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This paper introduces a practical implementation of the normalized dynamic energy stability margin (NDESM) which is simplified for wheeled mobile robots. Several types of sensors are generally required to measure the stability margin of a robot. This makes the practical implementation of a stability measurement system complicated. The simplified stability margin can be measured with only a few sensors, and it describes both static and dynamic effects on stability. The performance of the simplified stability margin is demonstrated through dynamic simulations. The experimental results and an implementation of the stability margin measurement system on a real robot are also presented.

The mobile robots to rescue a life in a disaster area and to explore planets demand high mobility as well as recognition of the environment. To avoid unknown obstacles exactly in unknown environment, accurate sensing is required. This paper proposes a sensor fusion to recognize unknown obstacles accurately by using low-cost sensors. Ultrasonic sensors and infrared sensors are used in this paper to avoid obstacles. If only one of these sensors is used alone, it is not useful fer the mobile robots to complete their tasks in the real world since the surrounding environment in the real world is complex and composed of many kinds of materials. So infrared sensor may not recognize transparent or reflective obstacles and ultrasonic sensor may not recognize narrow obstacles, far example, columns of small diameter. Therefore, I selected six ultrasonic sensors and five infrared sensors to detect obstacles. Then, I fused ultrasonic sensors with infrared sensors in order that both advantages and disadvantages of each sensor are utilized together. In fusing sensors, fuzzy algorithm is used to cope with the uncertainties of each sensor. TAMRY which is terrain-adaptive mobile robot is used as the mobile robot for experiments.

This paper introduces a smart mobile walker which was developed for elderly and disabled. It supports three features, which are walking aid, sit-to-stand aid, and electric scooter. Implementation of each function is briefly described. The overall performance of the smart mobile walker is evaluated by experts in rehabilitation hospitals and continuously being improved.

This paper introduces a smart mobile walker which support walking aid, sit-to-stand aid, and electric scooter functions for elderly and disabled. It is designed for indoor use like rehabilitation hospital. It helps users exercise sit-to-stand motion, make gaits, and move place to place inside a facility. To support these functions, it changes its configuration. A smart device provides a GUI to communication with users. Detailed implementation of each function is described.

This paper introduces a robotic walking and sit-to-stand support system, Smart Mobile Walker(SMW), and presents kinematic analysis of the system. For supporting person's sit-to-stand movement, the motion of SMW is modeled by the analysis of kinematics. The results of analysis are used for simulations and its feasibility is investigated by simulations. The feasibility shows our analysis of kinematics can be applied as a base of making optimal motion trajectory for helping sit-to-stand.

We have developed a dead reckoning sensor system and a tracking algorithm for mobile robots to estimate the path when a mobile robot explores an unknown, enclosed region where GPS access or landmarks are unavailable. A dead reckoning sensor system consists of a low-cost MEMS IMU and a navigation sensor (used in laser mice), which provide complementary functions. The IMU has benefits such as compact size, a self-contained system, and an extremely low failure rate but has a bias drift problem, which can accumulate substantial error over time. A navigation sensor measures the motion of a mobile robot directly without the slip error in the case of a wheel-type odometer, but it often fails to read a surface. A tracking algorithm consists of an extended Kalman filter (EKF) to fuse data from the IMU and the navigation sensor and a least-squares method to estimate acceleration bias in the EKF. We obtained experimental data by driving a radio-controlled car equipped with the sensor system in a 3D pipeline and compared the path estimated by the tracking algorithm with the path of the pipeline. The tracking algorithm combined data from the IMU and the navigation sensor and correctly estimated the path of the radio-controlled car. Our study can be applied to estimate position or path of mobile robots without external aids such as GPS, landmarks, and beacons.

Various static and dynamic stability criteria have been defined in many researches. To estimate these stability margins, many sensors are required. Therefore, implementation on a real vehicle is an important factor for stability margins. In this paper, a modified dynamic energy stability margin measured with only a few sensors is suggested to facilitate implementation. The performance of the modified stability margin is demonstrated through dynamic simulations. The experiment results and an implementation of the stability margin estimation system on a real vehicle are presented.