23 Jul 2024


Optimising robot moves can cut energy use by 40%

Swedish researchers say that they can cut the energy consumed by industrial robots by up to 40% by using an algorithm that optimises the movements of robot arms. The algorithm reduces the arms’ acceleration and deceleration, as well as the time they are at a standstill because they continue to consume energy when not moving.

The optimisation does not change the robot’s operation path, only its speed and the sequence of operations.

The potential savings are considerable because in robot-intensive manufacturing applications, such as bodywork operations in the automotive industry, robots can represent about half of all the energy used for production.

“We simply let the robot move slower instead of waiting for other robots and machines to catch up before carrying out the next sequence,” explains Professor Bengt Lennartson from the Chalmers University of Technology, who initiated the research together with, among others, General Motors.

“The optimisation also determines the order in which the various operations are carried out to minimise energy consumption – without reducing the total execution time,” he adds. “Thus, we can go into an existing robot cell and perform a quick optimisation without impacting production or the current cycle.”

To ensure safety, the movements of different robots operating in the same area need to be coordinated. The optimisation tool therefore initially identifies where the robots might collide, as well as the entry and exit positions for each collision zone, and for each robot path.

Tests conducted in Chalmers’ Robotics and Automation Laboratory show that for individual robots, the energy savings can amount to 15–35%, while for multi-robot systems, savings of up to 40% are possible.

“The first test results have shown a significant improvement – such as a 15–40% energy reduction – but the results are still preliminary,” says Dr Kristofer Bengtsson, a Chalmers researcher who is helping to implement the new optimisation strategy. “To estimate the actual energy savings, further testing in industry is required.”

The optimisation program starts by logging the movements of each robot during an operating cycle, as well as any collision zones. This information is processed by the algorithm, which generates new control instructions for the robots.

“The goal is to make this kind of optimisation standard and included in robots from the start,” says Bengtsson. “At each adjustment of the operating sequences, a new optimisation is conducted by default. But as we all know, it takes time to bring a development product into a robust production process, with several years of engineering work.”

The robot optimisation work being done at Chalmers University is part of a EU project called Areus which is developing hardware technologies to optimise bidirectional energy flows and to improve the use of renewable energy sources in factories. The project is also developing a new electrical power supply system that will reduce the energy consumed by robotic automation systems. There are ten members of the Areus consortium, spread across six EU countries, including Daimler, Kuka Robots and Danfoss.


The project is also developing a variable-voltage DC-based power network architecture that will re-use energy recovered from drive systems, and minimise the number of power conversion stages needed and thus the conversion losses. The aim is to eliminate all braking resistors and 30% of power converters. The use of DC, the absence of the skin effect, and reduced peak power demands, will also reduce the amount of copper needed.

The use of variable DC supply voltages could also reduce power peaks by up to 60%. External AC power compensation systems will be eliminated and power quality functions integrated into DC generation equipment.

The project is also aiming to use solar-generated electricity in production equipment and to integrate energy storage and backup systems.

As well as the hardware developments, Areus aims to integrate advanced mathematical models into commercial design tools to simulate sustainability. It will create detailed mathematical models  to simulate bi-directional energy flows and process energy costs. These models will be used for simulation, optimisation and control design