scholarly journals Leader-Follower Formation Control of Wheeled Mobile Robots without Attitude Measurements

2021 ◽  
Vol 11 (12) ◽  
pp. 5639
Author(s):  
Jonathan Hirata-Acosta ◽  
Javier Pliego-Jiménez ◽  
César Cruz-Hernádez ◽  
Rigoberto Martínez-Clark

The problem of leader-follower formation of a platoon of differential-drive wheeled mobile robots without using attitude measurements is addressed in this paper. Contrary to the position-distance approaches existing in the literature, the formation and collision avoidance is achieved by introducing a state-dependent delay in the desired trajectory. The delay is obtained as the output of a dynamical system and its magnitude will decrease/increase depending on the distance between the robots. To guarantee trajectory tracking and to overcome the lack of orientation measurements, an output feedback control and attitude observer are proposed based on the kinematic model of the robots. The attitude observer is designed directly on the special orthogonal group SO(2) and it can be used in open-loop schemes. The proposed control-observer scheme ensures asymptotic convergence of the tracking and observer errors. Finally, experimental results are presented to show the performance of the proposed approach.

2016 ◽  
Vol 173 ◽  
pp. 1485-1494 ◽  
Author(s):  
Zhaoxia Peng ◽  
Shichun Yang ◽  
Guoguang Wen ◽  
Ahmed Rahmani ◽  
Yongguang Yu

Robotica ◽  
2014 ◽  
Vol 33 (1) ◽  
pp. 87-105 ◽  
Author(s):  
Khoshnam Shojaei

SUMMARYMany research works on the control of nonholonomic wheeled mobile robots (WMRs) do not consider the actuator saturation problem and the absence of velocity sensors in practice. The actuator saturation deteriorates the tracking performance of the controller, and the use of velocity sensors increases the cost and weight of WMR systems. This paper simultaneously addresses these problems by designing a saturated output feedback controller for uncertain nonholonomic WMRs. First, a second-order input–output model of nonholonomic WMRs is developed by defining a suitable set of output equations. Then a saturated adaptive robust tracking controller is proposed without velocity measurements. For this purpose, a nonlinear saturated observer is used to estimate robot velocities. The risk of actuator saturation is effectively reduced by utilizing saturation functions in the design of the observer–controller scheme. Semi-global uniform ultimate boundedness of error signals is guarantied by the Lyapunov stability analyses. Finally, simulation results are provided to show the effectiveness of the proposed controller. Compared with one recent work of the author, a comparative study is also presented to illustrate that the proposed saturated controller is more effective when WMR actuators are subjected to saturation.


2019 ◽  
Vol 9 (5) ◽  
pp. 1034 ◽  
Author(s):  
Sendren Sheng-Dong Xu ◽  
Hsu-Chih Huang ◽  
Tai-Chun Chiu ◽  
Shao-Kang Lin

This paper presents a biologically-inspired learning and adaptation method for self-evolving control of networked mobile robots. A Kalman filter (KF) algorithm is employed to develop a self-learning RBFNN (Radial Basis Function Neural Network), called the KF-RBFNN. The structure of the KF-RBFNN is optimally initialized by means of a modified genetic algorithm (GA) in which a Lévy flight strategy is applied. By using the derived mathematical kinematic model of the mobile robots, the proposed GA-KF-RBFNN is utilized to design a self-evolving motion control law. The control parameters of the mobile robots are self-learned and adapted via the proposed GA-KF-RBFNN. This approach is extended to address the formation control problem of networked mobile robots by using a broadcast leader-follower control strategy. The proposed pragmatic approach circumvents the communication delay problem found in traditional networked mobile robot systems where consensus graph theory and directed topology are applied. The simulation results and numerical analysis are provided to demonstrate the merits and effectiveness of the developed GA-KF-RBFNN to achieve self-evolving formation control of networked mobile robots.


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