On uncertainty handling in robot part-mating planning.

Abstract

A key problem in robotics application on high-precision tasks, such as assembly tasks, is how to make robots operate reliably in the presence of uncertainties (such as mechanical, control, and sensor uncertainties). Since there is no general and unconditional solution for the problem; the uncertainty handling for robot assembly must be a dynamic process involving sensory information and general knowledge of contacts among the parts being assembled, and its success can only be guaranteed if certain constraints on the nominal design parameters, tolerances, and sensor error parameters are enforced. Based on the above belief, this dissertation introduces a replanning approach towards uncertainty handling by presenting a task-independent replanning strategy, using knowledge of contact and sensory data, and showing how eventual success of a task can be guaranteed in spite of certain class of sensor, control and manufacturing imperfections if certain design and motion constraints are satisfied.