JRM Vol.28 No.2 pp. 242-254
doi: 10.20965/jrm.2016.p0242


Teaching Mobile Robots Using Custom-Made Tools by a Semi-Direct Method

Jorge David Figueroa Heredia*, Hamdi Sahloul*, and Jun Ota**

*Department of Precision Engineering, School of Engineering, The University of Tokyo
7-3-1 Hongo, Bunkyo, Tokyo 113-8656, Japan

**Research into Artifacts, Center for Engineering (RACE), The University of Tokyo
5-1-5 Kashiwanoha, Kashiwa-shi, Chiba 277-8568, Japan

November 12, 2015
March 3, 2016
April 20, 2016
grasping objects, teaching data, service robots, custom-made tools, home and office users

Teaching Mobile Robots Using Custom-Made Tools by a Semi-Direct Method

Teach grasping point by custom-made tool

We propose a method for conveying human knowledge to home and office assistance robots by teaching them how to perform the process of grasping objects with a custom-made tool. Specifically, we propose a semi-direct teaching method that respects the limitations of the hardware on the robot while utilizing human experience for intuitive teaching. We specify the information necessary for grasping objects through the generation of teaching data, which include the grasping force, relative position, and orientation. To respect the hardware limitations and at the same time allow inexperienced users to perform the teaching process easily, we used a teaching tool that possesses the same mechanism as the end effector of the robot. To simplify the teaching, we developed a sensing system that would reduce the teaching time with accurate measurements. Subsequently, the robot would use the teaching data to grasp the object. Experiments conducted using volunteers demonstrated the validity of the proposed method, wherein the teaching data for three different tasks were generated in less than 30 s each and accurate measurements were obtained for both the grasping position and force for grasping the objects.

Cite this article as:
J. Heredia, H. Sahloul, and J. Ota, “Teaching Mobile Robots Using Custom-Made Tools by a Semi-Direct Method,” J. Robot. Mechatron., Vol.28, No.2, pp. 242-254, 2016.
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Last updated on Nov. 16, 2018