JRM Vol.35 No.6 pp. 1524-1531
doi: 10.20965/jrm.2023.p1524

Development Report:

Application of Object Grasping Using Dual-Arm Autonomous Mobile Robot—Path Planning by Spline Curve and Object Recognition by YOLO—

Naoya Mukai, Masato Suzuki, Tomokazu Takahashi, Yasushi Mae, Yasuhiko Arai, and Seiji Aoyagi

Kansai University
3-3-35 Yamate-cho, Suita, Osaka 564-8680, Japan

June 14, 2023
October 26, 2023
December 20, 2023
ROS, YOLO, spline curve

In the trash-collection challenge of the Nakanoshima Robot Challenge, an autonomous robot must collect trash (bottles, cans, and bentos) scattered in a defined area within a time limit. A method for collecting the trash is to use machine learning to recognize the objects, move to the target location, and grasp the objects. An autonomous robot can achieve the target position and posture by rotating on the spot at the starting point, moving in a straight line, and rotating on the spot at the destination, but the rotation requires stopping and starting. To achieve faster movement, we implemented a smooth movement approach without sequential stops using a spline curve. When using the training data previously generated by the authors in their laboratory for object recognition, the robot could not correctly recognize objects in the environment of the robot competition, where strong sunlight shines through glass, because of the varying brightness and darkness. To solve this problem, we added our newly generated training data to YOLO, an image-recognition algorithm based on deep learning, and performed machine learning to achieve object recognition under various conditions.

Object grasping under sunlight shining

Object grasping under sunlight shining

Cite this article as:
N. Mukai, M. Suzuki, T. Takahashi, Y. Mae, Y. Arai, and S. Aoyagi, “Application of Object Grasping Using Dual-Arm Autonomous Mobile Robot—Path Planning by Spline Curve and Object Recognition by YOLO—,” J. Robot. Mechatron., Vol.35 No.6, pp. 1524-1531, 2023.
Data files:
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Last updated on Feb. 19, 2024