ABB ROBOTSTUDIO CONTROLLED BY MATLAB LICENSE

This is an open access article under the CC BY-NC-ND license (). Usually identiп¬Ѓcation experimentsДў212-8271 В© 2014 Published by Elsevier B.V. Data is gathered by moving the robot along a periodic excitation trajectory, programmed in RAPIDcode, and measuring all motor currents using current clamps inside the robot controller. The measurements necessary to determine this dynamic model are obtained by carrying out identiп¬Ѓcation experiments on an IRB1600 robot. For this purpose, п¬Ѓrst of all a dynamic model needs to be available enabling accurate torque simulation during robot movement. This paper describes a methodology to obtain energy-optimal trajectories for an ABB IRB1600-1.45m robot and to automatically generate the RAPID-code needed to program the robot. Commercial robot-programming software, such as ABBвЂ™s RobotStudio, only allows programming linear and joint movements and does not have the possibility of energy-optimisation, thereby discouraging the robot programmer from taking energy consumption into consideration. Currently, in the majority of the cases, industrial robots move along trajectories which are far from energy-optimal, especially when the robot has a certain amount of idle time during its cycle. This research focuses onДЄn alternative approach to reduce energy consumption, namely by programming robot trajectories in a more intelligent way. The drawback of these methods however lies in the fact that they all require severe changes in robot hardware and come with a high initial investment cost, which is why many companies are still reluctant to implement them. An excellent survey of these methods is provided by Meike and Ribickis. These methods include the use of an intelligent braking management system or the temporal storage of energy in a capacitive buп¬Ђer. Several methods have been developed over the years, showing great potential to reduce the energy consumption of an industrial robot. This research focuses on the possibility of energy saving in industrial robotics. In fact, the European commission has set the goal of reducing the energy consumption of primary energy users with 20% by the year 2020, as compared to 2007. Reduction of our energy consumption, both on a household and on an industrial scale, therefore seems primordial from an economical as well as an ecological point of view. Introduction The past decade has shown a continuous increase in the price of electricity in the industrialised world and all signs point toward an even steeper increase in the future. Keywords: industrial robot, energy eп¬ѓciency, path planning, quadratic programmingДЎ. Additional stand-still experiments show that activation of the brakes is favoured over an actuated stand-still from an energy point of view, assuming that the start-up time when releasing the brakes is limited. The most remarkable result is that the fastest energy-optimal trajectory turns out to be 4% more energy eп¬ѓcient and 3% faster then the commercially available fastest trajectory. Simulation results show a signiп¬Ѓcant time and energy improvement (up to 5%) compared to most trajectories generated by the ABB software. To demonstrate the possibility of energy saving by вЂintelligentlyвЂ™ programming a robot trajectory the energy and time-optimal paths are generated taking all physical constraints into account. The obtained dynamic robot model is given and compared to the collected measurements. The systematic methodology is validated on an IRB1600 industrial ABB robot performing a custom pick-andplace operation. The resulting energy-optimal path is translated into custom robot commands executable on an industrial robot. A sequential quadratic programming solver is used to minimise a mechanical energy based cost function. the limited workspace and the constraints on the joint velocities and accelerations. The model is used as a dynamic constraint for a model predictive control problem, where other physical constraints are added i.e. This routine results in the speciп¬Ѓcation of a parametric dynamic robot model. Next, the collected data is compared with a parametrised dynamic robot model in an optimisation routine. The identiп¬Ѓcation experiments are carried out on-site, in a quick, non-invasive way using a CA8335 Qualistar three-phase electrical networks analyser. Of Mechanical Engineering, KU Leuven, campus Group T, Andreas VesaliusstrLeuven, Belgium of Mechanical Engineering, KU Leuven, Celestijnenlaan 300A, 3000 Leuven, BelgiumДЄbstract This paper presents a systematic methodology for on-site identiп¬Ѓcation and energy-optimal path planning of an industrial robot. Available online at ScienceDirect Procedia CIRP 15 (2014) 105 вЂ“ 110Дў1st CIRP Conference on Life Cycle EngineeringД®nergy eп¬ѓcient trajectories for an industrial ABB robot Koen Paesa, Wim Dewulfa, Karel Vander Elsta, Karel Kellensb, Peter Slaetsa a Department

Abb robotstudio controlled by matlab