Trajectory optimisation in collaborative robotics based on simulations and genetic algorithms

The separation distance between humans and robots in manufacturing shop-floors has been progressively reduced, thanks to the modern safety functionalities available in robot controllers. However, the activation of these safety criteria usually stops the production or reduces the productivity of machines and robots. With the aim of improving this situation, this paper presents a real-time trajectory optimisation method for collaborative robots. The robot trajectory is parameterised at instruction level, i.e. through the parameters characterizing the robot motion instruction. A genetic algorithm randomly modifies the parameters of the nominal trajectory of the robot, thus obtaining new sets of candidate trajectories. Each trajectory is simulated on a digital twin of the collaborative workspace, which allows to reproduce and simulate the robot motion, and to represent the volume of the work-cell occupied by the human operator. A lexicographic optimization is used to evaluate online the optimal robot trajectory that simultaneously minimizes the risk of collision with the human operator and the trajectory traversal time. The method is validated in an industrial scenario involving the ABB YuMi dual-arm robot for a small parts assembly task.