• Journal of the Korean Society of Manufacturing Process Engineers
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• v.10 no.6
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• pp.95-101
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• 2011

Micro-abrasive jet machining(${\mu}AJM$) using mask is a fine machining technology which can carve a figure on a material. The mask should have holes exactly same as the required figure. Abrasive particles are jetted into the holes of the mask and it collide with the material. The collision break off small portion of the material. And the ${\mu}AJM$ nozzle should move all over the machining area. However, in general the carving shape is modeled as in a bitmap figure, because it often contains characters. And the mask model is also often modeled from the bitmap image. Therefore, the machining path of the ${\mu}AJM$ also efficient if it can be generated from the bitmap image. This paper suggest an algorithm which can generate ${\mu}AJM$ tool path directly from the bitmap image of the carving figure. And shows some test results and applications.

• Journal of the Society of Naval Architects of Korea
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• v.56 no.1
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• pp.58-65
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• 2019

The stability, reliability and efficiency of a smart ship are important issues as the interest in an autonomous ship has recently been high. An automatic collision avoidance system is an essential function of an autonomous ship. This system detects the possibility of collision and automatically takes avoidance actions in consideration of economy and safety. In order to construct an automatic collision avoidance system using reinforcement learning, in this work, the sequential decision problem of ship collision is mathematically formulated through a Markov Decision Process (MDP). A reinforcement learning environment is constructed based on the ship maneuvering equations, and then the three key components (state, action, and reward) of MDP are defined. The state uses parameters of the relationship between own-ship and target-ship, the action is the vertical distance away from the target course, and the reward is defined as a function considering safety and economics. In order to solve the sequential decision problem, the Deep Deterministic Policy Gradient (DDPG) algorithm which can express continuous action space and search an optimal action policy is utilized. The collision avoidance system is then tested assuming the $90^{\circ}$intersection encounter situation and yields a satisfactory result.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1457-1463
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• 2018

In recent times, Natural User Interface/Natural User Experience (NUI/NUX) technology has found widespread application across a diverse range of fields and is also utilized for controlling unmanned aerial vehicles (UAVs). Even if the user controls the UAV by utilizing the NUI/NUX technology, it is difficult for the user to easily control the UAV. The user needs an autopilot to easily control the UAV. The user needs a flight path to use the autopilot. The user sets the flight path based on the waypoints. UAVs normally fly straight from one waypoint to another. However, if flight between two waypoints is in a straight line, UAVs may collide with obstacles. In order to solve collision problems, flight records can be utilized to adjust the generated path taking the locations of the obstacles into consideration. This paper proposes a natural path generation method between waypoints based on flight records collected through UAVs flown by users. Bayesian probability is utilized to select paths most similar to the flight records to connect two waypoints. These paths are generated by selection of the center path corresponding to the highest Bayesian probability. While the K-means algorithm-based straight-line method generated paths that led to UAV collisions, the proposed method generates paths that allow UAVs to avoid obstacles.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.3
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• pp.210-216
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• 2016

This paper presents a novel collision avoidance technique for mobile robots based on omni-directional vision simultaneous localization and mapping (SLAM). This method estimates the avoidance path and speed of a robot from the location of an obstacle, which can be detected using the Lucas-Kanade Optical Flow in images obtained through fish-eye cameras mounted on the robots. The conventional methods suggest avoidance paths by constructing an arbitrary force field around the obstacle found in the complete map obtained through the SLAM. Robots can also avoid obstacles by using the speed command based on the robot modeling and curved movement path of the robot. The recent research has been improved by optimizing the algorithm for the actual robot. However, research related to a robot using omni-directional vision SLAM to acquire around information at once has been comparatively less studied. The robot with the proposed algorithm avoids obstacles according to the estimated avoidance path based on the map obtained through an omni-directional vision SLAM using a fisheye image, and returns to the original path. In particular, it avoids the obstacles with various speed and direction using acceleration components based on motion information obtained by analyzing around the obstacles. The experimental results confirm the reliability of an avoidance algorithm through comparison between position obtained by the proposed algorithm and the real position collected while avoiding the obstacles.

• Journal of KIISE:Software and Applications
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• v.29 no.1_2
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• pp.91-97
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• 2002

This paper describes a heuristic search technique carrying out collision avoidance for Autonomous Underwater Vehicles(AUVs). Fuzzy relational products are used as the mathematical implement for the analysis and synthesis of relations between obstacles that are met in the navigation environment and available candidate nodes. In this paper, we propose a more effective evaluation function that reflects the heuristic information of domain experts on obstacle clearance, and an advanced heuristic search method performing collision avoidance for AUVs. The search technique adopts fuzzy relational products to conduct path-planning of intelligent navigation system. In order to verify the performance of proposed heuristic search, it is compared with $A^*$ search method through simulation in view of the CPU time, the optimization of path and the amount of memory usage.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.8
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• pp.73-82
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• 2004

In this paper, implementation of obstacle avoidance of a nonholonomic mobile robot in unstructured environment is introduced. To avoid obstacles, first, a reference collision-free path for the MR is generated off-line using HJB-based optimal path planning method. A controller is designed using integrator backstepping method for tracking the generated reference path. To implement the designed controller, a control system are needed and composed of camera system and PIC-based controller. The workspace is observed by a ceiling-mounted USB camera as part of an un-calibrated camera system. Thus the positional information of the MR is updated frequently and the MR can get the useful inputs for its tracking controller. The whole control system is realized by integrating a computer with PIC-based microprocessor using wireless communication: the image processing control module and path planning module serve as high level computer control while the device control serves as low level PIC microprocessor control. The simulation and experimental results show the effectiveness of the designed control system.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.2
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• pp.168-177
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• 2008

Path finding is a key element in the navigation of a mobile robot. To find a path, robot should know their position exactly, since the position error exposes a robot to many dangerous conditions. It could make a robot move to a wrong direction so that it may have damage by collision by the surrounding obstacles. We propose a method obtaining an accurate robot position. The localization of a mobile robot in its working environment performs by using a vision system and Virtual Reality Modeling Language(VRML). The robot identifies landmarks located in the environment. An image processing and neural network pattern matching techniques have been applied to find location of the robot. After the self-positioning procedure, the 2-D scene of the vision is overlaid onto a VRML scene. This paper describes how to realize the self-positioning, and shows the overlay between the 2-D and VRML scenes. The suggested method defines a robot's path successfully. An experiment using the suggested algorithm apply to a mobile robot has been performed and the result shows a good path tracking.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.4
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• pp.187-194
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• 2002

In this paper, a motion control algorithm is developed using a fuzzy control and the optimization of performance function, which makes a robot arm avoid an unexpected obstacle when the end-effector of the robot arm is moving to the goal position. During talc motion, if there exists no obstacle, the end-effector of the robot arm moves along the predefined path. But if these exists an obstacle and close to talc robot arm, the fuzzy motion controller is activated to adjust the path of the end-effector of the robot arm. Then, the robot arm takes the optimal posture far collision avoidance with the obstacle. To show the feasibility of the developed algorithm, numerical simulations are carried out with changing both the positions and sites of obstacles. It was concluded that the proposed algorithm gives a good performance for obstacle avoidance.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.43 no.6 s.312
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• pp.9-15
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• 2006

The recent development in robot field shows that service robot which interacts with human and provides specific service to human has been researched continually. Especially, robot for human welfare becomes the center of public concern. At present time, guide robot is priority field of general welfare robot and helps the blind keep safe path when he walks outdoor. In this paper, guide robot provides not only collision avoidance but also the best walking direction and velocity to blind people while recognizing environment information from various kinds of sensors. In addition, it is able to provide the most safe path planing on behalf of blind people.

• Journal of Electrical Engineering and Technology
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• v.12 no.2
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• pp.890-903
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• 2017

The problem of determining a smooth and collision-free path with maximum possible speed for a Mobile Robot (MR) which is chasing a moving target in a dynamic environment is addressed in this paper. Genetic Network Programming with Reinforcement Learning (GNP-RL) has several important features over other evolutionary algorithms such as it combines offline and online learning on the one hand, and it combines diversified and intensified search on the other hand, but it was used in solving the problem of MR navigation in static environment only. This paper presents GNP-RL based on predicting collision positions as a first attempt to apply it for MR navigation in dynamic environment. The combination between features of the proposed collision prediction and that of GNP-RL provides safe navigation (effective obstacle avoidance) in dynamic environment, smooth movement, and reducing the obstacle avoidance latency time. Simulation in dynamic environment is used to evaluate the performance of collision prediction based GNP-RL compared with that of two state-of-the art navigation approaches, namely, Q-Learning (QL) and Artificial Potential Field (APF). The simulation results show that the proposed GNP-RL outperforms both QL and APF in terms of smooth movement and safer navigation. In addition, it outperforms APF in terms of preserving maximum possible speed during obstacle avoidance.