Energy-Efficient Motion Planning for Dual-Arm Collaborative Robots Using a Digital Twin

The growing adoption of collaborative robots (cobots) in industrial applications has intensified the focus on optimizing energy efficiency. This study develops a digital twin for dual-arm ABB GoFa5 robots to estimate power consumption by integrating real-world sensor data for real-time monitoring and analysis. To understand how motion planning impacts energy usage, different algorithms, including CHOMP, RRT, and RRT*, were evaluated under no-load and loaded (2.5 kg) conditions. CHOMP demonstrated the highest energy efficiency, consuming up to 28% less power than RRT and 14% less than RRT* under no-load conditions, and up to 19% and 13% less, respectively, with a payload, confirming its effectiveness in optimizing cobot performance.