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1 June 2020 Robotic motion generation for realization of the target task using function and poses of objects
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Proceedings Volume 11515, International Workshop on Advanced Imaging Technology (IWAIT) 2020; 115150T (2020) https://doi.org/10.1117/12.2566898
Event: International Workshop on Advanced Imaging Technologies 2020 (IWAIT 2020), 2020, Yogyakarta, Indonesia
Abstract
In this paper, we propose a method for automatically determining the motion parameters of robots to execute target tasks such as “scooping powdered tea and putting it into a teacup”. For robots to handle everyday objects, it is necessary to determine the motion parameters of robots for handling objects of various shapes and sizes. There are two methods for determining motion parameters. One involves using a 3D model of an object and the other does not involve using such a model. The latter method is effective in places where there are a wide variety of everyday objects such as in homes. However, it is assumed with this method that an object is placed face up. Therefore, this method cannot be used when an object is placed face down. We propose a method for determining motion parameters for handling changes in the shapes, sizes and poses (face up or face down) of objects. Our method uses a 3D deep neural network to recognize an object’s functions (e.g., “scoop” and “grasp”) and recognizes the poses of an object from the function information. Motion parameters are then determined based on the recognition results. We conducted an experiment to evaluate the performance of the method by testing it on five spoons of different shapes, sizes, and poses. The method had a success rate of approximately 86%.
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Takuma Terasawa, Kentaro Kozai, and Manabu Hashimoto "Robotic motion generation for realization of the target task using function and poses of objects", Proc. SPIE 11515, International Workshop on Advanced Imaging Technology (IWAIT) 2020, 115150T (1 June 2020); https://doi.org/10.1117/12.2566898
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