Optimization of Energy Consumption in KUKA KR 16 Articulated Robot Manipulator

Abstract

A study for optimal energy consumption in KUKA KR 16 articulated robot for pick-and-place task was introduce in this paper. In order to achieve the optimal energy consumption, an improve trajectory planning is required. Essentially, trajectory planning encompasses path planning in addition to planning how to move based on velocity, time and kinematics. Trajectory planning gives a path from a starting to a goal point by avoiding collisions in a 2D or 3D space. Therefore, this paper is focus on analyze the PTP motion and Linear motion in order to determine which is the best motion that can improve the trajectory planning. The optimal energy consumption to minimizing the movement based on three main axes where it used a big motors used to drive the axes. This method is much simpler in terms of development process and did not require any additional hardware to be install to the robot’s system. KUKA KR 16 is use to study optimal energy consumption and analyze PTP and Linear motion. The energy performance is measures with respect to two categories of movements known as Default and Optimal movement which do the same task repetitively within specific time. The result show that PTP motion consumed 6% more energy than Linear motion but completed 773 cycles within one hour whereas Linear motion only completed 492 cycles. Energy performance between Default and Optimal movement shows that Optimal movement recorded 21.8% less energy usage when compared to Default movement although the total cycles completed for both movement almost the same

The Influence of the Operator’s Perception on the Energy Demand for a Hydraulic Manipulator with a Large Working Area

“…The second group is teleoperated manipulators that work in unknown and less structured environments, and their tasks are not predefined [14,15]. Due to the wide scope of their application [16][17][18][19][20][21][22][23][24][25], they are characterized by a large working area (larger than the operator's arm reach) and a hydraulic drive system, and their control method (e.g., direct or coordinated teleoperation) allows for the transfer of manipulation skills from humans to manipulators.…”

“…Regarding industrial manipulators, the literature most often contains works analyzing effector control algorithms along a trajectory with low energy consumption and productivity [16][17][18]. For example, paper [16] presents the minimization of energy demand and reduction in the work cycle time for an industrial manipulator by using an algorithm known as a teaching-learning-based optimization.…”

“…Similar issues were discussed in ref. [17], where a reduction in energy demand by up to 20% was achieved by optimizing the trajectory in terms of energy consumption and the velocity of pick-and-place tasks. When analyzing teleoperated manipulators, in the literature, one can most often find works analyzing their energy efficiency during the operation of their drive systems, the implementation of the assumed effector trajectory, or energy recovery.…”