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.
Highlights
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
This paper is focused on a cost-efficient solution for robotic assembly lines, highlighting an economic analysis to the robotic operation
The methodology allows the company’s profit to be quantified by minimizing the time to complete the robot’s scheduled tasks and the downtime of the assembly line. This is achieved by coupling an adaptive fuzzy sliding mode control (AFSMC) controller with a multi-objective optimization algorithm which enables the actual motion to track the desired motion
Summary
The developed algorithm is intended to provide the minimum execution time of a multitasking robot manipulator. Gibbs–Appell equation of motion, yielding a well-structured set of equations that can be computed in real time This approach is used instead of more usual dynamic formulations, due to its advantages such as the simplicity of the obtained expressions and an easy model expansion in order to include additional effects as rotor dynamics or the friction phenomenon. In this sense, for generating the optimal time trajectory (t) between two different configurations, a robot configuration C j = C j (αi j , pk j ) is defined unambiguously using the Cartesian coordinates of important points in the robot αi j = (αxi j , αyi j , αzi j ).
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