Modelling an Industrial Robot and Its Impact on Productivity

Abstract

This research aims to design an efficient algorithm leading to an improvement of productivity by posing a multi-objective optimization, in which both the time consumed to carry out scheduled tasks and the associated costs of the autonomous industrial system are minimized. The algorithm proposed models the kinematics and dynamics of the industrial robot, provides collision-free trajectories, allows to constrain the energy consumed and meets the physical characteristics of the robot (i.e., restriction on torque, jerks and power in all driving motors). Additionally, the trajectory tracking accuracy is improved using an adaptive fuzzy sliding mode control (AFSMC), which allows compensating for parametric uncertainties, bounded external disturbances and constraint uncertainties. Therefore, the system stability and robustness are enhanced; thus, overcoming some of the limitations of the traditional proportional-integral-derivative (PID) controllers. The trade-offs among the economic issues related to the assembly line and the optimal time trajectory of the desired motion are analyzed using Pareto fronts. The technique is tested in different examples for a six-degrees-of-freedom (DOF) robot system. Results have proved how the use of this methodology enhances the performance and reliability of assembly lines.

Robust Control Approaches and Trajectory Planning Strategies for Industrial Robotic Manipulators in the Era of Industry 4.0: A Comprehensive Review

“…To that end, Hu et al [24] proposed dynamic surface control (DSC) based on a nonlinear disturbance observer (NDO) with an interval type 2 fuzzy neural network (IT2FNN), which performed better than the adaptive DSC with a neural network (NN) approximator and type 1 fuzzy (T1F) approximator in converging the tracking error to a sufficiently small value. Furthermore, in order to improve trajectory tracking accuracy, an adaptive fuzzy sliding mode control (AFSMC) was used in [25], which allowed the researchers to compensate for parametric uncertainties, bounded external disturbances and constraint uncertainties. Meanwhile, Quynh [26] investigated using a Wavelet Neural Network (WNN) with adaptive fuzzy sliding model control and the Lyapunov method to train a two-DOF robotic arm with high tracking accuracy.…”

Trajectory Planning in Robot Joint Space Based on Improved Quantum Particle Swarm Optimization Algorithm

“…In this paper, we focus on trajectory planning in joint (driving limb) space to avoid coupling problems that exist in Cartesian space trajectory planning. Trajectory planning can also be regarded as an optimization problem involving establishing a weighted objective function that considers motion energy consumption, actuator torque, jerk, and acceleration, and then employing corresponding optimization algorithms to solve it [1][2][3][4][5][6][7][8][9]. Many researchers have looked into the multi-objective hybrid criterion and proposed various multi-objective optimization methods for planning robot paths [10][11][12].…”

Perspective Chapter: Fabulous Design Speed Industrial Robotic Arm

“…The mathematical model of a mechanical system is indeed fundamental for the development of experimental prototypes [79]. On the other side, optimal trajectory planning of industrial robots in the assembly line is a key topic to boost productivity in a variety of manufacturing tasks [80]. The main focus of this work is the design of a 5-DOF industrial robotic arm that further enhanced speed performance and optimized trajectory planning by modeling an HSPID controller based on improved GA (IGA).…”