Admittance Selection for Force Guided Assembly with Optimal Motion

Fernando Rodriguez Anton, Marquette University


Current robots lack the precise relative positioning necessary to complete automatic assembly tasks. Several solutions have been proposed. Some approaches use complex vision and force sensing systems to generate corrective motion if misalignment is present in the assembly task. Other solutions rely on generating elastic behavior, known as compliance, between the end eector and the held movable part. This compliant mechanism helps guide the movable part of the assembly into its proper position. The project focuses on designing a process by which passive compliant systems can achieve successful assembly for a range of misalignment and generate error-reducing motion that is considered of high quality. This is accomplished by using a velocity metric as the goal of a constrained optimization. The metric uses the average discrepancy of all the particle motion from an established "best motion". This motion minimizes the discrepancy in the velocity of all particles motion from their ideal motion towards their proper position. This procedure identifies the best worst case scenario for a representative set of configurations. The results obtained for optimization over polygonal geometries of 3, 4, and 5 vertices, demonstrate the effectiveness of the procedure in designing passive compliant behavior resulting in high quality error-reducing motion. Results also show that high quality motion is not only achieved for a set of finite configurations but also for all intermediate ones.