Applications of mobile manipulators over uneven terrain, for doing complex tasks autonomously, will be increased. In many cases, robot control algorithms and path planning methods ignore some characteristics of a robot and its environment. Thus they have a limited performance over uneven terrain. Therefore, robot instability that leads to roll over is not an unexpected matter. In this paper, mobile manipulator rollover recovery, by using a manipulator and a vision system, is presented. A genetic algorithm and a quasi static model are used to find the optimal recovery process. The criterion of optimization is the power consumption of actuators of the mobile manipulator. The power consumption depends on the path that the end effectors negotiate over the surface. To estimate the surface curve, a human like stereo vision is used. Finally, simulation results are presented.

Mobile robots that consist of a mobile platform with one or many manipulators mounted on it are of great interest in a number of applications. Combination of platform and manipulator causes robot operates in extended work space. The analysis of these systems includes kinematics redundancy that makes more complicated problem. However, it gives more feasibility to robotic systems because of the existence of multiple solutions in specified workspace. This paper presents a methodology for generating paths and trajectories for both the mobile platform and a 3DOF manipulator mounted on it, in the presence of obstacles. Obstacles add kinematics constraint into optimization problem. The method employs smooth and continuous functions such as polynomials. The proposed method includes obtaining time history of motion of mobile robot. It is supposed obstacles can be enclosed in cylinders. Platform that has been used in this research is a differentially-driven platform. The core of the method is based on mapping the non-holonomic constraint to a space where it can be satisfied trivially. A suitable criterion can be used to solve an optimization problem to find the optimal solution. In this research, the problem of path planning with simultaneous optimization of kinematics and dynamic indices has been accomplished using genetic algorithm in order to find the global optimum solution. The validity of the methodology is demonstrated by using a differential-drive mobile manipulator system, and various simulations of platform with a spatial 3-link manipulator are presented to show the effectiveness of the presented method.

This paper presents an optimal path planning ~for planar hyper redundant robot manipulators in presence of circular obstacles with a new analytical collision avoidance approach. To generate the robot's trajectory, a dual genetic algorithm for rapid achievement to the optimal solutions in complex space is offered. A polynomial based on cubic spline interpolation? is applied to approximate trajectories in joint space. The GA determines the parameters, which are the interior points to be interpolated to formulate the polynomial representing the trajectory, it is to minimize the fitness of the desired objective function. The effectiveness and capability of the proposed approach is demonstrated through simulation studies.

Forecasting the forces of the linear actuators with the aim of designating proportionate motors and or structural design of the motion system in flight simulators is of the considerable importance. In this paper, inverse dynamic analysis of the 6 degrees of freedom Stewart – Gough motion system, using Newton-Euler formulation approach consisting linear actuator components and moving platform dynamics with application prospects to flight simulators is illustrated. The developed inverse dynamics simulation software of the motion system is provided by the output results of the general nonlinear inverse kinematical based motion cueing system for computing the static and dynamic actuating forces in typical surge – pitch maneuver. Compared simulation results clearly indicate a significant disproportionate difference between the static and dynamic loads for the prototypical maneuver. Thereupon true attention on predicting the dynamic forces associated with the proposed inverse kinematics in structural design and or designating linear actuator is emphasized.

This paper, a robust dynamic slip mode controller for an electrical robot manipulator is presented. The control law calculates the motor voltage based on the voltage control strategy. Uncertainties are estimated using the Fourier series expansion and the cutting error is compensated. Fourier coefficients are adjusted based on stability analysis. Also in this paper is the design of a robust controller using a new adaptive Fourier series extension. Compared to previous related works based on the Fourier series expansion, the advantage of this paper is that it provides a matching law for the main frequency of the Fourier series expansion and thus eliminates the need for trial and error in its regulation. A case study of a articulated robot powered by DC magnet electric motors. The effect of uncertainty estimation based on the Fourier series expansion is studied instead of using the sign function. The proposed method is also compared with Legendre polynomials. The simulation results confirm the robust and satisfactory performance of the proposed controller.

In this article, a robust sliding mode control scheme based on a neural network is proposed to control a robotic manipulator in the presence of actuator saturation and external disturbances. In the proposed control method, the destructive effects of actuator saturation are mitigated by using an actuator saturation compensation system based on the Type-2 Chebyshev neural network. The presented control scheme has advantages such as fast convergence, small tracking error, robustness, and suitable performance of the control system in the presence of actuator saturation and external disturbances. The weights of the neural network are obtained using Lyapunov theory, and the stability of the closed-loop system is proven. The performance of the proposed controller is compared with other controllers, and the effectiveness of the proposed control scheme is examined for different trajectories through numerical simulations

A manipulator mounted on a moving vehicle is called a mobile manipulator. A mobile manipulator with an appropriate suspension system can pass over uneven surfaces, thus having an infinite workspace. If the manipulator could operate while the vehicle is traveling, the efficiency concerning the time and energy used for stopping and starting will be increased. This paper presents the kinematic and dynamic mode ling of a three degrees-of-freedom manipulator attached on a vehicle with three degrees-of-freedom suspension system. The manipulator is a RRR type, with three revolute joints, and the degrees of freedom of vehicle are bounce, pitch, and roll. The vehicle is considered to move with a constant linear speed over an uneven surface while the end effector tracks a desired trajectory in a fixed reference frame. Simulation results for straight-line trajectory are presented.