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Here is how to cut a robot cell's energy consumption by 23%

Industrial robots consume approximately 8% of the total energy in production processes. With this in mind, any approach that can reduce the energy consumption of robotic cells, without affecting the productivity, should be explored and adopted by industrial companies. Especially when the costs of electricity are increasing, and there is a general demand to reduce energy consumption.

Used ABB RobotStudio

The research presented in this white paper aims at testing how the integration of industrial Offline Programming (OLP) simulation system with multi-objective optimization can find the optimal trade-off between cycle time and energy consumption of a real robotic cell. In this study we used ABB’s RobotStudio (RS) as the powerful robot simulation and programming environment.

Can be implemented on new, or existing installation

Equipped with the robot Virtual Controller (VC) as an exact copy of the software that runs on a physical robot controller, RS allows very realistic simulations and accurate estimation of robot energy consumption. RS also facilitates the creation of Digital Twin when a real robot controller in a production cell can be connected to its virtual counterpart for the purpose of offline programming and virtual commissioning. The usage of OLP allows accurate estimation of cycle time, peak power, and energy consumption, as well as validation of path feasibility, which are the essential simulation outputs required for this multi-objective optimization study. The proposed approach can be used during the planning stage, virtual commissioning, or in the optimization of an existing robotic cell.

Saving up to 23.3% of the energy

Our proposed RS-based multi-objective optimization has been tested on analyzing a robotic cell which consists of an ABB IRB4600 robot, controlled by an IRC5 controller running RobotWare 5.15, responsible for the material handling of three operations, as shown in the snapshot of the simulation model below.

Fig. 1 proposed optimization. Fig 2. Energy saving chart.
Fig. 1. Proposed RS optimization. Fig 2. Energy saving chart.

When comparing the two Pareto fronts generated with and without the additional layout decision parameters, the one with optimized layouts has shifted the front toward optimal solutions that could lead to the reduction of peak power by 44.7%, or the energy saving of 23.3% (see the data plot above). Moreover, in this study, we have found that on a multi-core computer the computational performance of optimization can be improved by running multiple instances of RS, up to an optimal number of parallel simulation instances.

Video demonstration

Watch the video demonstration of how the multi-objective optimization works with RS and the 3D visualization of the optimized layouts projected onto Microsoft HoloLens.

The research presented in this white paper is performed by Bernard Schmidt, Amos H.C. Ng from the University of Skövde and Magnus Seger from ABB.

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