Safety is important to a robot that is used in a way that can impact on the wellbeing of people. However, understanding and managing the safety information of a robot is not a trivial task. The quantity of this information is large, and it is usually stored in text-based formats. These factors mean that maintaining safety information when it changes is error prone, and understanding it requires manually searching through lengthy reports. In this paper, we demonstrate that a robot's safety information can be stored in a model-based format. We use the SafeML modelling language to model the results of a safety analysis performed on a semi-autonomous wheelchair. We then illustrate and discuss the benefits of storing and managing safety information using a model, both to maintainability of the information and to understanding the safety of the robot.

This paper presents a new design method for force control, which aims to control the interaction force between a robot and a human by satisfying certain safety requirements. In this method, an optimization-based control algorithm, called Iterative Feedback Tuning (IFT), is used to employ safety requirements as constraints of an optimization problem, which is then solved using sequential quadratic programming (SQP). Therefore, this control method is applicable for safety-critical systems such as personal service robots. These robots are developed to provide assistance to patients or disabled people in their daily life by performing human-robot contact tasks such as wiping the face with a towel, scratching, etc. In addition, in IFT, a Newton search direction to update the controller parameters at each iteration is obtained on the basis of the closed-loop experimental data. Hence, IFT does not require explicit modeling of environment, in particular, human dynamics, otherwise in model-based approach it will be hard task to obtain a useful model. In the simulation and experiment, the effectiveness of the proposed method is examined by applying it to 1-DOF contact system.

A direct teaching system emphasizing safety and operability for SP02, an upper-body humanoid that cooperates with a human in a cell-production workspace has been described. The system has a direct teaching device, which enables an operator to teach a multi-D.O.F manipulator at comfortable posture despite large changes in the pose of an end-effector. The double-checked safety-related part(SRP/CS) introducing a safety module satisfies the safety performance defined in the international safety standard. To make the teaching easier to perform, impedance parameters in a variable impedance control are set through several direct teaching experiments. Through a simple direct teaching experiment, the safety performance of the SRP/CS and the teaching performance of the direct teaching system was successfully confirmed.

Although the need for humans and robots to work together in close proximity is increasing, this is currently not allowed with conventional industrial robots for reasons of safety. Next generation robots that can work safely in close proximity with humans must not only be highly functional, but must also be highly reliable with built in safety features. With this aim in mind, we have developed a safety module that integrates safety functions required for robots to work side by side with humans. The safety module is designed to be compliant with international safety standards and Japanese law. Redundant sensory signal processing by an external hardware module and plug-in software installed in the robot controller ensure high-reliability and flexibility. This paper describes the concept and design of the safety module and shows some evaluation results of its safety functions.

It is believed that from this point forward, there will be a need for industrial robots that work alongside and cooperatively with humans. However, the current development of existing robotics technology is inadequate to ensure the safety of such new industrial robots. Our research proposes a safety-planning technology called Coexistence Hazard Avoidance Technology, designed for use with a low-powered human-collaborative industrial robot. We load this technology into a risk-management simulator and verify that by using it, a dynamic planning method for the safe operation of robots can be reasonably undertaken in terms of both theory and calculation.

We describe a two-dimensional (2-D) multi-degree-of-freedom (DOF) robot manipulator made from a soft polymer gel actuator, which we call an artificial muscle. The independent voltage control of three segments of patterned electrodes on the artificial muscle enables independent partial bending motions of each segment and realizes a multi-DOF control of the manipulator. We introduce the kinematic modeling of these multi-DOF bending motions and propose visual feedback control of the artificial muscle. For high-speed visual feedback, we have developed a visual sensing system with a 1ms sampling rate. Finally, we show the experimental results of the visual sensing and kinematic modeling of the bending motions of the manipulator.

In this research, we have developed a variable baseline stereo tracking vision system using a 1ms high-speed vision system and a high-speed linear slider. The vision system tracks a moving object, captures a stereo image pair and estimates its distance in only 1ms. It also calculates stereo matching and estimates its 3D shape every 2ms. The high speed linear slider moves stereo cameras horizontally and independently so the baseline length can be changed. We also propose a baseline control method which is adaptive to the distance to the object. When the object appears large in the image, the system reconstructs its 3D shape, and when the object appears small in the image, the system only estimates the 3D position of its center of gravity. The experimental results show that the proposed baseline control method improves the depth accuracy and the system can track and estimate the 3D shapes of fast moving objects.

When considering real-world applications of robot control with visual servoing, both 3D information and a high feedback rate are required We have developed a 3D target tracking system with a 1ms feedback rate using two high-speed vision systems called column parallel vision (CPV) systems. To obtain 3D information, such as position, orientation, and shape parameters of the target object, a feature-based algorithm has been introduced using moment feature values extracted from vision systems for a spheroidal object model. Also, we propose a new 3D self windowing method to extract the target in 3D space, which is an extension of the conventional self windowing method in 2D images.

We have developed a 1 ms vision system, to provide a much faster frame rate than that of the conventional systems. Our 1 ms vision system has a 128/spl times/128 PD array and an all parallel processor array connected to each other in a column parallel architecture, so that the bottleneck of an image transfer is solved. 1 ms visual feedback has been realized in this system, in which the image feature value is extracted in 1 ms cycle-time for visual servoing. We have also developed a high speed active vision system (AVS)-II. Finally, we provide a detail discussion on our 1 ms vision system and its performance through some experiments.

In most conventional manipulation systems, changes in the environment cannot be observed in real time because the vision sensor is too slow. As a result the system is powerless under dynamic changes or sudden accidents. To solve this problem we have developed a grasping system using high-speed visual and force feedback. This is a multi-fingered hand-arm with a hierarchical parallel processing system and a high-speed vision system called SPE-256. The most important feature of the system is the ability to process sensory feedback at high speed, that is, in about 1 ms. By using an algorithm with parallel sensory feedback in this system, grasping with high responsiveness and adaptivity to dynamic changes in the environment is realized.

We introduce a visual impedance scheme for vision based control which realizes task-level dynamical robot control. This method is simply described as applying image features to the impedance equation so that integration of a visual servo and a conventional servo system can be naturally accomplished. With visual impedance, an adaptive motion is obtained for real robot tasks in dynamically changing or unknown environments based on the framework of impedance control. In such cases, very high rate visual feedback is necessary to control the robot dynamics but most conventional vision systems using CCD cameras can never satisfy this condition because their sampling rate is limited by the video signal. To solve this problem, we developed a general-purposed vision chip SPE-256 and the 1 ms visual feedback system which can achieve an adequate servo rate to control dynamics. The 1 ms visual feedback system for a robot control system is described. Experimental results with some real robot tasks are shown.

Most conventional visual feedback systems using CCD camera are restricted by video rates and therefore cannot be adapted to the changing environment sufficiently quickly. To solve this problem we developed a 1 ms visual feedback system using a general purpose massively parallel processing vision system in which photo-detectors and processing elements are directly connected. For high speed visual feedback fast image processing algorithms are also required. In particular, the difference of images between frames is very small in our system because of its high speed frame rate. Using this feature, we can realize several image processing techniques by simpler algorithms. In this paper we propose a simple algorithm for target tracking using the feature of high speed vision, and realize target tracking on the 1 ms visual feedback system.