日本ロボット学会誌
Online ISSN : 1884-7145
Print ISSN : 0289-1824
ISSN-L : 0289-1824
29 巻 , 5 号
選択された号の論文の17件中1~17を表示しています
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論文
  • —ロボットの意外な振る舞いは意図帰属の原因となる—
    寺田 和憲, 伊藤 昭
    2011 年 29 巻 5 号 p. 445-454
    発行日: 2011年
    公開日: 2011/07/15
    ジャーナル フリー
    In the present study, we investigated whether a robot is able to deceive a human by producing a behavior against his/her prediction. A feeling of being deceived by a robot would be a strong indicator that the human treated the robot as an intentional entity. We conducted a psychological experiment in which a subject played Darumasan ga Koronda, a Japanese children's game, with a robot. The main strategy to deceive a subject is to make him/her believe that the robot is unintelligent and unable to chant and move quickly. The experimental result indicated that unexpected change in robot's behavior gave rise to an impression of being deceived.
  • 竹中 透, 松本 隆志, 吉池 孝英
    2011 年 29 巻 5 号 p. 455-462
    発行日: 2011年
    公開日: 2011/07/15
    ジャーナル フリー
    In this paper, we propose a method to generate various walking gait patterns for biped robots in real-time using an approximated dynamics model with two point masses representing motion of the feet and a linear inverted pendulum representing the horizontal motion of the torso. The motion of the inverted pendulum is calculated from the trajectories of the feet and the desired ZMP. Supposing these trajectories for the current gait and the next cyclic gait are given, the initial state of the inverted pendulum of the cyclic gait is calculated, and the desired ZMP trajectory of the current gait is modified so that the current gait converges to the cyclic gait using the concept called “the divergent component of the motion” as a relaxed boundary condition. Using these techniques, real-time gait generation is achieved which requires a comparatively small amount of modification of the desired ZMP trajectory while guaranteeing the continuity of the gait generation.
  • 野川 晃佑, 小嶋 勝, 中島 正博, 本間 道夫, 福田 敏男
    2011 年 29 巻 5 号 p. 463-469
    発行日: 2011年
    公開日: 2011/07/15
    ジャーナル フリー
    Micro/nano robots are actively studied to realize novel techniques, such as micro/nano surgery inside the body. Recently, microorganisms, especially flagellated bacteria, have been used as the driving forces for the microobjects. To achieve the precise control of bacteria-driven microobjects, it is needed to establish the method to control the bacterial driving force. In many cases, the bacterial movements are regulated by environment. Therefore, the local environmental control technique is desired to control the bacterial driving force. We have constructed the local environmental control system with nano/micro dual pipettes, and achieved the manipulation of the transient-state of Na+-driven flagellar motor rotation by switching the local spout between Na+-containing and -free solutions. In this paper, we upgrade our nano/micro dual pipettes system to achieve the manipulation of the flagellar rotational speed in the steady-state. We demonstrate the steady-state rotational speed control of Na+-driven flagellar motor by simultaneous local spouts of Na+-containing and -free solutions with changing the spouting velocities independently.
  • 田崎 豪, 十倉 征司, 園浦 隆史, 尾崎 文夫, 松日楽 信人
    2011 年 29 巻 5 号 p. 470-479
    発行日: 2011年
    公開日: 2011/07/15
    ジャーナル フリー
    Locating all obstacles around a moving robot and classifying them as static obstacles or not by a sensor are essential for the robot's smooth movement and avoid problems in calibrating many sensors. However, there are few works on locating and classifying all obstacles around a robot while it is moving by only one omnidirectional camera. In order to locate obstacles, we regard floor boundary points where robots can measure the distance and direction from the robot by one omnidirectional camera as obstacles. Tracking them, we can classify obstacles by comparing the movement of each tracked point with odometry data. Moreover, our method changes a threshold to detect the points based on the result of comparing in order to enhance classification. The classification ratio of our method is 85.0%, which is four times higher than that of a method without changing a parameter to detect the points and that of a previous method.
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