Optimal time trajectories for industrial robots with torque, power, jerk and energy consumed constraints

Abstract

Purpose The purpose of this paper is to analyze the impact of the torque, power, jerk and energy consumed constraints on the generation of minimum time collision-free trajectories for industrial robots in a complex environment. Design/methodology/approach An algorithm is presented in which the trajectory is generated under real working constraints (specifically torque, power, jerk and energy consumed). It also takes into account the presence of obstacles (to avoid collisions) and the dynamics of the robotic system. The method solves an optimization problem to find the minimum time trajectory to perform the tasks the robot should do. Findings Important conclusions have been reached when solving the trajectory planning problem related to the value of the torque, power, jerk and energy consumed and the relationship between them, therefore enabling the user to choose the most efficient way of working depending on which parameter he is most interested in optimizing. From the examples solved we have found the relationship between the maximum and minimum values of the parameters studied. Research limitations/implicationsThis new approach tries to model the real behaviour of the actuators in order to be able to upgrade the trajectory quality. So a lot of work has to be done in this field. Practical implicationsThe algorithm solve the trajectory planning problem for any industrial robot and the real characteristics of the actuators are taken into account which is essential to improve the performance of it. Originality/value This new tool enables us to improve the performance of the robot by combining adequately the values of the mentioned parameters (torque, power, jerk and consumed energy).

Reducing the energy consumption of industrial robots in manufacturing systems

“…From the literature, the most widely acknowledged optimization constraints are environment constraints (e.g., collision), dynamic behavior (e.g., vibration, torque, speed, and acceleration), energy consumed, and execution time. For instance, the detailed approaches for optimizing the energy consumption of IR based on collision constraints can be found in [17][18][19], while those based on optimal time trajectory can be found in [20].…”

“…Meanwhile, more complex optimization criteria are presented in [20], which not only focuses on collision constraints but also on the minimum execution time and the minimum jerk that is related to IR productivity and quality.…”

Comparison of Uncalibrated Model-Free Visual Servoing Methods for Small-Amplitude Movements: A Simulation Study

“…The data for static targets are presented in Table 4, the obtained values being within the ranges reported in the literature on robot control applications [40]. Examination of the table reveals that for static cases the particle filter has by far the largest jerk values, although it is the best estimation algorithm.…”

Solving the Time‐Jerk Optimal Trajectory Planning Problem of a Robot Using Augmented Lagrange Constrained Particle Swarm Optimization

“…The solution of the original constrained problem (4) can be obtained by solving (5). The Lagrange multiplier iterative formula is expressed as…”

“…The purpose of minimumtime trajectory planning is to plan the shortest execution time trajectory according to the given points while the boundary constraints are satisfied. Examples of minimum-time trajectory planning are provided in [1][2][3][4][5]. In fact, vibration may appear when a robot is in the moving process or when it is stopped, which will negatively impact the tracking accuracy of the robot; therefore, minimum vibration is an important performance index, and many researchers have studied how to utilize minimum-jerk trajectory planning to reduce vibration [6][7][8].…”