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Univector Field Method based Multi-Agent Navigation for Pursuit Problem
Hoang Huu Viet,Sang Hyeok An,TaeChoong Chung 한국지능시스템학회 2012 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.12 No.1
This paper presents a new approach to solve the pursuit problem based on a univector field method. In our proposed method, a set of eight agents works together instantaneously to find suitable moving directions and follow the univector field to pursue and capture a prey agent by surrounding it from eight directions in an infinite grid-world. In addition, a set of strategies is proposed to make the pursuit problem more realistic in the real world environment. This is a general approach, and it can be extended for an environment that contains static or moving obstacles. Experimental results show that our proposed algorithm is effective for the pursuit problem.
An online complete coverage approach for a team of robots in unknown environments
Hoang Huu Viet,SeungYoon Choi,TaeChoong Chung 제어로봇시스템학회 2013 제어로봇시스템학회 국제학술대회 논문집 Vol.2013 No.10
This paper presents a novel approach to deal with the online complete coverage problem for a team of robots in unknown environments. In our approach each robot covers an unvisited region using a single boustrophedon motion until a robot reaches an ending point, which is surrounded by covered positions or obstacles. At the ending point the robot detects backtracking points based on the accumulated knowledge, plans the shortest backtracking path to the next starting point based on the proposed Theta<SUP>*</SUP> with multi-goals. Then, it follows the planed path to the next starting point to cover the next unvisited region. The robot team finishes the coverage task when no backtracking point is detected. Computer simulations show that our proposed approach is efficient for the complete coverage task of a robot team in terms of the coverage rate and the coverage path length.
Univector Field Method based Multi-Agent Navigation for Pursuit Problem
Viet, Hoang Huu,An, Sang-Hyeok,Chung, Tae-Choong Korean Institute of Intelligent Systems 2012 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.12 No.1
This paper presents a new approach to solve the pursuit problem based on a univector field method. In our proposed method, a set of eight agents works together instantaneously to find suitable moving directions and follow the univector field to pursue and capture a prey agent by surrounding it from eight directions in an infinite grid-world. In addition, a set of strategies is proposed to make the pursuit problem more realistic in the real world environment. This is a general approach, and it can be extended for an environment that contains static or moving obstacles. Experimental results show that our proposed algorithm is effective for the pursuit problem.
A New Bearing-Only Navigation Law
Minh Hoang Trinh,Viet Hoang Pham,Phuong Huu Hoang,Jin-Hee Son,Hyo-Sung Ahn 제어로봇시스템학회 2017 제어로봇시스템학회 국제학술대회 논문집 Vol.2017 No.10
This paper proposes a new bearing-only control law which can be used for autonomous navigation task. We firstly compare the proposed control law with a commonly used control law in the literature when the agent navigates with only one beacon. Second, we prove that if there are two or more beacons, the agent can almost globally asymptotically reaches the desired position under the proposed control law. Third, the proposed control law is then applied to a cooperative bearing-only navigation problem. Simulation result is given to support the mathematical analysis.
Unsupervised Outpatients Clustering: A Case Study in Avissawella Base Hospital, Sri Lanka
Hoang, Huu-Trung,Pham, Quoc-Viet,Kim, Jung Eon,Kim, Hoon,Park, Junseok,Hwang, Won-Joo Korea Multimedia Society 2019 멀티미디어학회논문지 Vol.22 No.4
Nowadays, Electronic Medical Record (EMR) has just implemented at few hospitals for Outpatient Department (OPD). OPD is the diversified data, it includes demographic and diseases of patient, so it need to be clustered in order to explore the hidden rules and the relationship of data types of patient's information. In this paper, we propose a novel approach for unsupervised clustering of patient's demographic and diseases in OPD. Firstly, we collect data from a hospital at OPD. Then, we preprocess and transform data by using powerful techniques such as standardization, label encoder, and categorical encoder. After obtaining transformed data, we use some strong experiments, techniques, and evaluation to select the best number of clusters and best clustering algorithm. In addition, we use some tests and measurements to analyze and evaluate cluster tendency, models, and algorithms. Finally, we obtain the results to analyze and discover new knowledge, meanings, and rules. Clusters that are found out in this research provide knowledge to medical managers and doctors. From these information, they can improve the patient management methods, patient arrangement methods, and doctor's ability. In addition, it is a reference for medical data scientist to mine OPD dataset.
Offsetting obstacles of any shape for robot motion planning
Laskar, Md Nasir Uddin,Viet, Hoang Huu,Choi, Seung Yoon,Ahmed, Ishtiaq,Lee, Sungyoung,Chung, Tae Choong Cambridge University Press 2015 Robotica Vol.33 No.4
<B>SUMMARY</B><P>We present an algorithm for offsetting the workspace obstacles of a circular robot. Our method has two major steps: It finds the raw offset curve for both lines and circular arcs, and then removes the global invalid loops to find the final offset. To generate the raw offset curve and remove global invalid loops, <I>O(n)</I> and <I>O((n+k)</I>log <I>m</I>) computational times are needed respectively, where <I>n</I> is the number of vertices in the original polygon, <I>k</I> is the number of self-intersections and <I>m</I> is the number of segments in the raw offset curve, where <I>m</I> ≤ <I>n</I>. Any local invalid loops are removed before generating the raw offset curve by invoking a pair-wise intersection detection test (PIDT). In the PIDT, two intersecting entities are checked immediately after they are computed, and if the test is positive, portions of the intersecting segments are removed. Our method works for conventional polygons as well as the polygons that contain circular arcs. Our algorithm is simple and very fast, as each sub-process of the algorithm can be completed in linear time except the last one, which is nearly linear. Therefore, the overall complexity of the algorithm is nearly linear. By applying our simple and efficient approach, offsetting obstacles of any shape make it possible to construct a configuration space that ensures optimized motion planning.</P>