We propose an object tracking system which recognizes everyday objects and estimates their positions by using distributed sensors in a room and mobile robots. The placement of objects is frequently changed according to human activities. Although a passive RFID tag is attached to each object for the object's recognition, the placement is often not uniquely determined due to the deficiency of measured data. We have already proposed a method for estimating the placement of objects by using the moving trajectories of objects. This estimation result is expressed as the probability distribution of the object placement. However intersections of trajectories cause the decrease of the estimation accuracy. So we propose a new method based on Bayesian inference to improve the estimation accuracy by using the size and the shape of an object measured by laser range finder. Then a mobile robot settles the placement with small workload by using the mounted sensor. The system successfully recognized and localized 10 objects in the experiment.

This paper describes an object tracking system for a robot working in an everyday environment, which tracks and recognizes everyday objects. Passive RFID (Radio Frequency IDentification) tags are attached to the objects for object recognition. The system consists of static sensors embedded in the environment and mobile sensors mounted on mobile robots. By utilizing the different characteristics and advantages of these sensors, the system achieves good performance in an everyday environment. Although the tag ID and the position of an object carried by a person is not measurable by static sensors or by mobile sensors, the system can estimate them by using an SIR (Sequential importance resampling) particle filter that integrates the data obtained by the static sensors and the mobile sensors. In the experiment, the system successfully tracked 20 objects, some of which were held by a person.

This paper describes a tactile sensing to estimate the position and orientation of a joint-axis of a linked object. This tactile sensing is useful when a multi-jointed multi-fingered robotic hand manipulates a tool which has a joint. This estimation requires sensing of the location of a contact point and the direction of an edge of the tool as contact information measured by a robotic fingertip. A conventional hard fingertip with a force sensor can measure only the location of a contact point. In contrast, we have already developed a robotic fingertip with a force sensor and a soft skin, and it can measure not only the location of a contact point but also the direction of an edge of an object. The estimation of a joint-axis of a linked object is demonstrated by using the soft fingertip.

We propose an object tracking system for a service robot working in an everyday environment. The system is composed of an intelligent cabinet, a floor sensing system and a data management system. The position of an object can be classified into three areas: 1) in/on furniture, 2) on the floor, 3) held by a human or a robot. Being equipped with a RFID reader and loadcells, the intelligent cabinet measures the position of an object in/on itself. The floor sensing system which uses a laser range finder, measures the position of an object on the floor and the position of a human walking in a room. The data management system integrates the position data of the intelligent cabinets and the floor sensing system, and it performs position measurement of an object carried by a human. The data management system provides robots with position information to support robot activities.

This paper proposes a new method of measuring position of daily commodities placed on a floor. Picking up an object on a floor will be a typical task for a robot working in our daily life environment. However, it is difficult for a robotic vision to find a small daily life object left on a large floor. The floor surface may have various texture and shadow, while other furniture may obstruct the vision. Various objects may also exist on the floor. Moreover, the surface of the object has various optical characteristics: color, metallic reflection, transparent, black etc. Our method uses a laser range finder (LRF) together with a mirror installed on the wall very close to floor. The LRF scans the laser beam horizontally just above the floor and measure the distance to the object. Some beams are reflected by the mirror and measure the distance of the object from virtually different origin. Even if the LRF fails two measurements, the method calculates the position of the object by utilizing information that the two measurements are unavailable. Thus, the method achieves two major advantages: 1) robust against occlusion and 2) applicable to variety of daily life commodities. In the experiment, success rate of observation of our method achieves 100% for any daily commodity, while that of the existing method for a cell-phone is 69.4%.

This paper proposes a segmentation method of human manipulation task based on measurement of contact force imposed by a human hand on a grasped object. We define an index measure for segmenting a human manipulation task into primitives. The indices are calculated from the set of the contact forces measured at all the contact points during a manipulation task. Then, we apply the EM algorithm to the set of the indices in order to segment the manipulation task into primitives. These primitives are mapped onto the robotic hand to impose appropriate contact forces on a grasped object. In the experiments, manipulation tasks performed in daily human life have been successfully segmented.

The present paper describes a decision method for the placement of tactile elements for manipulation task recognition. Based on the mutual information of the manipulation tasks and tactile information, an effective placement of tactile elements on a sensing glove is determined. Although the effective placement consists of a small number of tactile elements, it has a recognition performance that is as high as that of a placement consisting of many tactile elements. The effective placement of tactile elements decided by the proposed method has been evaluated through experiments involving the recognition of grasp type from grasp taxonomy defined by Kamakura [1].

This paper presents a sensor network system consisting of distributed cameras and laser range finders for multiple objects tracking. Sensory information from cameras is processed by the level set method in real time and integrated with range data obtained by laser range finders in a probabilistic manner using novel SIR/MCMC combined particle filters. Though the conventional SIR particle filter is a popular technique for object tracking, it has been pointed out that the conventional particle filter has some disadvantages in practical applications such as its low tracking performance for multiple targets due to the degeneracy problem. In this paper, the new combined particle filters consisting of a low-resolution MCMC particle filter and a high-resolution SIR particle filter is proposed. Simultaneous tracking experiments for multiple moving targets are successfully carried out and it is verified that the combined particle filters has higher performance than the conventional particle filters in terms of the number of particles, the processing speed, and the tracking performance for multiple targets.

We propose a new method of tactile sensing for a fingertip of robotic hand based on the analysis of the mechanical constraints imposed to the contact between the fingertip and the contacting object. Using a simple fingertip with soft surface cover and a six-axis force/torque sensor, a new function is developed to measure direction of local geometry of a class of objects when the fingertip contacts with them. The developed function is useful not only to plan strategies for stable grasp and dexterous manipulation but also to recognize a geometrical shape of an object. Two necessary components, a six-axis force/torque sensor and a soft skin, are commercially available. They neither restrict design of a robotic fingertip nor badly affect essential functions of stable grasping and manipulation

We propose a new method of tactile sensing for a fingertip of robotic hand. Using a simple fingertip with soft surface cover and a six-axis force/torque sensor, developed is a new function of measuring direction of edge when the fingertip contacts with an edge of the object. The developed function is useful not only to plan strategies for stable grasp and dexterous manipulation but also to recognize a geometrical shape of an object. Two necessary components, a six-axis force/torque sensor and the soft skin, are commercially available. They will neither restrict design of a robotic fingertip nor badly affect essential functions of stable grasping and manipulation.

A new approach to telerobotic manipulation is proposed. Using swept volume of a slave manipulator tele-operated initially by a human operator, on-line transition to autonomous tele-operation from master-slave manipulation is achieved without any geometric model of the object in an environment. This feature enables wider application in an unstructured environment.

This paper describes a novel fingertip equipped with a soft elastic cover and a hard nail. The soft elastic cover of the fingertip enables changing friction coefficient at the contact with an object. It also enables detecting change of contact type with an object while manipulating it: the fingertip contact with a flat surface and with a convex sharp edge. The solid nail enables detecting a very small step of sub-millimeter on a smooth surface.

This paper describes a sensing function of a multi-jointed multi-fingered hand with soft fingertips. We propose a new method to detect change of contact types with an object while manipulating it: the fingertip contact with a flat surface and with a convex sharp edge. Proposed method can apply to the manipulation with rolling contact. Experimental results are demonstrated.