Autonomous Execution of Insertion Operations in Space Assembly Tasks

Abstract

Robotic in-space assembly is a key technology to enable fabrication of large structures in space. A critical task of robotic assembly is insertion, where uncertainty in pose from factors such as positional sensor errors and abruptly changing environmental conditions may cause misalignment and ultimately lead to jamming. In this work, we demonstrate a robotic arm performing an insertion task of geometry with significant complexity. The robotic system utilizes a two-stage vision-based algorithm to provide pose estimation prior to attempting insertion. The first stage provides a coarse pose estimate which allows the camera mounted to the wrist of the robotic arm to be moved to a closer view of the target. The second stage, a learning-based model, provides a fine pose estimate of the assembly misalignment. The fine pose estimate is then used to perform insertion using an impedance controller. In our experiments, we show the learning-based model's pose estimation capability under varied lighting conditions and demonstrate that the model can be trained to be robust to lighting conditions unseen in training data. Additionally, we tune an impedance controller to actively comply to misalignments during insertion, which further improves insertion success rate. Overall, we demonstrate an autonomous robotic insertion algorithm which provides robustness to uncertainty through state estimation as well as controls.