Title | ||
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Towards a Programming-Free Robotic System for Assembly Tasks Using Intuitive Interactions |
Abstract | ||
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Although industrial robots are successfully deployed in many assembly processes, high-mix, low-volume applications are still difficult to automate, as they involve small batches of frequently changing parts. Setting up a robotic system for these tasks requires repeated reprogramming by expert users, incurring extra time and costs. In this paper, we present a solution which enables a robot to learn new objects and new tasks from non-expert users without the need for programming. The use case presented here is the assembly of a gearbox mechanism. In the proposed solution, first, the robot can autonomously register new objects using a visual exploration routine, and train a deep learning model for object detection accordingly. Secondly, the user can teach new tasks to the system via visual demonstration in a natural manner. Finally, using multimodal perception from RGB-D (color and depth) cameras and a tactile sensor, the robot can execute the taught tasks with adaptation to changing configurations. Depending on the task requirements, it can also activate human-robot collaboration capabilities. In summary, these three main modules enable any non-expert user to configure a robot for new applications in a fast and intuitive way. |
Year | DOI | Venue |
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2021 | 10.1007/978-3-030-90525-5_18 | SOCIAL ROBOTICS, ICSR 2021 |
Keywords | DocType | Volume |
Robotic manipulation, Multimodal perception, Object and task teaching, Grasping and insertion, Human-robot collaboration | Conference | 13086 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 8 |
Name | Order | Citations | PageRank |
---|---|---|---|
Nicolas Gauthier | 1 | 0 | 2.03 |
wenyu liang | 2 | 0 | 1.01 |
Qianli Xu | 3 | 90 | 15.17 |
Fen Fang | 4 | 0 | 2.03 |
Liyuan Li | 5 | 48 | 13.24 |
Ruihan Gao | 6 | 0 | 0.34 |
Wu Yan | 7 | 38 | 8.09 |
Joo-Hwee Lim | 8 | 783 | 82.45 |