| Robotic manipulators have been used to perform a myriad of repetitive industrial tasks with varying degrees of success and precision over the past several decades. Their use in mechanical assembly tasks, however, has been relatively minor due to both their limitations in classical position-control paradigms and the difficulty in algorithmically describing the process of assembly. Though the technology for sensing and compliantly adapting to physical contact has improved, robotic assembly solutions are still largely relegated to simple responsibilities such as peg-in-hole and rigidly fixtured configurations. This dissertation represents the progressive development and assessment of self-guided learning for model-assisted robotic assembly applications. Utilizing industrial manipulators outfitted with six-degree of freedom (DoF) force/torque sensors for compliant motion control, a method for self-optimization of assembly search parameters is developed that allows the robot to determine when its performance has improved using simple metrics of success. Based on prior experiences, the robot then generates internal representations---or models---of the assembly process in order to attempt to predict when certain parameter sequences are likely to result in superior assembly performances. This method is further augmented to algorithmically determine the quality and anticipated effectiveness of the models based on their profiles in the parameter-performance mapping space. Analysis of simulations with arbitrarily-large N-dimensional parameter spaces suggest that even relatively simple models are capable of abstracting useful information of assemblies, even in the presence of noise. These results were then corroborated by running physical trials with and without assistive models on a variety of automobile part assemblies. |
