Abstract

The dataset is related to results of a genetic algorithm able to evaluate a suitable path for robotic manipulation of elastic objects. The goal is to find a path, given an initial and final position, that accounts for the distance covered by the robot while minimizing the forces perceived. These forces are generated by flexible objects that are difficult to model analytically. The proposed model-free approach considers these elastic forces in the fitness functions, making the genetic algorithm stiffness-aware. Furthermore, a dynamic exploration strategy allows for the convergence to effective solutions in a finite number of iterations. The data were produced in the framework of Horizon Europe INTELLIMAN project and are presented in the publication: Pasquali, Alex, et al. "Stiffness-Aware Genetic Algorithm for Robotic Path Finding Optimization." 2024 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), Boston, MA, USA, 2024, p. 942-947, doi: 10.1109/AIM55361.2024.10637048.