Hidenori Shirasawa

Development of multi-fingered universal robot hand with torque limiter mechanism

A multi-fingered universal robot hand has been developed in order to construct the platform of humanoid hand study. We also have developed a small and five-fingered robot hand. The robot hand is designed to protect the small driving system from a large external force. This protection mechanism is small enough to be installed in the joint driving mechanism and adaptable enough to deal with various load. This paper describes basic and unique specifications of the robot hand, and the effectiveness is confirmed by fundamental experiments.

Shape classification based on tactile information by Universal Robot Hand

We propose a shape classification in continuous rotational manipulation by a multi-fingered robot hand. Our robot hand has five fingers equipped with tactile sensors. Each tactile sensor can measure a pressure distribution once every 10 ms while the robot hand rotates an object continuously. Our proposal classification method consists of the following processes: A kurtosis is calculated from each pressure distribution, and it quantifies the shape of the current contact surface. By rotating an object and measuring a time-series pressure distribution, the hand obtains a time-series kurtosis. An evaluated value is calculated from the time-series kurtosis and reference patterns using continuous dynamic programming (CDP) matching scheme. The contact shape is classified whether the evaluated value is lower than a given threshold. Experiments using three symmetrical objects and an asymmetrical object demonstrate the effectiveness of our proposal shape classification.

Fingertip force and position control using force sensor and tactile sensor for Universal Robot Hand II

Various humanoid robots and multi-fingered robot hands are used in research and development. As these robot hands grasp and manipulate an object, the control phase is divided into an “approach phase” and a “manipulation phase.” In the approach phase, a position control method is necessary to control the posture of the robot hand. In the manipulation phase, a force control method is necessary to control the fingertip force of the robot hand. However, it is difficult to control both the force and position of these hands at the same time. In this paper, we propose a grasping force control method based on position control for manipulation. In this proposed method, the finger position is controlled in the direction of the force vector. With this control method, any external force is cancelled and the initial force is kept constant, or the setting force is applied to an object.

Shape classification in rotation manipulation by universal robot hand

We propose a method for shape classification in rotation manipulation by a multi-fingered robotic hand. The robotic hand has five fingers equipped with tactile sensors. Each tactile sensor can measure the pressure distribution once every 10(ms). Our proposed classification method consists of the following processes: A kurtosis is calculated from the pressure distribution, and it quantifies shape of the current contact surface. By rotating an object and measuring a time-series pressure distribution, the hand obtains a kurtosis pattern. Finally, a degree of similarity is calculated between the kurtosis pattern and a reference pattern through a dynamic programming (DP) matching scheme. This classifies the contact surface shape using thresholds. Laboratory experiments confirm that this method can classify three objects with a high degree of accuracy.

High-Speed Tactile Sensing for Array-Type Tactile Sensor and Object Manipulation Based on Tactile Information

We have developed a universal robot hand with tactile and other sensors. An array-type tactile sensor is crucial for dexterous manipulation of objects using a robotic hand, since this sensor can measure the pressure distribution on finger pads. The sensor has a very high resolution, and the shape of a grasped object can be classified by using this sensor. The more the number of measurement points provided, the higher the accuracy of the classification, but with a corresponding lengthening of the measurement cycle. In this paper, the problem of slow response time is resolved by using software for an array-type tactile sensor with high resolution that emulates the human sensor system. The validity of the proposed method is demonstrated through experiments.

Slip detection with multi-axis force/torque sensor in universal robot hand

A humanoid robot hand receives much attention in various fields. We have developed the universal robot hand with the multi-axis force/torque sensors. In order to manipulate an object by the robot hand without dropping the object, it is important to detect a slip between the object and the robot finger. Therefore, we propose a slip detection method with the multi-axis force/torque sensor and an anti-slip control method based on the slip detection. The effectiveness of the proposed slip detection and the anti-slip control is verified through some experiments with the universal robot hand.

Outer shape classification in rotation manipulation by Universal Robot Hand

We propose a method for shape classification in continuous rotation manipulation by a multi-fingered robot hand. Our robot hand has five fingers equipped with tactile sensors. Each tactile sensor can measure a pressure distribution once every 10(ms) while the robot hand rotates the object continuously. Our proposed classification method consists of the following processes: A kurtosis is calculated from each pressure distribution, and it quantifies shape of the current contact surface. By rotating an object and measuring a time-series pressure distribution, the hand obtains a time-series kurtosis. Finally, a evaluated value is calculated between the time-series kurtosis and reference patterns through a continuous dynamic programming (CDP) matching scheme. The contact shape is classified if the evaluated value is lower than a threshold. We show the effectiveness of our method through experiments.

Shape Classification in Rotating Manipulation by Universal Robot Hand-Local Shape Classification of Object using DP Matching

We propose a method of shape classification in rotating manipulation by a multi-fingered robot hand. A multi-fingered robot hand understands position and posture of a object and can realize suitable manipulations by classifying a shape of contact surface in rotating manipulation. Our method uses pressure distributed sensors equipped in fingers and consists of the following three processes. First, periodic pressure distributions are measured by a pressure distributed sensor in rotating manipulation. A kurtosis is calculated from each pressure distribution and can quantify a shape of contact surface in the moment. Secondly, a kurtosis pattern is cut out from a periodic kurtosis. Finally, a degree of similarity calculates between a kurtosis pattern and a reference pattern by DP matching method and classifies the shape of contact surface using thresholds. We confirm that our method classifies three objects with a high degree of accuracy and can keep the classification rate even in changing rotation velocity through laboratory experiments.