• Transactions of the Korean Society of Automotive Engineers
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• v.11 no.5
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• pp.183-192
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• 2003

Obstacle detection and avoidance are considered as one of the key technologies on an unmanned vehicle system. In this paper, we propose a method of obstacle detection and avoidance and it is composed of vehicle control, modeling, and sensor experiments. Obstacle detection and avoidance consist of two parts: one is longitudinal control system for acceleration and deceleration and the other is lateral control system for steering control. Each system is used for unmanned vehicle control, which notes its location, recognizes obstacles surrounding it, and makes a decision how fast to proceed according to circumstances. During the operation, the control system of the vehicle can detect obstacles and perform obstacle avoidance on the road, which involves vehicle velocity. In this paper, we propose a method for vehicle control, modeling, and obstacle avoidance, which are evaluated through road tests.

• International Journal of Automotive Technology
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• v.4 no.4
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• pp.173-180
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• 2003

Obstacle avoidance is considered as one of the key technologies in an unmanned vehicle system. In this paper, we propose a method of obstacle avoidance, which can be expressed as vehicle control, modeling, and sensor experiments. Obstacle avoidance consists of two parts: one longitudinal control system for acceleration; and deceleration and a lateral control system for steering control. Each system is used for unmanned vehicle control, which notes its location, recognizes obstacles surrounding it, and makes a decision how fast to proceed according to circumstances. During the operation, the control strategy of the vehicle can detect obstacles and perform obstacle avoidance on the road, which involves vehicle velocity. The method proposed for vehicle control, modeling, and obstacle avoidance has been confirmed through vehicle tests.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.932-937
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• 2003

In this paper, we will explain about the unmanned vehicle control and modeling for combined obstacle avoidance and lane tracking. First, obstacle avoidance is considered as one of the important technologies in the unmanned vehicle. It is consisted by two parts: the first part includes the longitudinal control system for the acceleration and deceleration and the second part is the lateral control system for the steering control. Each system uses to the obstacle avoidance during the vehicle moving. Therefore, we propose the method of vehicle control, modeling and obstacle avoidance. Second, we describe a method of lane tracking by means of vision system. It is important in the unmanned vehicle and mobile robot system. In this paper, we deal with lane tracking and image processing method and it is including lane detection method, image processing algorithm and filtering method.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.487-490
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• 2003

In this research, we will devise an obstacle avoidance algorithm for a previously unmanned vehicle. Whole systems consist mainly of the vehicle system and the control system. The two systems are separated; this system can communicate with the vehicle system and the control system through wireless RF (Radio Frequency) modules. These modules use wireless communication. And the vehicle system is operated on PIC Micro Controller. Obstacle avoidance method for unmanned vehicle is based on the Virtual Force Field (VFF) method. An obstacle exerts repulsive forces and the lane center point applies an attractive force to the unmanned vehicle. A resultant force vector, comprising of the sum of a target directed attractive force and repulsive forces from an obstacle, is calculated for a given unmanned vehicle position. With resultant force acting on the unmanned vehicle, the vehicle's new driving direction is calculated, the vehicle makes steering adjustments, and this algorithm is repeated.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.2
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• pp.192-197
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• 2009

In this paper, we proposed the obstacle avoidance system using a single camera image and LM(Levenberg-Marquart) neural network fuzzy controller. According to a robot technology adapt to various fields of industry and public, the robot has to move using self-navigation and obstacle avoidance algorithms. When the robot moves to target point, obstacle avoidance is must-have technology. So in this paper, we present the algorithm that avoidance method based on fuzzy controller by sensing data and image information from a camera and using the LM neural network to minimize the moving error. And then to verify the system performance of the simulation test.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.691-694
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• 2000

In this paper, a motion control algorithm is proposed by using neural network system, which makes a robot arm successfully avoid unexpected obstacle when the robot is moving from the start to the goal position. During the motion, if there is an obstacle the vision system recognizes it. And in every time the optimization-algorithm quickly chooses a motion among the possible motions of robot. The proposed algorithm has a good avoidance characteristic in simulation.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.408-412
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• 2003

Obstacle avoidance algorithm is very important on an unmanned vehicle. Therefore, in this research, we propose a algorithm of obstacle avoidance and we can prove through vehicle test and sensor experiments. Obstacle avoidance must be divided into two parts: the first part includes the longitudinal control for acceleration and deceleration and the second part is the lateral control for steering control. Each system is used for unmanned vehicle control, which notes its location, recognizes obstacles surrounding it, and makes a decision how fast to proceed according to circumstances. During the operation, the control strategy of the vehicle can detect obstacles and perform obstacle avoidance on the road, which involves vehicle velocity. In this paper, we propose a method for vehicle control, modeling, and obstacle avoidance, which are confirmed through vehicle tests.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.1
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• pp.89-93
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• 2009

This paper deals with obstacle avoidance for unmanned vehicle using stereo system. The "DARPA Grand Challenge 2005" shows that the robot can move autonomously under given waypoint. RADAR, IMS (Inertial Measurement System), GPS, camera are used for autonomous navigation. In this paper, we focus on stereo system for autonomous navigation. Our approach is based on Singh et. al. [5]'s approach that is successfully used in an unmanned vehicle and a planetary robot. We propose an improved algorithm for obstacle avoidance by modifying the cost function of Singh et. al. [5]. Proposed algorithm gives more sharp contrast in choosing local path for obstacle avoidance and it is verified in experimental results.

• Journal of Advanced Navigation Technology
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• v.11 no.4
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• pp.359-370
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• 2007

Lane tracking and obstacle avoidance are considered two of the key technologies on an unmanned ground vehicle system. In this paper, we propose a method of lane tracking and obstacle avoidance, which can be expressed as vehicle control, modeling, and sensor experiments. First, obstacle avoidance consists of two parts: a longitudinal control system for acceleration and deceleration and a lateral control system for steering control. Each system is used for unmanned ground vehicle control, which notes the vehicle's location, recognizes obstacles surrounding it, and makes a decision how fast to proceed according to circumstances. During the operation, the control strategy of the vehicle can detect obstacle and perform obstacle avoidance on the road, which involves vehicle velocity. Second, we explain a method of lane tracking by means of a vision system, which consists of two parts: First, vehicle control is included in the road model through lateral and longitudinal control. Second, the image processing method deals with the lane tracking method, the image processing algorithm, and the filtering method. Finally, in this paper, we propose a method for vehicle control, modeling, lane tracking, and obstacle avoidance, which are confirmed through vehicles tests.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.1
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• pp.161-166
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• 2011

This paper addresses the vision based autonomous walking control system. To handle the obstacles which exist beyond the field of view(FOV), we used the 3d panoramic depth image. Moreover, to decide the avoidance direction and walking motion of a humanoid robot for the obstacle avoidance by itself, we proposed the vision based path planning using 3d panoramic depth image. In the vision based path planning, the path and walking motion are decided under environment condition such as the size of obstacle and available avoidance space. The vision based path planning is applied to a humanoid robot, URIA. The results from these evaluations show that the proposed method can be effectively applied to decide the avoidance direction and the walking motion of a practical humanoid robot.