New Framework Balances Speed, Energy, and Worker Safety in Robot-Human Factory Cells
Swedish researchers have developed an optimization method that lets manufacturers design shared human-robot workstations without sacrificing worker welfare for productivity gains. The approach simultaneously addresses conflicting demands—faster robots, lower energy use, and better ergonomics—making it practical for small-batch, custom production runs where flexibility matters most.
Originaltitel: Multi-disciplinary Optimization for Designing Human-Robot Collaborated Work-Cell for Low-Volume and High-Variant Production
<p>Human–robot collaboration solutions have gradually become popular, in which some tasks are performed by robots and others by humans. Designing such a production cell requires simultaneous consideration of human-centered factors, machine-focused mechanical design, and system engineering in the early planning stages. However, different objectives often conflict (e.g., speeding up a robot can improve productivity while compromising energy efficiency), and the same variables can affect multiple models and simulations simultaneously (e.g., a machine where humans and robots collaborate can influence both the operator’s working posture and the robot’s cycle time). Therefore, multidisciplinary tools and multi-level optimization are needed to model, simulate, and optimize elements such as production flows, robotics, and human operators to balance objectives related to cycle time, energy consumption, and worker well-being. In this paper, we formulate an approach that integrates different simulation tools and a bi-level optimization framework to balance worker well-being, cycle time, and energy consumption. We demonstrate this approach through a real industrial case of designing a work cell for elevator pipe assembly in a grain conveying system, where ABB RobotStudio is used for robotic simulation and IPS IMMA for human simulation. IBM ILOG CPLEX Optimization Studio is employed for the top-level task allocation optimization, and a set of results is presented based on data extracted from the lower-level robot-centered optimization. The results show that our approach can effectively balance different objectives by incorporating detailed information from different levels of the work cell design.</p>