• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.2
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• pp.138-142
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• 2007

This paper presents an algorithm which extends the rapidly-exploring random tree (RRT) framework to deal with change of the task environments. This algorithm called the Retrieval RRT Strategy (RRS) combines a support vector machine (SVM) and RRT and plans the robot motion in the presence of the change of the surrounding environment. This algorithm consists of two levels. At the first level, the SVM is built and selects a proper path from the bank of RRTs for a given environment. At the second level, a real path is planned by the RRT planners for the: given environment. The suggested method is applied to the control of $KUKA^{TM}$, a commercial 6 DOF robot manipulator, and its feasibility and efficiency are demonstrated via the cosimulatation of $MatLab^{TM}\;and\;RecurDyn^{TM}$.

• The Journal of Korea Robotics Society
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• v.7 no.2
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• pp.150-159
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• 2012

In this paper, we propose a cost-aware Rapidly-exploring Random Tree (RRT) path planning algorithm for mobile robots. A mobile robot is presented with a cost map of the field of interest and assigned to move from one location to another. As a robot moves, the robot is penalized by the cost at its current location according to the cost map. The overall cost of the robot is determined by the trajectory of the robot. The goal of the proposed cost-aware RRT algorithm is to find a trajectory with the minimal cost. The cost map of the field can represent environmental parameters, such as temperature, humidity, chemical concentration, wireless signal strength, and stealthiness. For example, if the cost map represents packet drop rates at different locations, the minimum cost path between two locations is the path with the best possible communication, which is desirable when a robot operates under the environment with weak wireless signals. The proposed cost-aware RRT algorithm extends the basic RRT algorithm by considering the cost map when extending a motion segment. We show that the proposed algorithm gives an outstanding performance compared to the basic RRT method. We also demonstrate that the use of rejection sampling can give better results through extensive simulation.

• Korean Journal of Veterinary Research
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• v.15 no.1
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• pp.93-99
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• 1975

In order to compare the diagnostic value of Modified California Mastitis Test (MCMT), Modified Whiteside Test (MWT) and Resazurin Reduction Test (RRT) using the direct microscopic leucocyte count (DMLC) as standard, a total of 739 quarter milk samples were examined. The results obtained were as follows: 1. Of the 739 samples, 24.4% had positive DMLC value (over 500,000 leucocytes per mI.), 32.6% positive MCMT reaction, 34.9% positive RRT reaction and 39.9% positive MWT reaction. 2. The identical ratings of the three mastitis screening tests with DMLC values were 60.7% (MWT), 61. 8% (MCMT) and 72.1% (RRT). 3. The mean reaction values of the predicted mastitis screening tests were $1.09{\pm}0.01$ (MWT), $1.12{\pm}0.06$ (MCMT) and $1.25{\pm}0.40$ (RRT). The efficiency ratings of them were 34.8% (MWT), 49.3% (RRT) and 55.0% (MCMT) respectively.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.3
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• pp.217-225
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• 2016

In this paper, we propose an improved path planning method and obstacle avoidance algorithm for two-wheel mobile robots, which can be effectively applied in an environment where obstacles can be represented by circles. Firstly, we briefly introduce the rapidly exploring random tree (RRT) and single polar polynomial (SPP) algorithm. Secondly, we present additional two methods for applying our proposed method. Thirdly, we propose a global path planning, smoothing and obstacle avoidance method that combines the RRT and SPP algorithms. Finally, we present a simulation using our proposed method and check the feasibility. This shows that proposed method is better than existing methods in terms of the optimality of the trajectory and the satisfaction of the kinematic constraints.

• Journal of the Korean Society for Nondestructive Testing
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• v.29 no.3
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• pp.256-263
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• 2009

After several PWSCCs were found in Bugey(France), Ringhals(Sweden), Tihange(Belgium), Oconee, Arkansas, Crystal Fever, Davis-Basse, VC Summer(U.S.A.), Thuruga(Japan), USNRC and PNNL started the research on PWSCC, that is, the PINC project. USNRC required KINS to participate in the PINC project in May 2005. KINS organized the Korean consortium at March 2006 and Pre-RRT for RVHP were performed for the preparation of PINC RRT. Through these preliminary RRT, Korea NDE teams can learn and develop the detection and sizing technique for RVHP dissimilar metal weld. These techniques are now being prepared in Korea and need to be utilized for the In-service inspection of the RVHP and BMI of Korea Nuclear Power Plants. PINC RRT mock-ups will be helpful to training.

• Nuclear Engineering and Technology
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• v.51 no.3
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• pp.825-836
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• 2019

One of the most challenging safety precautions for workers in dynamic, radioactive environments is avoiding radiation sources and sustaining low exposure. This paper presents a sampling-based algorithm, DL-RRT*, for minimum dose walk-path re-planning in radioactive environments, expedient for occupational workers in nuclear facilities to avoid unnecessary radiation exposure. The method combines the principle of random tree star ($RRT^*$) and $D^*$ Lite, and uses the expansion strength of grid search strategy from $D^*$ Lite to quickly find a high-quality initial path to accelerate convergence rate in $RRT^*$. The algorithm inherits probabilistic completeness and asymptotic optimality from $RRT^*$ to refine the existing paths continually by sampling the search-graph obtained from the grid search process. It can not only be applied to continuous cost spaces, but also make full use of the last planning information to avoid global re-planning, so as to improve the efficiency of path planning in frequently changing environments. The effectiveness and superiority of the proposed method was verified by simulating radiation field under varying obstacles and radioactive environments, and the results were compared with $RRT^*$ algorithm output.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.11
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• pp.809-817
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• 2022

The development of unmanned robots and UAVs has increased the need for path planning methods such as RRT^* algorithm. It mostly works well in various environments and is utilized in many fields. A lot of research has been conducted to obtain a better path in terms of efficiency through various modifications to the RRT^* algorithm, and the performance of the algorithm is continuously improved thanks to these efforts. In this study, a method using the limitation of sampling range is proposed as an extension of these efforts. Based on the idea that a path passing close to obstacles is similar to the optimal path in obstacle environments, nodes are produced around the obstacle. Also, rewiring algorithm is modified to quickly obtain the path. The performance of the proposed algorithm is validated by comparative analysis of the previous basic algorithm and the generated path is tracked by a UAV's kinematic model for further verification.

• The Korean Journal of Applied Statistics
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• v.28 no.1
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• pp.75-82
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• 2015

We discuss confidence intervals for sensitive binomial attributes obtained by a direct question method and indirect question method. The Randomized Response Technique(RRT) by Warner (1965) is an indirect question method that uses a randomization device to reduce the response burden of respondents. We used the mean coverage probability (MCP), root mean squared error (RMSE), and mean expected width (MEW) to compare the confidence intervals by the two methods. The numerical comparisons indicated found that the MEW of RRT is too large and the RRT is so conservative that the MCP exceeds a nominal level(${\alpha}$); therefore, it is necessary to complement these problem in order to increase the utility of the indirect question method.

• Journal of the Korean Society for Nondestructive Testing
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• v.29 no.3
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• pp.248-255
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• 2009

After several damages by PWSCC were found in the world, USNRC and PNNL(Pacific Northwest National Laboratory) started the research on PWSCC under the project name of PINC. The aim of the project was 1) to fabricate representative NDE mock-ups with flaws to simulate PWSCCs, 2) to identify and quantitatively assess NDE methods for accurately detecting, sizing and characterizing PWSCCs, 3) to document the range of locations and morphologies of PWSCCs and 4) to incorporate results with other results of ongoing PWSCC research programs, as appropriate. Korea nuclear industries have also been participating in the project. Thermally and mechanically cracked-four mockups were prepared and phased array and manual ultrasonic testing(UT) techniques were applied. The results and lessons learned from the preliminary RRT are summarized as follows: 1) Korea RRT teams performed the RRT successfully. 2) Crack detection probability of the participating organizations was an average 87%, 80% and 80% respectively. 3) RMS error of the crack sizing showed comparatively good results. 4) The lessons learned may be helpful to perform the PINC RRT and PSI /ISI in Korea in the future.

• Journal of Advanced Navigation Technology
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• v.27 no.4
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• pp.365-373
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• 2023

Unmanned aerial vehicles are widely used in various fields, and real-time path replanning is a critical factor in enhancing the safety and efficiency of these devices. In this paper, we propose a real-time path replanning technique based on RRT* and LOSPO. The proposed technique first generates an initial path using the RRT* algorithm and then optimizes the path using LOSPO. Additionally, the optimized path can be converted into a trajectory that considers actual time and the dynamic limits of the aircraft. In this process, environmental changes and collision risks are detected in real-time, and the path is replanned as needed to maintain safe operation. This method has been verified through simulation-based experiments. The results of this paper make a significant contribution to the research on real-time path replanning for UAVs, and by applying this technique to various situations, the safety and efficiency of UAVs can be improved.