• Journal of the Korean Society of Industry Convergence
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• v.17 no.3
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• pp.151-169
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• 2014

We propose a new technique for autonomous navigation and travelling of mobile robot based on ultrasonic sensors through the narrow labyrinth that leave only distance of a few centimeters on each side between the guides and the robot. In our current implementation the ultrasonic sensor system fires at a rate of 100 ms, that is, each of the 8 sensors fires once during each 100 ms interval. This is a very good firing rate, implemented here for optimal performance. This paper presents an extensively tested and verified solution to the problem of obstacle avoidance. Our solution is based on the optimal placement of ultrasonic sensors at strategic locations around the robot. Both the sensor location and the associated navigation algorithm are defined in such a way that only the accurate radial sonar data is used for accurate travelling.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.32 no.7
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• pp.69-75
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• 2004

An avoidance/guidance problem of an aircraft against moving obstacle is considered in two dimensional space. The aircraft is modelled as a point mass flying with constant speed. The lateral acceleration is assumed the control input. Artificial potential functions are applied to the terminal point and moving obstacles in order that repulsive forces and an attractive force are produced by the obstacles and the terminal point respectively. A real time guidance/avoidance law is proposed by using the potential forces and relative velocity. The guidance law for a logarithm potential function results the well-known proportional navigation law. The avoidance control command is inverse proportional to the time-to-go to the obstacle and turns the aircraft toward the negative direction of the line-of-sight change. The performance of the proposed guidance/avoidance law is verified with simulations.

• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.6
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• pp.951-957
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• 2010

An optimal path generation method considering the safety of UGV(Unmanned Ground Vehicle) is proposed and demonstrated through examples. Among various functions of UGV, real-time obstacle avoidance is a key issue to realize realistic scenario in FCS(Future Combat Systems). A two-dimensional narrow corridor environment is considered as a test field. For each step of UGV movement, two objectives are considered: One is to minimize the distance to the target and the other to maximize the distance to the nearest point of an obstacle. A weighted objective function is used in the optimization problem. Equality and inequality constraints are taken to secure the UGV's dynamics and safety. The weighting factors are controlled by a fuzzy controller which is constructed by a fuzzy rule set and membership functions. Simulations are performed for two cases. First the weighting factors are considered as constant values to understand the characteristics of the corresponding solutions and then as variables that are adjusted by the fuzzy controller. The results are satisfactory for realistic situations considered. The proposed optimal path generation with the fuzzy control is expected to be well applicable to real environment.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.12
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• pp.2220-2227
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• 2008

The safety equipments of railway level crossing which are installed at intersections between roads and railway lines prevent level crossing accidents by informing all of the vehicles and pedestrians of approaching trains. The intelligent safety system for level crossing which employs information and communication technology has been developed in USA and Japan, etc. But, in Korea, the relevant research has not been performed. In this paper, we analyze the cause of railway level crossing accidents and the inherent problem of the existing safety equipments. Based on analyzed results, we design the intelligent safety system which prevent collision between a train and a vehicle. This system displays train approaching information in real-time at roadside warning devices, informs approaching train of the detected obstacle in crossing areas, and is interconnected with traffic signal to empty the crossing area before train comes. Especially, we present the video based obstacle detection algorithm and verify its performance with prototype H/W since the abrupt obstacles in crossing areas are the main cause of level crossing accidents. We identify that the presented scheme detects both pedestrian and vehicle with good performance.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.10
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• pp.1479-1485
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• 2015

Robot's technology was very simple and repetitive in the past. Nowadays, robots are required to perform intelligent operation. So, path planning has been studied extensively to create a path from start position to the goal position. In this paper, potential field algorithm was used for path planning in dynamic environments. It is used for a path plan of mobile robot because it is elegant mathematical analysis and simplicity. However, there are some problems. The problems are collision risk, avoidance path, time attrition. In order to resolve path problems, we amalgamated potential field algorithm with the artificial neural network system. The input of the neural network system is set using relative velocity and location between the robot and the obstacle. The output of the neural network system is used for the weighting factor of the repulsive potential function. The potential field algorithm problem of mobile robot's path planning can be improved by using artificial neural network system. The suggested algorithm was verified by simulations in various dynamic environments.

• Journal of Internet of Things and Convergence
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• v.7 no.4
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• pp.29-34
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• 2021

In order to apply reinforcement learning to a robot in a real environment, it is necessary to use simulation in a virtual environment because numerous iterative learning is required. In addition, it is difficult to apply a learning algorithm that requires a lot of computation for a robot with low-spec. hardware. In this study, ML-Agent, a reinforcement learning frame provided by Unity, was used as a virtual simulation environment to apply reinforcement learning to the obstacle collision avoidance problem of mobile robots with low-spec hardware. A DQN supported by ML-Agent is adopted as a reinforcement learning algorithm and the results for a real robot show that the number of collisions occurred less then 2 times per minute.

• The Journal of Korea Robotics Society
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• v.18 no.3
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• pp.337-345
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• 2023

In this paper, we propose a 3D LiDAR sensor-based costmap generation and path planning algorithm using it for reliable autonomous flight in complex indoor environments. 3D path planning is essential for reliable operation of UAVs. However, existing grid search-based or random sampling-based path planning algorithms in 3D space require a large amount of computation, and UAVs with weight constraints require reliable path planning results in real time. To solve this problem, we propose a method that divides a 3D space into several 2D spaces and a path planning algorithm that considers the distance to obstacles within each space. Among the paths generated in each space, the final path (Best path) that the UAV will follow is determined through the proposed objective function, and for this purpose, we consider the rotation angle of the 2D space, the path length, and the previous best path information. The proposed methods have been verified through autonomous flight of UAVs in real environments, and shows reliable obstacle avoidance performance in various complex environments.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.31 no.3
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• pp.71-78
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• 2023

To minimize the occurrence of aviation accidents in the post-COVID, continuous flight training is crucial. However, the current infrastructure of domestic airports and airfields is insufficient to prepare for and respond to accidents, and there is a need for sufficient facility capacity. Therefore, this study examines the construction of a regional airport to minimize aviation accidents and selects factors necessary for determining the site location. Among the 11 selected factors, six were considered the most important site selection factors, including noise issues, weather conditions, obstacle limitations, environmental issues, airspace conditions, and facility usability. Applying these factors, an analysis was conducted on the Songpo area of Sacheon City, Gyeongsangnam-do. Based on a comprehensive review, it can be concluded that the Songpo area is a suitable choice for a regional airport due to its excellent transportation environment, consideration of noise and environmental issues with the residential population, and other factors. Furthermore, the development of the aviation industry is expected to bring about an increase in tourism and economic benefits, and it is anticipated to make a significant contribution to the domestic aviation industry along with the construction of the currently under-construction Ulleung Airport.

• Journal of Electrical Engineering and Technology
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• v.12 no.2
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• pp.918-925
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• 2017

This paper presents a path planning problem for multi-robot system in the environment with dynamic obstacles. In order to guide the robots move along a collision-free path efficiently and reach the goal position quickly, a navigation method based on fuzzy logic controllers has been developed by using proximity sensors. There are two kinds of fuzzy controllers developed in this work, one is used for obstacle avoidance and the other is used for orientation to the target. Both static and dynamic obstacles are included in the environment and the dynamic obstacles are defined with no type of restriction of direction and velocity. Here, the environment is unknown for all the robots and the robots should detect the surrounding information only by the sensors installed on their bodies. The simulation results show that the proposed method has a positive effectiveness for the path planning problem.

• International Journal of Aeronautical and Space Sciences
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• v.18 no.4
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• pp.767-779
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• 2017

Automated mission planning for unmanned aerial vehicles (UAVs) is difficult because of the propagation of several sources of error into the solution, as for any large scale autonomous system. To ensure reliable system performance, we quantify all sources of error and their propagation through a mission planner for operation of UAVs in an obstacle rich environment we developed in prior work. In this sequel to that work, we show that the mission planner developed before can be made robust to errors arising from the mapping, sensing, actuation, and environmental disturbances through creating systematic buffers around obstacles using the calculations of uncertainty propagation. This robustness makes the mission planner truly autonomous and scalable to many UAVs without human intervention. We illustrate with simulation results for trajectory generation of multiple UAVs in a surveillance problem in an urban environment while optimizing for either maximal flight time or minimal fuel consumption. Our solution methods are suitable for any well-mapped region, and the final collision free paths are obtained through offline sub-optimal solution of an mTSP (multiple traveling salesman problem).