Self-localization is important for mobile robots in order to move accurately, and many works use an omnidirectional camera for self-localization. However, it is difficult to realize fast and accurate self-localization by using only one omnidirectional camera without any calibration. For its realization, we use “tracked scale and rotation invariant feature points” that are regarded as landmarks. These landmarks can be tracked and do not change for a “long” time. In a landmark selection phase, robots detect the feature points by using both a fast tracking method and a slow “Speed Up Robust Features (SURF)” method. After detection, robots select landmarks from among detected feature points by using Support Vector Machine (SVM) trained by feature vectors based on observation positions. In a self-localization phase, robots detect landmarks while switching detection methods dynamically based on a tracking error criterion that is calculated easily even in the uncalibrated omnidirectional image. We performed experiments in an approximately 10 [m] × 10 [m] mock supermarket by using a navigation robot ApriTau™ that had an omnidirectional camera on its top. The results showed that ApriTau™ could localize 2.9 times faster and 4.2 times more accurately by using the developed method than by using only the SURF method. The results also showed that ApriTau™ could arrive at a goal within a 3 [cm] error from various initial positions at the mock supermarket.

Locating all obstacles around a moving robot and classifying them as stable obstacles or not by a sensor such as an omnidirectional camera are essential for the robot's smooth movement and avoiding problems in calibrating many cameras. 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 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.

This paper describes an efficient indoor map building method that involves a robot collaborating with a human. A robot acquires the shape of a required part of an indoor environment, searches for a suitable picture from the scene of the destination, and registers it as a picture landmark at the same time as the robot follows the human. Once a picture landmark is registered on a map, a robot finds it and can self-localize from the next navigation. The validity of the proposed method is demonstrated by experiments carried out on an actual mobile robot ldquoApriAttendatraderdquo, developed by Toshiba.

We have developed the person following robot "ApriAttenda(TM)". This robot can accompany a person using vision based target detection and avoid obstacles with ultrasonic sensors. The robot first identifies an individual with its image processing system by detecting a person's region and recognizing the registered color and texture of his/her clothes. Our newly developed algorithm allows the robot to extract a particular individual from a cluttered background, and to find and reconnect with the person if it loses visual contact. Tracking people with vision was realized by systematizing visual and motion control with a robust algorithm that utilizes various characteristics of the image data. ApriAttenda(TM) has been exhibited at Aichi EXPO 2005, and its robust functions and smooth person following capability were successfully demonstrated.

We have developed the person following robot "ApriAttendatrade". This robot can accompany a person using vision based target detection and avoid obstacles with ultrasonic sensors while following the person. The robot first identifies an individual with its image processing system by detecting a person's region and recognizing the registered color and texture of his/her clothes. Usually, the person following robot has to detect and recognize the specified person and calculate his/her position in a complicated real-life environment of fixed objects and moving people. Our newly developed algorithm allows the robot to extract a particular individual from a cluttered background, and to find and reconnect with the person if it loses visual contact. Tracking people with stereo vision was realized by systematizing visual and motion control with a robust algorithm that utilizes various characteristics of the image data. The developed algorithm uses several analyses to extract information on the distance to each feature point, speed of target, color and texture of clothes for a stable tracking in many situations including changes of view due to self motion, shifts in lighting and objects similar to the target. The person following robot ApriAttendatrade has been exhibited at Aichi EXPO 2005, and its robust functions and smooth person following capability were successfully demonstrated

We have proposed a concept of a robotic information home appliance corresponding to one category of home robots, and developed ApriAlpha, a concept model of the robotic information home appliance. ApriAlpha is a wheel locomotion type human friendly home robot which controls advanced home appliances, standing between their users and them as a voice controlled information terminal, and offers security and information services to users. We have integrated various robot technologies such as voice communication, image recognition, planning and motion control on ApriAlpha, and we have introduced a framework of the distributed object technology based open robot controller architecture (ORCA), which we are currently developing in view of its easy extension and efficient development. This paper describes the proposed robotic information home appliance and its concept model, ApriAlpha. The functions of the developed robot are confirmed by performing several demonstrations, and the merit of applying the framework of ORCA to the home robot controller is also confirmed through its development.

The Mechanical Engineering Laboratory (MEL) is developing technologies for multi-robot collaboration in remote environments with the Toshiba Mechanical Systems Laboratory (TMSL). Human operators' delayed visual perception due to the communication delay over the network seriously affects the collaboration performance in the multi-operator-multi-robot tele-manipulation. We have built an experimental tele-manipulation test bed connecting the MEL and the TMSL to study the time-delayed remote tele-collaboration between two places. In the test bed, one operator controls her master robot nearby two slave robots in the work site and another from a distance. A local online graphics simulator without time delay is developed to cope with the communication delay and several multi-robot coordinated control strategies are devised to avoid the collision. To verify the validity of this simulator assisted approach, we demonstrated a maintenance work on a plant mock-up in the TMSL by two different slave robots and two distant operators in the MEL and TMSL respectively through an ISDN.

We developed a robot system which can play one-on-one volleyball games with a human player. It can not only hit a flying ball back to the player correctly but also have various autonomous functionalities to interact with humans necessary for playing games, i.e., it can pick up a ball of a requested color by recognizing voice instructions, can locate the players by recognizing their faces, and can shake hands. This robot performed the entire game sequence over 100 times at a company-sponsored exhibition, where the ball rally continued over 25 times with an arbitrary player. In this paper, we mainly describe the real-time visual feedback system of the robot which is essential for playing ball games.

This paper reports comparative experiments with a new model-based adaptive force control algorithm for robot arms. This controller provides simultaneous position and force trajectory tracking of a robot arm whose tool tip is in point contact with a smooth rigid surface. The algorithm is provably stable with respect to the commonly accepted rigid-body nonlinear dynamical model for robot arms. Comparative experiments show the new adaptive model-based controller to provide performance superior to that of both nonmodel-based controllers and nonadaptive controllers over a wide range of operating conditions.

This paper presents a force controlled robot system for grinding, chamfering and polishing in machining plants. To facilitate easy operations, a task-oriented robot language and a force control method for following the edges of workpieces have been developed. In the task-oriented robot language, a task is declared first by a task name, a tool code and a tool center point. The tool center point is a position in contact with a workpiece. These indexes and the force control motion command determine the parameters for the force control of the robot. The workpieces for grinding, chamfering, and polishing almost always differ their setting positions, their sizes and/or their shapes, even when the workpieces are the same parts in a machine. To cope with the workpiece difference, a tool moment control method, a skip function, and a round function have been developed. Experiments on chamfering of the edges of a part in an injection molding machine are reported.

This paper reports comparative experiments with a provably correct model-based adaptive robot control algorithm for simultaneous position and force trajectory tracking of a robot arm whose gripper is in point contact with a smooth surface. The experiments show the new adaptive model-based offers performance superior to that of its non-model-based counterpart over a wide variety of operating conditions.

The dynamic parameters of a rigid body model for a two-degree-of-freedom SCARA-type robot are estimated through experiments to evaluate the validity of the model. The input torque and the torque calculated using the estimated parameters are compared in order to evaluate the validity of the model. The difference between the two torques is examined, and the contributions of the terms in the model are calculated to observe which term has the greatest effect on the difference. The model can describe the robot dynamics within about 10% for velocities higher than 1 rad/s for the shoulder joint and for the complete velocity range for the elbow joint. Since Coulomb dynamic friction is dominant in the low- velocity region, it seems that the friction in the model is not sufficient to describe the dynamics there.