Abstract
In this paper an analysis of productivity will be carried out from the resolution of the problem of trajectory planning of industrial robots. The analysis entails economic considerations, thus overcoming some limitations of the existing literature. Two methodologies based on optimization-simulation procedures are compared to calculate the time needed to perform an industrial robot task. The simulation methodology relies on the use of robotics and automation software called GRASP. The optimization methodology developed in this work is based on the kinematics and the dynamics of industrial robots. It allows us to pose a multiobjective optimization problem to assess the trade-offs between the economic variables by means of the Pareto fronts. The comparison is carried out for different examples and from a multidisciplinary point of view, thus, to determine the impact of using each method. Results have shown the opportunity costs of non using the methodology with optimized time trajectories. Furthermore, it allows companies to stay competitive because of the quick adaptation to rapidly changing markets.
Highlights
Time needed to perform a trajectory for industrial robots is a very important issue in order to improve productivity in many economic activities
The comparison is applied to several examples, which covers a wide range of parameters that govern the kinematics and dynamics of the industrial robots
The proposed multiobjective optimization methodology and the Pareto optimality have been applied to different examples in order to set the above mentioned trade-offs between the benefits and the obtained times
Summary
Time needed to perform a trajectory for industrial robots is a very important issue in order to improve productivity in many economic activities. The algorithms have been polished and the working assumptions of the robotic systems have been increasingly adjusted to real conditions This fact has been achieved by analysing the complete behaviour of the robotic system, the characteristics of the actuators and the mechanical structure of the robot. To tackle this problem other important working parameters and variables have been taken into account, such as the input torques, the energy consumed, and the power transmitted. The aforementioned algorithms provide a smooth robot motion for the robotic system
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