We have been studying a remote-collaboration system called SCOPE (sight collaboration by projection effect) featuring image projecting and capturing capabilities as implemented in a maintenance robot. With the help of SCOPE and a remote support person, an on-site worker can perform maintenance very efficiently. We propose a mobile SCOPE that can significantly expand the area of activity of the conventional SCOPE. To enable an on-site worker to share the field of view of a remote support person, we developed a technique for aligning the optical axes of a camera and a projector. We present experimental data that demonstrates the validity of our optomechanical design. Finally, we show that the mobile SCOPE enables workers to continuously share their field of view, no matter where the mobile SCOPE moves

To create a more efficient environment in which to perform remote maintenance, the authors propose a novel collaboration concept, called robot-augmented communication (RAC). In RAC a maintenance robot and an on-site human worker are assisted by another skilled human operator at a remote support center. By emphasizing the intentions of the remote supporter through robot functions that cannot be imitated by humans, RAC can provide effective support for the on-site worker. In the present study, as the first manifestation of RAC, featuring the projection capability of a maintenance robot, a prototype remote-collaboration system named SCOPE (sight collaboration by projection effect) was developed. This system consists of a projector unit with a camera, a multiple control unit (MCU) for video/voice communications, and PCs connected via a network. It has three key functions: creating a shared field of view between a remote supporter and an on-site worker, keeping meta-information related to objects in real space, and projecting meta-information onto the objects. The effectiveness of these functions was evaluated by experiments on two fundamental tasks $"comprehending a target object" and "transmission of instruction details" nvolved in a typical maintenance operation. The experimental results show that each function is effective in shortening the time taken to perform the maintenance operation.

Teaching a sensor-enhanced robot under many task constraints is very difficult even for skilled operators, let alone novice operators. The problem is that the indexes for performing a robotic task, which the operators should simultaneously pay attention to, are not clear. To overcome this problem, we classify the task constraints into three indexes for robot, tool and sensor. We substitute the weighted indexes into the null-space method and generate robot motion for performing a task. We use this method in a tool-manipulation task such as welding and experimentally confirm the usefulness for reflecting the operator's teaching skill on the end-effector position and orientation using weights of indexes. Moreover we discuss the extraction of the operator's skills from teaching data as weights of these indexes.

A model of contact-state transitions is useful for motion planning, robot control, and sensing. Modeling the entire set of contact-state transitions every object, however, is very troublesome. To overcome the problem, we have developed a method for programming the model from human demonstration using a model of local contact-state transitions between a vertex pair (local model). Our method consists of three parts: (1) allocating the local model to vertex pairs where contact-state transitions will occur, (2) gathering them into groups according to an assembly sequence, and (3) unifying different groups resulting from different demonstrations of the same task. The allocation and grouping are done by simply showing a robotic system an assembly sequence through demonstration. The unification is done by comparing local models between different groups. We applied the method to two object-insertion tasks and did two kinds of experiments: (1) programming groups of local models from demonstration and (2) performing the demonstrated task by a robot using the programmed groups. The results showed the effectiveness in programming from demonstration and in the modeling problem.