Formation control for car-like mobile robots using front-wheel driving and steering

Chen, Haojie; Yang, Hong-an; Wang, Xu; Zhang, Ting

doi:10.1177/1729881418778228

Cited by 8 publications

(8 citation statements)

References 16 publications

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“…The error between leader AUV and virtual AUV trajectory and speed of virtual AUV are used as the input of backstep control. The kinematic formation control law is obtained by the backstep method 29,30…”

Section: Algorithm Descriptionmentioning

confidence: 99%

A leader–follower formation control approach for target hunting by multiple autonomous underwater vehicle in three-dimensional underwater environments

Cao

2019

International Journal of Advanced Robotic Systems

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As one of the challenging tasks of multiple autonomous underwater vehicles systems, the realization of target hunting is the great significance. The multiple autonomous underwater vehicle target hunting is studied in this article. In some research, because the hunting members cannot reach the hunting point at the same time, the hunting time is long or the target escapes. To improve the efficiency of the target hunting, the leader–follower formation algorithm is introduced. Firstly, the task is assigned based on the distance between the autonomous underwater vehicle and the target. Then, the autonomous underwater vehicles with the same task are formed based on leader–follower mode, and the formation is kept to track the target. In the final capture phase, multiple autonomous underwater vehicle system use angle matching algorithm to round up target. The simulation results show that the proposed algorithm can effectively accomplish the target hunting task, save the hunting time, and avoid the target escape. Compared with the bioinspired neural network algorithm, the proposed algorithm shows better performance.

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Section: Algorithm Descriptionmentioning

confidence: 99%

A leader–follower formation control approach for target hunting by multiple autonomous underwater vehicle in three-dimensional underwater environments

Cao

2019

International Journal of Advanced Robotic Systems

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“…Let the distance between the two actuated wheels be 2l. Substituting equation (1) into equation (2) and subtracting sidewise equation (2)…”

Section: Platform Kinematicsmentioning

confidence: 99%

“…As shown in Figures 7, 9 and 10, the maximum steering angles of the DDMR travelling on the normal, stretched and squeezed Lamé curves are 47 , 20 and 73 , respectively. Taking the normal Lamé curve, for example, the relatively computational errors of the generalized inertia matrix reach 19.2% for the (1, 1) entry, 53% for the (1,2) and (2, 1) entries, and, finally, 24.9% for entry (2,2). The comparison results verify definitely that the dynamic effects of casters on the whole system model exceed the significance that was expected as usual, even if the mass of the caster only accounts for a small proportion in the total mass of a DDMR.…”

Section: Modelling-performance Comparisonmentioning

confidence: 99%

“…Differential-driving mobile robots (DDMRs) are widely used in research and industry environments, for example, some well-known products like Pioneer, Roomba and KIVA. One main advantage of DDMRs is the capability of changing their direction by varying the angular velocities of their two driving wheels, without any additional steering mechanism, as opposed to car-like mobile robots 1,2 and unmanned ground vehicles. 3 When a DDMR undergoes planar motion, three wheels are sufficient to guarantee the stable balance of its platform.…”

Section: Introductionmentioning

confidence: 99%

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Steering-angle computation for the multibody modelling of differential-driving mobile robots with a caster

Ángeles²,

Zou

et al. 2018

International Journal of Advanced Robotic Systems

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Since many off-the-shelf motor drives are supplied with complete control capability in the current, velocity and position loop, the robot model in the navigation control architecture can be oriented either to kinematics, interfaced with the velocity loop, or to dynamics, with the motor-current loop. Moreover, no constraints are imposed by a caster on the mobility of differential-driving mobile robots. Hence, a reduced model, containing only the platform, is sufficient for navigation control based only on the robot kinematics. However, if the multibody system model is used for navigation control based on the robot dynamics, to cope with the demands of high-speed manoeuvres and/or heavy-load operations, then the caster kinematics, especially the knowledge of the steering angle, is required to calculate the inertia matrix and the terms of Coriolis and centrifugal forces. While this angle can be measured by means of dedicated encoders to be installed for casters, the computation technique based on the existing tachometers, already mounted on the motor shafts for the servo control of the two driving wheels, is proved to be sufficient. Both a thorough kinematics model and a multibody dynamics model, including the platform and all different wheels, are formulated here for differential-driving mobile robots. Computational methods based on velocity compatibility and rigid body twists are proposed to estimate the steering angle. Simulation results of the differential-driving mobile robot moving on a smooth trajectory show the feasibility of the steering-angle computational scheme, which obviates the need of installing caster encoders. Moreover, a performance comparison on system modelling is implemented via simulation, between the differential-driving mobile robot model with and without caster dynamics. This further validates the importance of the dynamic effects of casters on the whole system model. Therefore, the multibody modelling approach for casters with the steering-angle computation technique can facilitate the navigation control architecture under dynamics conditions.

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“…Multi-robot formation control is one of the most important areas of research in Multi-Robot Systems [1,2], due to general practical applications such as joint handing [3], cooperative rescue [4], group stalking, and exploration [5]. The goal of multi-robot formation control is to maintain a specified geometrical shape of a group of robots by adjusting the pose (positions and orientations) of robots [6], which generally follows the process of forming, maintaining, and switching of formation.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Robot Formation Platform based on an Indoor Global Positioning System

et al. 2019

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Aimed at the problem that experimental verifications are difficult to execute due to lacking effective experimental platforms in the research field of multi-robot formation, we design a simple multi-robot formation platform. This proposed general and low-cost multi-robot formation platform includes the indoor global-positioning system, the multi-robot communication system, and the wheeled mobile robot hardware. For each wheeled mobile robot in our platform, its real-time position information in the centimeter‑level precise is obtained by the Marvelmind Indoor Navigation System and orientation information is obtained by the six-degree-of-freedom gyroscope. The Transmission Control Protocol/Internet Protocol (TCP/IP) wireless communication infrastructure is selected to support the communication among robots and the data collection in the process of experiments. Finally, a set of leader–follower formation experiments are performed by our platform, which include three trajectory tracking experiments of different types and numbers under deterministic environment and a formation-maintaining experiment with external disturbances. The results illustrate that our multi-robot formation platform can be effectively used as a general testbed to evaluate and verify the feasibility and correctness of the theoretical methods in the multi-robot formation. What is more, the proposed simple and general formation platform is beneficial to the development of platforms in the fields of multi-robot coordination, formation control, and search and rescue missions.

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Formation control for car-like mobile robots using front-wheel driving and steering

Cited by 8 publications

References 16 publications

A leader–follower formation control approach for target hunting by multiple autonomous underwater vehicle in three-dimensional underwater environments

A leader–follower formation control approach for target hunting by multiple autonomous underwater vehicle in three-dimensional underwater environments

Steering-angle computation for the multibody modelling of differential-driving mobile robots with a caster

A Multi-Robot Formation Platform based on an Indoor Global Positioning System

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