• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.37.3-37
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• 2002

$\textbullet$ Mobile robots $\textbullet$ Sensor-based motion planning $\textbullet$ Potential field $\textbullet$ Local minimum-free motion $\textbullet$ Virtual target point $\textbullet$ Set of linked line segments $\textbullet$ We build a sensor-based motion planning using virtual target point for free of local minimum

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.900-903
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• 1996

The objective of this paper is to make the weighted graph map for path planning using the ultrasonic sensor measurements that are acquired when an A.M.R (autonomous mobile robot) explores the unknown circumstance. First, The A.M.R navigates on unknown space with wall-following and gathers the sensor data from the environments. After this, we constructs the occupancy grid map by interpreting the gathered sensor data to occupancy probability. For the path planning of roadmap method, the weighted graph map is extracted from the occupancy grid map using morphological image processing and thinning algorithm. This methods is implemented on an A.M.R having a ultrasonic sensor.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.39 no.6
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• pp.549-559
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• 2015

In this paper, an efficient dynamic reactive motion planning method for an autonomous vehicle in a dynamic environment is proposed. The purpose of the proposed method is to improve the robustness of autonomous robot motion planning capabilities within dynamic, uncertain environments by integrating a virtual plane-based reactive motion planning technique with a sensor fusion-based obstacle detection approach. The dynamic reactive motion planning method assumes a local observer in the virtual plane, which allows the effective transformation of complex dynamic planning problems into simple stationary ones proving the speed and orientation information between the robot and obstacles. In addition, the sensor fusion-based obstacle detection technique allows the pose estimation of moving obstacles using a Kinect sensor and sonar sensors, thus improving the accuracy and robustness of the reactive motion planning approach. The performance of the proposed method was demonstrated through not only simulation studies but also field experiments using multiple moving obstacles in hostile dynamic environments.

• The Journal of Korea Robotics Society
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• v.6 no.3
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• pp.255-262
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• 2011

Many intelligent robots have to be given environmental information to perform tasks. In this paper an intelligent robot, that is, a cleaning robot used a sensor fusing method of two sensors: LRF and StarGazer, and then was able to obtain the information. Throughout wall following using laser displacement sensor, LRF, the working area is built during the robot turn one cycle around the area. After the process of wall following, a path planning which is able to execute the work effectively is established using flow network algorithm. This paper describes an algorithm for minimal turning complete coverage path planning for intelligent robots. This algorithm divides the whole working area by cellular decomposition, and then provides the path planning among the cells employing flow networks. It also provides specific path planning inside each cell guaranteeing the minimal turning of the robots. The proposed algorithm is applied to two different working areas, and verified that it is an optimal path planning method.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.12
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• pp.577-582
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• 2002

The objective of this paper is to produce a weighted graph map for path-planning of an autonomous mobile robot(AMR) based on the measurements from a single ultrasonic sensor, which are acquired when the autonomous mobile robot explores unknown indoor circumstance. The AMR navigates in th unknown space by following the wall and gathers the range data using the ultrasonic sensor, from which the occupancy grid map is constructed by associating the range data with occupancy certainties. Then, the occupancy grid map is converted to a weighted graph map suing morphological image processing and thinning algorithms. the path- planning for autonomous navigation of a mobile robot can be carried out based on the occupancy grid map. These procedures are implemented and tested using an AMR, and primary results are presented in this paper.

• Journal of the Semiconductor & Display Technology
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• v.17 no.1
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• pp.88-92
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• 2018

Currently drone industry has become one of the fast growing markets and the technology for unmanned aerial vehicles are expected to continue to develop at a rapid rate. Especially small unmanned aerial vehicle systems have been designed and utilized for the various field with their own specific purposes. In these fields the path planning problem to find the shortest path between two oriented points is important. In this paper we introduce a path planning strategy for an autonomous flight of unmanned aerial vehicles through reinforcement learning with self-positioning technique. We perform Q-learning algorithm, a kind of reinforcement learning algorithm. At the same time, multi sensors of acceleraion sensor, gyro sensor, and magnetic are used to estimate the position. For the functional evaluation, the proposed method was simulated with virtual UAV environment and visualized the results. The flight history was based on a PX4 based drones system equipped with a smartphone.

• The Journal of Korea Robotics Society
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• v.2 no.1
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• pp.71-79
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• 2007

We propose and develop Home Security robot system based on Sensor Network (HSSN) configured by sensor nodes including radio frequency (RF), ultrasonic, temperature, light and sound sensors. Our system can acknowledge security alarm events that are acquired by sensor nodes and relayed in the hop-by-hop transmission way. There are sensor network, Home Security Mobile Robot (HSMR) and Home Server(HS) in this system. In the experimental results of this system, we presented that our system has more enhanced performance of response to emergency context and more speedy and accurate path planning to target position for arriving an alarm zone with obstacle avoidance and acquiring the context-aware information.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.1
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• pp.108-114
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• 2009

In this paper, we presents a behavior planning for humanoid robots using behavior primitive in 3 dimensional workspace. Also, we define behavior primitives that humanoid robot accomplishes various tasks effectively. Humanoid robot obtains information of the outside environment and its inner information from various sensors in complex workspace with various obstacles. We verify our approach on a developed small humanoid robot using embedded vision and sensor system in a experimental environment. The experimental results show that the humanoid robot performs its tasks fast and effectively.

• Journal of the Korean Society of Radiology
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• v.13 no.1
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• pp.39-44
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• 2019

Radiation safety management is being considered very important since radioactive isotopes such as Co-60 and Ir-192 are widely used in fields such as non-destructive test(NDT). In this study, the applicability of Mercury(II) Iodide($HgI_2$) source for tracing system was evaluated. To make sure the unit cell sensor's reliability, we evaluated the electrical properties of the sensor made with $HgI_2$, and then position dependence of the sensor was analyzed and compared with the dose distribution from the planning system. As a result of the evaluation, high reliability of the sensor was shown through the linearity of R-sq > 0.990 and reproducibility of CV < 0.015. In the position dependence evaluation, the maximum value was measured at the isocenter of the sensor and gradually decreased according to the distance. However, the dose distribution data from the planning system was turned out that has difference with that of the sensor up to 30%. This seems to come from the difference between single-point measuring based planning system and area measuring based sensor.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.9
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• pp.785-798
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• 2004

This paper introduces the development of a range sensor based integrated navigation system for a multi-functional indoor service robot, called PSR (Public Service Robot System). The proposed navigation system includes hardware integration for sensors and actuators, the development of crucial navigation algorithms like mapping, localization, and path planning, and planning scheme such as error/fault handling. Major advantages of the proposed system are as follows: 1) A range sensor based generalized navigation system. 2) No need for the modification of environments. 3) Intelligent navigation-related components. 4) Framework supporting the selection of multiple behaviors and error/fault handling schemes. Experimental results are presented in order to show the feasibility of the proposed navigation system. The result of this research has been successfully applied to our three service robots in a variety of task domains including a delivery, a patrol, a guide, and a floor cleaning task.