• Journal of Institute of Control, Robotics and Systems
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• v.22 no.4
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• pp.253-265
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• 2016

We propose an obstacle classification method using multi-decision factors and decision sections based on Single 2D LiDAR. The existing obstacle classification method based on single 2D LiDAR has two specific advantages: accuracy and decreased calculation time. However, it was difficult to classify obstacle type, and therefore accurate path planning was not possible. To overcome this problem, a method of classifying obstacle type based on width data was proposed. However, width data was not sufficient to enable accurate obstacle classification. The proposed algorithm of this paper involves the comparison between decision factor and decision section to classify obstacle type. Decision factor and decision section was determined using width, standard deviation of distance, average normalized intensity, and standard deviation of normalized intensity data. Experiments using a real autonomous vehicle in a real environment showed that calculation time decreased in comparison with 2D LiDAR-based method, thus demonstrating the possibility of obstacle type classification using single 2D LiDAR.

• The Journal of Korea Robotics Society
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• v.2 no.1
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• pp.55-63
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• 2007

Mobile robots traveling on rough terrain need several algorithms to overcome obstacles. In this paper, we propose the step-type obstacle traversal algorithm to adapt the mobile robot with six arms and wheels to travel on rough terrain. Obstacle traversal is composed of two different stages: planning and control. In planning stage, the required joint torque of each arm as well as the interference between the wheels and the arms are analyzed to guarantee traversing obstacles. Control stage includes such steps as checking distance to obstacle, determining the height and length of obstacle, performing arm motion according to sensed torque data, and evaluating safety at every instance. The proposed algorithm is designed and implemented for CALEB 1 six legged robot developed in the laboratory and verified by simulation and experiment in outdoor environment.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.378-380
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• 2009

For the autonomous movement, the optimal pat]1 planning connecting between current and target positions is essential, and the optimal path of mobile robot means obstacle-free and the shortest length path to a target position. Many actual mobile robots should move without any information of surrounded obstacles. This paper suggests a new method of obstacle avoidment which is suitable in unknown environments. This method of obstacle avoidance is designed with a distributed fuzzy control system, and imitates a Potential Field method. A simulation confirms the performance and correctness of the obstacle avoidance.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.5
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• pp.551-561
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• 1992

A neural optimization network and fuzzy rules are proposed to control the redundant robot manipulators in an environment with obstacle. A neural optimization network is employed to solve the optimization problem for resolved motion control of redundant robot manipulators in an environment with obstacle. The fuzzy rules are proposed to determine the weights of neural optimization networks to avoid the collision between robot manipulators and obstacle. The inputs of fuzzy rules are the resultant distance and change of the distance and sum of the changes by differential motion of each joint. And the output of fuzzy rules is defined as the capability of collision avoidance of joint differential motion. The weightings of neural optimization networks are adjusted according to the capability of collision aboidance of each joint. To show the validities of the proposed method, computer simulation results are illustrated for the redundant robot of the planar type with three degrees of freedom.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.1
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• pp.82-86
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• 2008

The article presents ARTMAP and Fuzzy BK-Product approach underwater obstacle avoidance for the Autonomous underwater Vehicles (AUV). The AUV moves an unstructured area of underwater and could be met with obstacles in its way. The AUVs are equipped with complex sensorial systems like camera, aquatic sonar system, and transducers. A Neural integrated Fuzzy BK-Product controller, which integrates Fuzzy logic representation of the human thinking procedure with the learning capabilities of neural-networks (ARTMAP), is developed for obstacle avoidance in the case of unstructured areas. In this paper, ARTMAP-Fuzzy BK-Product controller architecture comprises of two distinct elements, are 1) Fuzzy Logic Membership Function and 2) Feed-Forward ART component. Feed-Forward ART component is used to understanding the unstructured underwater environment and Fuzzy BK-Product interpolates the Fuzzy rule set and after the defuzzyfication, the output is used to take the decision for safety direction to go for avoiding the obstacle collision with the AUV. An on-line reinforcement learning method is introduced which adapts the performance of the fuzzy units continuously to any changes in the environment and make decision for the optimal path from source to destination.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.51 no.3
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• pp.155-160
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• 2002

This paper focuses on the problem of local obstacle avoidance of mobile robots. To solve this problem, the safety direction section search algorithm is suggested. This concept is mainly composed with non-collision section and collision section from the detecting area of laser scanning sensor. Then, we will search for the most suitable direction in these sections. The proposed local motion planning method is simple and requires less computation than others. An environment model is developed using the vector space concept to determine robot motion direction taking the target direction, obstacle configuration, and robot trajectory into account. Since the motion command is obtained considering motion dynamics, it results in smooth and fast as well as safe movement. Using the mobile base, the proposed obstacle avoidance method is tested, especially in the environment with pillar, wall and some doors. Also, the proposed autonomous motion planning and control algorithm are tested extensively. The experimental results show the proposed method yields safe and stable robot motion through the motion speed is not so fast.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.38 no.1
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• pp.7-18
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• 2001

In this paper, we present a new method to generate obstacle-free path by using conformal mapping, when avatar navigates in virtual environment. First, we show that the proposed method generates a path to keep away from a circular obstacle. Then, we show that the method can be extended to an elliptical obstacle and multiple obstacles. For real applications, we combine the proposed local method with a global navigation method using sub-target to generate a global obstacle-free path by which avatar navigates naturally in virtual environment.

• The Journal of Korea Robotics Society
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• v.12 no.3
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• pp.297-305
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• 2017

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.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.871-875
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• 2003

To detect and avoid obstacles is one of the important tasks of mobile navigation. In a real environment, when a mobile robot encounters dynamic obstacles, it is required to simultaneously detect and avoid obstacles for its body safely. In previous vision system, mobile robot has used it as either a passive sensor or an active sensor. This paper proposes a new obstacle detection algorithm that uses a stereo camera as both a passive sensor and an active sensor. Our system estimates the distances from obstacles by both passive-correspondence and active-correspondence using laser slit. The system operates in three steps. First, a far-off obstacle is detected by the disparity from stereo correspondence. Next, a close obstacle is acquired from laser slit beam projected in the same stereo image. Finally, we implement obstacle avoidance algorithm, adopting the modified Dynamic Window Approach (DWA), by using the acquired the obstacle's distance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.12B
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• pp.1349-1358
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• 2009

Because of the limitation of supporting power, the current WSN(Wireless Sensor Network) Technologies whose one of the core attributes is low power consumption are the best solution for shipping container networking in stack environment such as on vessel. So it is effective to use the Wireless Sensor Network Technology. In this case, many nodes join in the network through a sink node because there are difficulties to get big money and efforts to set up a lot of sink node. It needs clustering-based proactive protocol to manage many nodes. But it shows low reliability because they have effect on radio frequency in metal obstacle environments(interference, distortion, reflection, and etc) like the intelligent container. In this paper, we proposed an improved Modified LEACH Protocol for stableness radio frequency environment. In the proposed protocol, we tried to join the network and derived stable topology composition after the measuring of link quality. Finally, we verified that the proposed protocol is composing more stable topology than previously protocol in metal obstacle environment.