Energy Saving Possibilities in the Industrial Robot IRB 1600 Control Anton Rassõlkin, Hardi Hõimoja, Raivo Teemets Tallinn University of Technology/Department of Electrical Drives and Power Electronics, Tallinn (Estonia) anton.rassolkin@tptlive.ee, hardi.hoimoja@ttu.ee, raivo.teemets@ttu.ee Abstract- The paper presents the approaches for electric energy saving possibilities and electricity consumption characteristics in the modern industrial robots together with practical examples concerning robot programming and positioning The paper is based on measurements, made in the laboratory of Tallinn University of Technology with an industrial robot IRB 1600 I INTRODUCTION The development of modern robotic technologies is a matter of different subdisciplines with constantly augmenting application in various manufacturing processes An industrial robot might be observed as an actuator mechanism, requiring energy for motion As the environmental assets are limited, more and more attention is paid to energy saving opportunities and robotics is not an exception The very first energy saving option is based on the advantage of robots before humans, i.e on the fact that robots can operate in dark and cold environments, which means fewer expenses on lighting and heating The current paper casts additional light to some energy saving possibilities in industrial robots with improved control methods Modern industrial robots are essentially intelligent assemblies, able to choose the optimal operation mode, motion trajectory and other parameters on their own [1] The manufacturing companies often not disclose the full data about their products, as this may affect their competitive market potential, therefore this paper is based on the independent measurements carried out on a conventional industrial robot without prior detailed information about its construction The presented measurements were made on the ABB robot system, located in the laboratory of Department of the Electrical Drives and Power Electronics at the Tallinn University of Technology The central part of the studied robotic system is the welding robot IRB 1600, manufactured by ABB To determine the effect of different control possibilities on industrial robot energy consumption, four experiments were made: 1) determination of optimal motion trajectory; 2) determination of optimal tool weight; 3) determination of optimal workpiece position; 4) determination of optimal operation speed In the next sections these experiments are described in more detail 226 II.ENERGY SAVING BASICS IN ROBOTIC SYSTEMS A The Essence of Energy Saving Energy saving means reducing both energy consumption and losses in manufacturing processes Frequent change of temperature, caused by the inner losses, bring along rapid aging and deterioration of devices Therefore, fighting the losses can extend the life cycle of devices and minimise the repair costs Energy-efficient operation is important also from the viewpoint of the market economy, because it reduces energy transmission costs and losses; increases duty time of the energy storage units and provides an opportunity to reduce the capacity and costs of such units, reduces costs of energy per product, thus increasing their competitiveness Another aspect of energy savings is related to mobile robots, operating on batteries [2][3] It is self-evident, that increasing the operational efficiency of such robots yields increased running time and autonomy B Motion Characteristics of a Manipulator Industrial robot IRB 1600, used in described measurements, has degrees of freedom (DOFs) Each DOF has its own synchronous motor The motor data, unfortunately undisclosed by the manufacturer, can give a basis to investigate the energy consumption of an industrial robot Moreover, the use of electric motors itself is related to hardly noticeable and measurable losses concerning friction, heat and magnetic leakage [4] So it is more reasonable to carry out practical measurements and assess the energy consumption of the robot on a concrete example Industrial robot energy consumption depends on the characteristics of its movement Different trajectories mean the involvement of different DOFs, which in turn means operating different motors That is why it was necessary to select such a path that engages all DOFs, represented by the ∞-sign like closed trajectory, lopsided in one plane The active power exerted by the robot’s mechanics is expressed by the equation [5] Probot = n ∑ Ti ⋅ ωi ⋅ i =1 n , (1) ∏η mec,i ⋅ηel ,i i =1 where n is the number of DOFs, Ti is the torque applied to the ith DOF, ωi the angular velocity of the ith DOF, ηmec,i and ηel,i 978-1-4244-8807-0/11/$26.00 ©2011 IEEE mechanic and electric efficiencies of the ith DOF drives, respectively The active energy consumed by a robot is the integral of active power over time tf: Wact = tf ∫0 Probot ⋅ dt (2) power in case of nearly horizontal movement is insignificantly higher than in case of nearly vertical movement This can be explained by the fact that during one half of the trajectory, the gravity has the same sign with the motion [6], thus motors in generating quadrant supply other motors in motoring quadrant over the common dc bus In ac circuits also reactive power flows exist, though the use of a common diode rectifier (Fig 1) must yield unity power factor The differing results, discussed below, can only be explained by undisclosed data about the research object III.MEASUREMENTS ON THE IRB 1600 ROBOT Fig Measured trajectory in the horizontal xy-axis plane Mains A Measurement Conditions Studies were made with 3-phase power quality analyzer Fluke 434 Measurement points were chosen with provision of losses and power consumption of other functional units (Fig 1), for example the controller itself poses almost the same load (0.3 kW) than the manipulator in low duty (0.43 kW) Fig Generalized power diagram of the IRB 1600 robot During the experiments robot was moving along the preprogrammed path In each measurement the robot repeated the path 50 times, each measurement repeated three times in order to reduce the random error Additional conditions, such as speed, movement character, weight of the tool etc are explained separately for each experiment B Determination of the optimal motion trajectories The load of robots drives depends on the movement direction When the manipulator moves almost vertically, then the gravity has the opposite direction with upward movements and the same direction with downward movements If the manipulator moves almost horizontally, the gravity force has the same influence in both directions In the first part of experiment the points P1, P2 and P3 were parallel to robot y-axis on the xy-plane, with the sketch shown in Fig In the second part of experiment the points P1, P2 and P3 were chosen parallel to robot z-axis on the yz-plane, as shown in Fig Additional conditions of measurements were as follows: number of cycles – 50, the tool weight - 2.5 kg; velocity - 500 mm/s Both experiments lasted 305 s Fig illustrates the results of measurements: the real power as well as the apparent Fig Measured trajectory in the vertical yz-axis plane Active energy [Wh] Reactive energy [VArh] [varh] 23.3 Vertical 10.4 24.0 Horizontal 11.4 10 15 20 25 30 Fig Robot’s energy consumption at different motion directions In case of the horizontal motion the x-axis is leaned forward, thus increasing the effective radius affecting the moment of inertia From the classical equation of motion Ti = T L ,i + J i dω i , dt (3) where TL,i is the static load and Ji the moment of inertia, it might be concluded that increased Ji yields additional energy need, as theoretically explained by Eq (1) and (2) as well as the conducted experiment C Effect of the Tool Weight on the Energy Consumption Industrial robots have different payloads, depending on the robot’s weight and application The weight of a robot tool can 227 be permanent (e.g a welding robot) or variable (e.g a pickand-place robot) During this test robot was loaded with three different weights: 1) kg – without payload; 2) 2.5 kg – the weight of the tool used in studying process; 3) kg – the maximum possible payload of IRB 1600 Additional conditions of measurements were as follows: the plane of the movements - horizontal; velocity - 500 mm/s Experiments with three possible payloads lasted 305 s like during the previous measurements The results, shown on Fig 5, can be explained by the robot motor characteristics In the permanent magnet synchronous motors, the current is proportional to the torque, which depends on the tool’s weight The small differences are due to the fact, that the payloads are relatively small compared to the weight of the robot’s links In larger robots where payloads are heavier, the differences are even more remarkable Active energy [Wh] kg Fig Determination of the optimal workpiece position 24.0 11.4 kg 10.5 Reactive energy [VArh] [varh] 24.3 11.7 2.5 kg material, detail thickness etc [7] Typical speed for pick-andplace robots is around 3000 mm/min - 15000 mm/min During the experiments the manipulator was moving along the pre-programmed path 50 times with different speeds The speed was increased from 100 mm/s to the maximum, the latter depending on the load Additional conditions of measurements were as follows: the plane of the movements horizontal, the tool weight - 2.5 kg Active energy [Wh] 22.8 10 -1050 15 20 25 9.7 -900 9.2 Fig Robot’s energy consumption at different tool weights D Determination of Optimal Workpiece Position The main objective of this test was to get to know how the energy consumption of the robot depends on the workpiece position The reference point of the IRB 1600 robot was defined by its home position, as shown in Fig During the measurements robot was moving alongside the preprogrammed path on 10 different heights One was 150 mm over the reference position and other ones were below, decreasing by 150 mm increments The lowest working plane was on the same level with the manipulator’s base, determined by the possible working range All the experiments lasted 305 s Test results are presented on Fig The most energyefficient position of workpiece is 600 mm above the manipulator’s base plane, the highest energy consumption is above the reference position Usually the workpiece is on a conveyor line or a positioner As follows, the positioner IRBP 250 L used in the robotic system with IRB 1600 is preferably located 600 mm above the base plane The positioner’s location is selected taking into consideration the kinematical characteristics, so that operation are is the broadest A Determination of Optimal Operating Speed Working speed of the robot depends on the actual operation mode For example, a typical welding speed is in the range of 100 mm/min - 500 mm/min, depending from welding current, 228 -750 19.8 7.9 -300 20.8 8.5 -150 9.8 21.3 19.1 7.7 -450 21.8 19.6 8.3 -600 22.5 24.0 11.4 +150 27.9 12.7 Reactive energy [VArh] [varh] 10 15 20 25 30 Fig Results of the optimal workpiece position measurements The results of the optimal operational speed determination are shown in Fig Under the given condition the lowest energy consumption was at 600 mm/s Although reducing the motion speed can minimise the energy consumed by the robot, the increase in the time needed to carry out the operations counteract to the set objectives by additional energy consumption Energy savings in terms of speed reduction is not always thinkable, especially when it comes to mass production, where the duration of a cycle is crucial [8] 13.3 1000 mm/s 11.9 800 mm/s 11.5 600 mm/s 10.7 500 mm/s 11.4 400 mm/s 22.5 6000 40 5000 30 4000 20 10 1000 25.6 60 50 2000 0 100 200 300 400 500 600 800 1000 max 28.8 Velocity [mm/s] 10 Fig Optimal operational speed vs productivity 35.6 56.3 21.8 VAh/cycle 3000 24.0 14.4 100 mm/s kVAh/year 7000 22.8 12.2 200 mm/s 8000 23.2 11.4 300 mm/s 9000 26.1 kVAh/year max 70 10000 Reactive energy [varh] [VArh] VAh/cycle Active energy [Wh] 20 30 40 50 60 Fig Results of the optimal operation speed measurements Distributed systems are also one possibility to use the robots more rationally [10] The main point of distributed system is to use multiple robots in the system with a upstream main controller, which coordinates and forecasts the actions of the individual robots on the basis of minimal energy and maximal productivity ratio [11] IV.CONCLUSIONS AND FUTURE WORK ACKNOWLEDGMENT A Results of the Measurements The results of the measurements can be divided into two parts: the results of two first tests – determination of optimal trajectory (Fig 4) and energy consumption with different tools weight (Fig 5) – not yield enough energy savings, explained by the low capacity of IRB 1600, where the bulk of the weight is constituted by the mass of the joints themselves; the results of two other tests – determination of optimal workpiece position (Fig 7) and optimal operating speed (Fig 8) – give already some hints for more essential energy savings In that case one can conclude that a properly installed and correctly tuned robot can operate with improved energy efficiency This research work has been supported by Estonian Ministry of Education and Research (Project SF0140016s11) and Estonian Archimedes Foundation (project „Doctoral School of Energy and Geotechnology-II“) B Economic Benefits Though the differences in consumed energy, determined during performed experiments might seem insignificant, one must remember that an industrial robot is often running continuously Thus, when multiple robots are applied in an industrial process, the yearly savings would be remarkable [9] In terms of economic benefits, finding a relationship between optimal operational speed and yearly energy consumption might be interesting In Fig 9, this optimum is defined as the intersection point between the two curves, in current case approximately 700 mm/s C Future Prospects To improve the results it would be useful to repeat the tests with a more powerful robot Carrying out additional test like investigating the performance of a robotic system as a part of a smart grid would be a topic REFERENCES [1] W Khalil, Modeling, identification & control of robots London, Kogan Page Science, 2004 [2] Y Mei et al., “Energy-efficient motion planning for mobile robots” IEEE International Conference on Robotics and Automation, vol pp 4344-4349, 2004 [3] Y Mei et al., “Energy-efficient mobile robot exploration” IEEE International Conference on Robotics and Automation, pp 505-511, 2006 [4] E.S Sergaki, G.S Stavrakis, A.D Pouliezos, “Optimal Robot Speed Trajectory by Minimization of the Actuator Motor Electromechanical Losses”, Journal of Intelligent and Robotic Systems, vol 33, pp 187– 207, 2002 [5] H Choset et al., Principles of robot motion: theory, algorithms, and implementation London : MIT Press, 2005 [6] D Verscheure et al., “Time-Energy Optimal Path Tracking for Robots: a Numerically Efficient Optimization Approach”, IEEE 10th Annual Workshop on Advanced Motion Control, pp 727-732, 2008 [7] J.N Pires, A Loureiro and G Bölmsjo, Welding Robots Technology System Issues and Applications London, Springer, 2006 [8] S.A Alshahrani, H Diken, A.A.N Aljawi, “Optimum trajectory function for minimum energy requirements of a spherical robot”, The 6th Saudi Engineering Conference, vol 4, pp 613-625, 2002 [9] Y Li and G.M Bone, “Are Parallel Manipulators More Energy Efficient?” IEEE International Symposium on Computational Intelligence in Robotics and Automation, pp 41-46, 2001 [10] T.D Ngo, H Raposo, H Schioler, “Potentially Distributable Energy: Towards Energy Autonomy in Large Population of Mobile Robots”, IEEE International Symposium on Computational Intelligence in Robotics and Automation, pp 206-211, 2007 [11] A Vergnano et al., “Embedding detailed robot energy optimization into high-level scheduling“ IEEE 6th Annual Conference on Automation Science and Engineering, pp 386-392, 2010 229