Robot arms are being increasingly used in various fields with special attention given to unmanned systems. In this research, we developed a high payload dual-arm robot, in which the forearm module is replaceable to meet the assigned task, such as object handling or lifting humans in a rescue operation. With each forearm module specialized for an assigned task (e.g. safety for rescue and redundant joints for object handling task), the robot can conduct various tasks more effectively than could be done previously. In this paper, the design of the high payload dual-arm robot with replaceable forearm function is described in detail. Two forearms are developed here. Each of forearm has quite a different goal. One of the forearms is specialized for human rescue in human familiar flat aspect and compliance parts. Other is for general heavy objects, more than 30 kg, handling with high degree of freedom more than 7.

In this article art performing applications of industrial dual-arm robots are introduced. It was real collaboration among robot researchers and artist. Artist designed the performance to use dual-arm robot. Robot researchers collaborated with artist by providing robotic constraints and configuring robot motion. Two art performances were configured with two industrial dual-arm robots. In both performance robots carry objects to be used as moving screens. Both performances rely on the high power and high precision of robots. In addition human-like appearance make those performances be familiar to public

This paper proposes a design methodology of self-reconfigurable kinematics and control engine for modular and reconfigurable robots. A modular manipulator has been proposed to meet the requirement of task adaptation in versatile needs for service and industrial robot area and the function of self-reconfiguration is required to extend the application of modular robots. Kinematic and dynamic contexts are extracted from the module and assembly information and related codes are automatically generated including controller. Thus a user can easily build and use a modular robot without professional knowledge. Simulation results are presented to verify the validity of the proposed method.

Automation and robotization has been required in construction for several decades and construction industry has become one of the important research areas in the field of service robotics. Especially in the steel construction, automatic recognition of structural steel members in the stockyard is emphasized. However, since the pose of steel frame in the stockyard is site dependent and also the stockyard is usually in the outdoor environment, it is difficult to determine the pose automatically. This paper adopts the recognition method based on the augmented reality to cope with this problem. Particularly focusing on the light condition of the outdoor environment, we formulated the optimization problem with the constraint and suggested the methodology to evaluate the optimal camera arrangement. From simulation results, sub-optimal solution for the position of the camera can be obtained.

This paper presents a goal-directed reactive obstacle avoidance method based on lane method. The reactive collision avoidance is necessarily required for a robot to navigate autonomously in dynamic environments. Many methods are suggested to implement this concept and one of them is the lane method. The lane method divides the environment into lanes and then chooses the best lane to follow. The proposed method does not use the discrete lane but chooses a line closest to the original target line without collision when an obstacle is detected, thus it has a merit in the aspect of running time and it is more proper for narrow corridor environment. If an obstacle disturbs the movement of a robot by blocking a target path, a robot generates a temporary target line, which is parallel to an original target line and tangential to an obstacle circle, to avoid a collision with an obstacle and changes to and follows that line until an obstacle is removed. After an obstacle is clear, a robot returns to an original target line and proceeds to the goal point. Obstacle is recognized by laser range finder sensor and represented by a circle. Our method has been implemented and tested in a corridor environment and experimental results show that our method can work reliably.

In this paper, new filtering method for sensor registration is provided to estimate and correct error of registration parameters in multiple sensor environments. Sensor registration is based on filtering method to estimate registration parameters in multiple sensor environments. Accuracy of sensor registration can increase performance of data fusion method selected. Due to various error sources, the sensor registration has registration errors recognized as multiple objects even though multiple sensors are tracking one object. In order to estimate the error parameter, new nonlinear information filtering method is developed using minimum mean square error estimation. Instead of linearization of nonlinear function like an extended Kalman filter, information estimation through unscented prediction is used. The proposed method enables to reduce estimation error without a computation of the Jacobian matrix in case that measurement dimension is large. A computer simulation is carried out to evaluate the proposed filtering method with an extended Kalman filter.