,

Gómez-Peñate

et al. 2024

ICBI

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En este artículo presenta un observador proporcional integral convexo (PIC) para la estimación de fallas en actuadores en un robot móvil con ruedas mecanum. Mediante la técnica del sector no lineal, se desarrolla un modelo convexo con estructura producto tensor el cual proporciona mayor flexibilidad en la solución de las ganancias del observador. Para las condiciones de estabilidad del error de estimación, se utiliza el método directo de Lyapunov, dando condiciones suficientes a través de un conjunto de desigualdades lineales matriciales (LMIs) que garantizan la convergencia asintótica del estado y fallas estimados al vector de estado y fallas del sistema. Finalmente, se evalúan los resultados a nivel simulación con base al modelo no lineal de un robot móvil con ruedas mecanum.

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“…A holonomic robot is a mobile robot where the number of controllable degrees of freedom (DOF) is the same as the number of total DOF-that is, the robot has no kinematic constraints [1]. In contrast, in a nonholonomic robot, the number of controllable DOF is less than the number of total DOF; thus, the robot may have one or more kinematic constraints.…”

Section: Introductionmentioning

confidence: 99%

Nonuniform Dual-Rate Extended Kalman-Filter-Based Sensor Fusion for Path-Following Control of a Holonomic Mobile Robot with Four Mecanum Wheels

et al. 2022

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This paper presents an extended Kalman-filter-based sensor fusion approach, which enables path-following control of a holonomic mobile robot with four mecanum wheels. Output measurements of the mobile platform may be sensed at different rates: odometry and orientation data can be obtained at a fast rate, whereas position information may be generated at a slower rate. In addition, as a consequence of possible sensor failures or the use of lossy wireless sensor networks, the presence of the measurements may be nonuniform. These issues may degrade the path-following control performance. The consideration of a nonuniform dual-rate extended Kalman filter (NUDREKF) enables us to estimate fast-rate robot states from nonuniform, slow-rate measurements. Providing these estimations to the motion controller, a fast-rate control signal can be generated, reaching a satisfactory path-following behavior. The proposed NUDREKF is stated to represent any possible sampling pattern by means of a diagonal matrix, which is updated at a fast rate from the current, existing measurements. This fact results in a flexible formulation and a straightforward algorithmic implementation. A modified Pure Pursuit path-tracking algorithm is used, where the reference linear velocity is decomposed into Cartesian components, which are parameterized by a variable gain that depends on the distance to the target point. The proposed solution was evaluated using a realistic simulation model, developed with Simscape Multibody (Matlab/Simulink), of the four-mecanum-wheeled mobile platform. This model includes some of the nonlinearities present in a real vehicle, such as dead-zone, saturation, encoder resolution, and wheel sliding, and was validated by comparing real and simulated behavior. Comparison results reveal the superiority of the sensor fusion proposal under the presence of nonuniform, slow-rate measurements.

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Holonomic and Omnidirectional Locomotion Systems for Wheeled Mobile Robots: A Review

Cited by 9 publications

References 30 publications

Analysis of Traveling Strategies for Driving Omni-Wheeled Vehicle Around a Corner

Analysis of Traveling Strategies for Driving Omni-Wheeled Vehicle Around a Corner

Estimación de fallas en actuadores de un robot móvil con ruedas mecanum

Nonuniform Dual-Rate Extended Kalman-Filter-Based Sensor Fusion for Path-Following Control of a Holonomic Mobile Robot with Four Mecanum Wheels

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