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Stiffness Analysis of Crank Motion Task using Musculoskeletal Model with Sixmuscles
Nobutomo MATSUNAGA,Ryohei MERA,Hiroshi OKAJIMA,Shigeyasu KAWAJI 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
It is important to clarify that how are the stiffness and the torque of human arm controlled in the interaction with the environment. Traditionally, the stiffness of human arm has been measured by adding perturbation. However, it is difficult to measure the stiffnes sinisotonic movements with various loads, because the perturbation provides the unexpected stiffness increase by an tagonistic muscles. In this paper, the musculoskeletal model with six musclesis used to estimate the joint torque and stiffness incrank rotation tasks. The parameters of the model are estimated by experiments. And the pre-programmed reaction for the predictable load torque are evaluated.
Evaluation of Multilayered Pain Sens or Model of Human Skin
Nobutomo MATSUNAGA,Aydin Tarik ZENGIN,Shigeyasu KAWAJI 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
In the coexistence circumstance with humans and robots, sensory and emotional feelings of human should be considered incase of interaction. A typical unpleasant feeling that stems from this interaction is pain. We have developed a new multi-layered pain sensor similar to the laminated structure of human skin, to clarify this feeling which is abstract for robots. The dynamical characteristics of this sensor in either cases of slow and fast stimuli were evaluated in experiments. For both cases, the pain sensor showed a similar reaction with real human measurements.
Formation path learning for cooperative transportation of multiple robots using MADDPG
Kenta Miyazaki,Nobutomo Matsunaga,Kazuhi Murata 제어로봇시스템학회 2021 제어로봇시스템학회 국제학술대회 논문집 Vol.2021 No.10
The cooperative transportation using multiple robots has been expected to be used in factories and construction sites. Cooperative transportation can support various situations compared to transportation using one robot. However, the industrial application of cooperative transportation has not been advanced due to the complexity of formation change. In this paper, the formation change using multi-agent deep deterministic policy gradient (MADDPG) is proposed, which is a deep reinforcement learning method specialized for multi-agent systems. The effectiveness of method is evaluated by simulations for multi-agent system.
Development of an automatic picking system for assistive robots using MR
Naoki Miyamura,Nobutomo Matsunaga,Hiroshi Okajima 제어로봇시스템학회 2021 제어로봇시스템학회 국제학술대회 논문집 Vol.2021 No.10
Welfare vehicles are widely used for disabled or elders, however, it is difficult for users to pick up their package while they are in the vehicle. For these picking tasks, the automatic mobile robot with a robot arm has been used. The pointing of the package and its actions should be instructed at first. For disabled or elders, however, it is desirable to give instructions as simple as possible to operate the robot. Recently, Mixed Reality (MR) devices attract attention to support these tasks. Users will give instructions for the robot intuitively by using an HMD with MR. In this paper, an assist robot in which a robot arm automatically sucks the package is proposed by indicating the rough position using HMD. We have developed a system in which the user commands an assist robot to handle packages from a distance location using an HMD and an assist robot picks packages automatically using AR markers.