This research is to develop a street-walking aid for the visually impaired, which detects front direction obstacles within 2 meters and inform the user with sound and vibration as well. This way it keeps the visually impaired from such obstacles. This aid also identifies 10 different colors quite correctly so that the visually impaired could apply it when he or she tries to match the color of his or her clothes, or he or she wants to know the color of things he or she would buy. This aid has additional function of identifying the brightness level around the user. It provides also various ergonomic design features such as optimized centroid design for minimal fatigue of hands even after long use, ideal position of back button for easy and convenient grip, easy and compact recharge, raised letters on all buttons, the usefulness of main body itself without attached white cane, non-slip hand grip, high-performance ultrasonic sensors, and wrist strap.

항해 중인 함정은 늘 충돌 가능성이 존재하지만 충돌회피를 위한 명확한 기동지침은 없고 함교 당직사관의 직관적 판단에 의존하 는 경향이 있다. 본 연구에서는 항해 중인 함정이 방위끌림이 없는 장애물을 조우하는 상황에서 함교 당직사관을 대상으로 언제 어떻게 충돌 을 회피하는지 설문조사를 실시하였다. 설문 결과를 활용하여 방위끌림이 없는 장애물 조우 상황, 주·야간 충돌 회피 방법을 분석하였다. 조함이 까다로운 지역은 평택, 목포 순이었고, 주로 협수로 내에서 발생하였다. 빈도는 4시간 항해 시 평균 1회 정도로 나타났으며, 1:1 조우 상황보다 다수 선박 조우가 많았다. 충돌침로 확인 시 전자해도보다 육안 확인 결과를 더 신뢰하였고, 충돌회피 고려 요소로 최단 접근거리, 최단 접근시간을 우선시하였다. 피항의무선과 침로유지선의 충돌회피 기동상 특별한 차이는 없었지만 주·야간 시 최단 접근거리의 차이는 존재 했다. 충돌회피 시대부분의 항해사들은 변침·변속을 함께 사용하는 것을 선호하며 타각 10~15°, 변속 ±5knots, 변침침로는 타함 함미 정방향에서 함미 가중치를 두었다. 이러한 결과들은 승조원들에게 부임 함정의 충돌 회피 기준을 제공하는데 도움이 될 것이며 나아가 AI, 빅데이터 기반의 무인함정 충돌회피 알고리즘 개발에도 적용될 것이다.

As the development of autonomous vehicles becomes realistic, many automobile manufacturers and components producers aim to develop ‘completely autonomous driving’. ADAS (Advanced Driver Assistance Systems) which has been applied in automobile recently, supports the driver in controlling lane maintenance, speed and direction in a single lane based on limited road environment. Although technologies of obstacles avoidance on the obstacle environment have been developed, they concentrates on simple obstacle avoidances, not considering the control of the actual vehicle in the real situation which makes drivers feel unsafe from the sudden change of the wheel and the speed of the vehicle. In order to develop the ‘completely autonomous driving’ automobile which perceives the surrounding environment by itself and operates, ability of the vehicle should be enhanced in a way human driver does. In this sense, this paper intends to establish a strategy with which autonomous vehicles behave human-friendly based on vehicle dynamics through the reinforcement learning that is based on Q-learning, a type of machine learning. The obstacle avoidance reinforcement learning proceeded in 5 simulations. The reward rule has been set in the experiment so that the car can learn by itself with recurring events, allowing the experiment to have the similar environment to the one when humans drive. Driving Simulator has been used to verify results of the reinforcement learning. The ultimate goal of this study is to enable autonomous vehicles avoid obstacles in a human-friendly way when obstacles appear in their sight, using controlling methods that have previously been learned in various conditions through the reinforcement learning.

The Expanded Guide Circle (EGC) method has been originally proposed as the guidance navigation method for improving the efficiency of the remote operation using the sensory information. The previous algorithm is, however, concerned only for the omni-directional mobile robot, so it needs to suggest a suitable one for a mobile robot with non-holonomic constraints. The ego-kinematic transform is a method to map points of R2 into the ego-kinematic space which implicitly represents non-holonomic constraints for admissible paths. Thus, robots with non-holonomic constraints in the ego-kinematic space can be considered as “free-flying object”. In this paper, we propose an effective obstacle avoidance method for mobile robots with non-holonomic constraints by applying EGC method in the ego-kinematic space using the ego-kinematic transformation. This proposed method shows that it works better for non-holonomic mobile robots such as differential-drive robot than the original one. The simulation results show its effectiveness of performance.

이 연구에서는 소뇌에 이상이 있는 소뇌성 운동실조증 환자들(n=9)을 대상으로 단일 장애물 보행과 연속적인 다중 장애물 보행을 수행하는 동안에 운동학적 특성과 전략을 분석함으로써 소뇌가 어떠한 역할을 하는지 규명하였다. 실험과제는 과제의 난이도 별로 단일 장애물 보행과 다중 장애물 보행 조건으로 설정하여 장애물을 넘는 동안 발의 높이, 장애물을 넘기 전 발의 이륙거리, 장애물을 넘은 후 착지거리, 장애물을 넘는 동안 발의 외전량, 보행 속도 등의 운동학적 변인을 측정하였다. 연구 결과, 소뇌 환자 집단은 정상인들에 비해 장애물을 통과하는 속도가 느리며, 단일 장애물 과제를 수행 할 때 보다 많은 정보처리를 요구되는 다중 장애물 과제의 수행에 어려움을 보여 주었다. 또한 장애물을 넘기 위한 행동 전략으로 다중 장애물뿐만 아니라 단일 장애물 과제 모두에서 발의 위치를 장애물에 가까이하고 발의 높이를 높게 하여 발이 장애물에 걸리지 않게 하는 전략을 선택하는 것 역시 운동계획과 예측 전략에 어려움이 있음을 시사한다. 이러한 결과는 시각적 정보 처리가 요구되는 동작 과제와 연속적인 다중 장애물 보행과 같은 복잡한 하지 제어의 계획과 실행에 소뇌가 중요한 역할을 담당한다는 사실을 보여준다.

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, we provide experimental results and verification for obstacle avoidance algorithm 'ELA(Emergency Level Around)', which is applicable to rescue robots. ELA is a low level intelligence-based obstacle avoidance algorithm, so can be used in fast mobile robots requiring high speed in operation with little computational load. Constructed system for experiments consist of laptop, sensors, peripheral devices and mobile robot platform VSTR(Variable Single-tracked Robot) to realize predetermined scenarios. Finally, experiment was conducted in indoor surroundings including miscellaneous things as well as dark environment to show fitness and robustness of ELA for rescue, and it is shown that VSTR navigates endowed area well with real-time obstacle avoidance based on ELA. Therefore, it is concluded that ELA can be a candidate algorithm to increase mobility of rescue robots in real situation.

Collision avoidance is a fundamental and important task of an autonomous mobile robot for safe navigation in real environments with high uncertainty. Obstacles are classified into static and dynamic obstacles. It is difficult to avoid dynamic obstacles because the positions of dynamic obstacles are likely to change at any time. This paper proposes a scheme for vision-based avoidance of dynamic obstacles. This approach extracts object candidates that can be considered moving objects based on the labeling algorithm using depth information. Then it detects moving objects among object candidates using motion vectors. In case the motion vectors are not extracted, it can still detect the moving objects stably through their color information. A robot avoids the dynamic obstacle using the dynamic window approach (DWA) with the object path estimated from the information of the detected obstacles. The DWA is a well known technique for reactive collision avoidance. This paper also proposes an algorithm which autonomously registers the obstacle color. Therefore, a robot can navigate more safely and efficiently with the proposed scheme.

We propose a novel real-time obstacle avoidance method for rescue robots. This method, named the ELA(Emergency Level Around), permits the detection of unknown obstacles and avoids collisions while simultaneously steering the mobile robot toward safe position. In the ELA, we consider two sensor modules, PSD(Position Sensitive Detector) infrared sensors taking charge of obstacle detection in short distance and LMS(Laser Measurement System) in long distance respectively. Hence if a robot recognizes an obstacle ahead by PSD infrared sensors first, and judges impossibility to overcome the obstacle based on driving mode decision process, the order of priority is transferred to LMS which collects data of radial distance centered on the robot to avoid the confronted obstacle. After gathering radial information, the ELA algorithm estimates emergency level around a robot and generates a polar histogram based on the emergency level to judge where the optimal free space is. Finally, steering angle is determined to guarantee rotation to randomly direction as well as robot width for safe avoidance. Simulation results from wandering in closed local area which includes various obstacles and different conditions demonstrate the power of the ELA.

This paper proposes a method of avoiding obstacles and tracking a moving object continuously and simultaneously by using new concepts of virtual tow point and fuzzy danger factor for differential wheeled mobile robots. Since differential wheeled mobile robot has smaller degree of freedom to control and are non-holonomic systems, there exist multiple solutions (trajectories) to control and reach a target position. The paper proposes 'fuzzy danger factor' for obstacles avoidance, 'virtual tow point' to solve non-holonomic object tracking control problem for unique solution and three kinds of fuzzy logic controller. The fuzzy logic controller is policy decision controller with fuzzy danger factor to decide which controller's result is more valuable when the mobile robot is tracking a moving object with obstacles to be avoided.