Humanoid Loco-Manipulations Using Combined Fast Dense 3D Tracking and SLAM With Wide-Angle Depth-Images

Abstract

To efficiently achieve complex humanoid loco-manipulation tasks in industrial contexts, we propose a combined vision-based tracker-localization interplay integrated as part of a task-space whole-body optimization control. To achieve good perception complementarity between manipulation and localization, a new fast dense 3D model-based tracking using wide-angle depth image is developed and used in conjunction with a simultaneous localization and mapping software. Our approach allows humanoid robots, targeted for industrial manufacturing, to manipulate and assemble large-scale objects while walking. It is assessed with experiments consisting in rolling and assembling in an unwinder a heavy and wide bobbin using bimanual grasping and bipedal locomotion at a time. This experimental use-case is found in some large-scale manufacturing where bobbins are enrolled with various materials (cables, papers, rubbers, etc.). The same experiments are made using two different humanoid robots of the same family. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper aims at deploying humanoid robots in large-scale manufacturing industries. We consider non-added value tasks related to transporting large tools or objects such as large bobbins by means of locomanipulation skills, similarly to human workers. We developed a task-space control framework that has been successfully applied in the aircraft industry. In the frame of a current collaboration with other major industrial sectors, we enhanced our control framework to interplay between SLAM and visual tracking to realize robust loco-manipulation tasks. Our approach can be applied and ported to any humanoid robot or bi-manual wheeled mobile robots with minor programming effort as the software is made open. Preliminary experiments with two different humanoids and use-cases suggest that our approach is feasible. In future research, we will address the problem of performance to reach at least human-speed in the execution of locomanipulation tasks in large-scale industry and automation contexts.