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nonholonomic mobile robots are widely used in industrial environments due to their extended workspace. Also, to increase the productivity and efficiency of the mobile robot, their path planning is an important task, and is attracted attention of many of robotic scientists. In this paper, the optimal path planning of the wheeled mobile robots are performed considering their nonlinear dynamic equations and the nonholonomic constraints. Problem of the trajectory optimization is formulated, and conditions of the optimality are derived as a set of nonlinear differential equations by means of indirect method of optimal control method. Then, the optimality equations are solved numerically, and variant simulations are executed. To verify the simulation study, some experimental analysis are done for the Scout mobile robot and compared to the simulation results. The experimental analysis verifies the simulation results, and demonstrates the applicability of the proposed method for the optimal path planning of the mobile robots.

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The main purpose of this paper is to derive the inverse dynamic equation of motion of n-rigid robotic manipulator that mounted on a mobile platform, systematically. To avoid the Lagrange multipliers associated with the nonholonomic constraints the approach of Gibbs-Appell formulation in recursive form is adopted. For modeling the system completely and precisely the dynamic interactions between the manipulator and the mobile platform as well as both nonholonomic constraints associated with the no-slipping and the no-skidding conditions are also included. In order to reduce the computational complexity, all the mathematical operations are done by only 3×3 and 3×1 matrices. Also, all dynamic characteristics of a link are expressed in the same link local coordinate system. Finally, a computational simulation for a manipulator with five revolute joints that mounted on a mobile platform is presented to show the ability of this algorithm in generating the equation of motion of mobile robotic manipulators with high degree of freedom.

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Motion control of a planar nonholonomic system with four DoF is addressed in this paper. Three actuators are responsible for shape control of this system. Furthermore, assuming no external forces and zero angular momentum, imposes a nonholonomic constraint to the problem. First it is shown that although the simplified equations of motion for this system, could be converted to Heisenberg and chained-form systems, the conventional control methods for these systems, may not be applied to the considered problem. Then, using sliding modes and online path planning, two different closed-loop control laws are designed for bringing the system to and stabilizing around any desired equilibrium state started from any initial condition. Simulation results, show the efficiency of the proposed methods.

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Tractor-trailer wheeled mobile robot (TTWMR) is a robotic system that consists of a tractor module towing a trailer. Trajectory tracking is one of the challenging problems which is focused in the context of wheeled mobile robots (WMRs) that has been discussed in this paper. First, kinematic equations of TTWMR are obtained. Then, reference trajectories for tracking problem are produced. Subsequently, an output feedback kinematic control law and a dynamic Fuzzy Sliding Mode Control (FSMC) are designed for the TTWMR. The proposed controller steer the TTWMR asymptotically follow reference trajectories. Finally, experimental results of the designed controller on an experimental setup and comparison results are presented. Obtained results show the effectiveness of the proposed controller.

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In this paper, trajectory tracking control of a wheeled mobile robot is analyzed. Wheeled mobile robot is a nonlinear system. This system including three generalized coordinates (x,y,ϕ), and a nonholonomic constraint. First, system kinematic and dynamic equations are obtained. A non-model-based control algorithm using PD-action filtered errors has been used in order to control the wheeled mobile robot. Non-model-based controllers are always more appropriate than model-based algorithms due to independency from dynamic models, lower computational costs and also robustness to uncertainties. Asymptotic stability of the closed loop system for trajectory tracking control of wheeled mobile robot has been investigated using appropriate Lyapunov function and also Barbalat’s lemma method. Finally, in order to show the effectiveness of the proposed approach simulation and experimental results have been presented. Obtained results show that without requiring a priori knowledge of plant dynamics, and with reduced computational burden, the tracking performance of the presented algorithm is quite satisfactory. Therefore, the proposed control algorithm is well suited to most industrial applications where simple efficient algorithms are more appropriate than complicated theoretical ones with massive computational burden.

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Trajectory tracking is one of the main control problems in the context of Wheeled Mobile Robots (WMRs). Besides, control of underactuated systems possesses a particular complexity and importance; so it has been focused by many researchers in recent years. In this paper, these two important control subjects are discussed regarding a Tractor-Trailer Wheeled Mobile Robot (TTWMR); which includes a differential drive wheeled mobile robot towing a passive spherical wheeled trailer. The use of spherical wheels instead of standard wheels in trailer makes the robot highly underactuated with severe nonlinearities. Spherical wheels are used for the trailer to increase robots’ maneuverability. In fact, standard wheels create nonholonomic constraints by means of pure rolling and nonslip conditions, and reduce robot maneuverability. In this paper, after introducing the robot, kinematics and kinetics models are obtained, and combined as the dynamics model. Then, based on physical intuition a new controller is developed for the robot, named as Lyapaunov-PID control algorithm. Then, singularity avoidance of the proposed algorithm is discussed and the stability of the algorithm is discussed. Simulation results reveal the suitable performance of the proposed algorithm. Finally, experimental implementation results are presented which verify the simulation results.

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This paper investigates the leader-follower formation control problem of nonholonomic mobile robots based on backstepping technique composed with the bio-inspired neurodynamics while avoiding collision with obstacles. Kinematics model of robot and nonholonomic constraint are introduced and formation control scheme is formed based on backstepping technique. In order to solve velocity jump in backstepping kinematics model, the bio-inspired neurodynamic approach is used. In most of the previous studies, researches are used separation-bearing approach and also supposed that desired separation and bearing are constant. In this paper this assumption is relaxed and desired separation and bearing are considered to be time varying. Error dynamics equations are derived and a new controller is proposed. Also an auxiliary reference angular velocity control law is proposed to guarantee global asymptotic stability of the followers and local asymptotic stability of the entire formation according to direct method of Lyapunov. A common example of changing the formation is obstacle avoidance, when an obstacle is located within a follower path and is not in its leader path. Time varying functions for desired separation and bearing are chosen and the new controller is developed with its proof of stability. Simulations results reveal that each follower robot can track its real time leader employing the proposed kinematic controller while avoiding obstacles. Furthermore control inputs at the start moment and also while avoiding obstacles, do not contain impractical jumps and are reasonable thanks to integrating bio-inspired neurodynamic with backstepping technique.

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One of the main topics in the field of robotics is the formation control of the group of robots in trajectory tracking problem. Using organized robots has many advantages compared to using them individually. Among them the efficiency of using resources, the possibility of robots' cooperation, increasing reliability and resistance to defects can be pointed out. Therefore, formation control of multi-body robotic systems and intelligent vehicles attracted considerable attention that is discussed in this paper. First, kinematic and kinetic equations of a differential drive wheeled robot are obtained. Then, reference trajectories for tracking problem of the leader robot are produced. Next, a kinematic control law is designed for trajectory tracking of the leader robot. The proposed controller steer the leader robot asymptotically follow reference trajectories. Subsequently, a dynamic control algorithm for generating system actuator toques is designed based on feedback linearization method. Afterwards, formation control of the robots has been considered and an appropriate algorithm is designed in order to organize the follower robots in the desired configurations, meanwhile tracking control of the wheeled robot. Furthermore the stability of the presented algorithms for kinematic, dynamic and formation control laws is analyzed using Lyapunov method. Finally, obtained results for different reference paths are presented which represents the effectiveness of the proposed controller.

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The tractor-trailer system is a wheeled mobile robot (WMR), including one wheeled unit named tractor that is equipped with actuators and one or some wheeled units named trailer that are connected to each other with a passive joint. As a special case, in this paper, the system locomotion is by tractor, and trailer is completely passive. In this paper, we develop the kinematic model of tractor-trailer; so the inputs of system are supposed to be tractor angular velocity and trailer linear velocity. Because of pure rolling condition between wheel and ground surface, we encounter a nonholonomic system. In this paper Model Predictive Control (MPC) is used in control designing of Tractor-Trailer for the first time and the goal is trajectory tracking of trailer position. First, the kinematic equations of tractor-trailer are developed. Then a feasible reference geometrical path is considered. After linearizing the system equations around reference path and using MPC that is an optimal control based method, a tracking controller is designed that minimizes a predefined cost function. Considering actuator saturation in system inputs, we solve a constrained optimization problem using numerical methods. The controller designed in this paper, will be compared against classic controller PID and its better performance is presented. Finally, the effectiveness and robustness of controller against disturbances and parameter uncertainties is proved using MATLAB results.

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One of the main topics in the field of robotics is the motion control of wheeled mobile robots. Motion control encompasses trajectory tracking and point stabilization problems. In this paper these control problems will be considered for the tractor-trailer wheeled robots and a predictive control algorithm is developed for solving these problems. Therefore first kinematic model of the tractor_trailer robot is developed. Next, reference trajectories is produced for the system. Subsequently, predictive control law is designed for the trajectory tracking and point stabilization problems. Predictive control based on the known values of reference trajectories in the future, produces the control inputs in present time. Consequently the error signal with respect to the reference trajectory in future will be used in order to control the system at the present instant of time. This method is developed for solving the aforementioned control problems and is employed on the tractor_trailer wheeled robot. As can be seen from the results, the proposed control algorithm steer the wheeled robot asymptotically follow reference trajectories. Obtained results from the implementation of the proposed method for solving trajectory tracking and point stabilization problems, demonstrate the effectiveness of the presented algorithm.

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There exist satisfactory results in the analysis of the motion control of the vehicles with the assumption of nonslip (pure rolling) condition of robot wheeles, But unfortunately in practice due to the presence of uncertainties such as sliding of wheels especially in agriculture applications where working conditions are rough the results and the quality of the control performance of the system are affected. The ideal control of wheeled systems is performed with the assumption of the existence of nonholonomic non-slip constraints, while in the real system these constraints are violated due to the presence of slippages. In this paper the problem of trajectory tracking control of wheeled vehicles in the presence of sliding is addressed. To take sliding effects into account, sliding models are introduced into the kinematic model. In other words, these effects are added as unknown parameters to the ideal kinematic model. For taking into account the sliding effects their mathematical models are introduced in system kinematic model. In another word these effects as an unknown parameters are added to the system ideal kinematics. An integrating parameter adaptation technique and backstepping control algorithm has been utilized in order to control the system. The backstepping control law is designed to track the reference trajectories and make the robot asymptotically stable around the reference trajectories. Finally, the obtained results are presented for tracking reference trajectories and comparison results shows the efficiency of using the estimation of slips in control of the system.

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In this article, a novel control of wheeled mobile robot based on machine vision is considered. One of the common methods for controlling such systems is the use of Model Predictive Control (MPC) algorithms. In these systems, the response speed of the control algorithm and the optimality of these are two basic factors for achieving the optimal performance. Also, the impossibility of achieving precise values of the robot parameters and their variation during the operation of the robot is an important challenge in the implementation of the controller, therefore, this paper focuses on real-time and robust MPC, so that it can ensure the system against uncertainties and environmental disturbances in addition to the optimal and real-time response. Hence, the optimization based on projection recurrent neural network (PRNN) has been used as an optimizer to reduce the calculation time cost. The combination of PRNN optimization with MPC leads to new formulation and constraints that are considered to be the article innovations. Finally, in order to verify the validity of the proposed algorithm, the robot passes through the corridor with the presence of obstacles, which is simulated in the V-REP software. The results show that the optimum control input speed has been increased in comparison with similar methods, and the optimal path selection by the fuzzy system in the presence of obstacles has been well suited.
 

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