The Current Limits and Potentials of Autonomous Assembly

Tetsuyou Watanabe, Kensuke Harada1, Ryuta Ozawa2, Tokuo Tsuji3

  • 1Osaka University
  • 2Meiji University
  • 3Kanazawa University

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Imagenet large scale visual recognition challenge (ILSVRC) has increased the image recognition ability based on machine learning including Deep Neural Network, whereas Amazon Picking or Robotics Challenge has demonstrated that the image recognition methods are useful for robots to pick and place many types of objects. A pick-and-place task does not require accurate motion control, and the next challenge thus should involve the requirements for high motion accuracy. In this context, World Robot Summit industry category (WRS) was held to pursue the robot ability for completing a complex assembly task. However, even top teams did complete only several parts of the assembly tasks. It indicates not only a large gap between robots and human at assembling ability, but also the current limitations and potentials of robotic assembly technologies. The advancement of assembly technologies are required as a next step. To build up the next technologies, the reviewing of current state-of-the-art technologies are necessary. Based on this, this workshop aims at clarifying the current limits and potentials at robotic assembly by investigating the state-of-art technologies for robotic assembly as well as the assembly challenge program results, to accelerate the generation of new methodologies, strategies, and techniques for autonomous robotic assembly.

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