• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 22-24 Oct. 1991
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• pp.1939-1943
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• 1991

Path planning is an important task for optimal motion of a robot in structured or unstructured environment. The goal of this paper is to plan the shortest collision-free path in 3D, when a robot is navigated to pick up some tools or to repair some parts from various locations. To accomplish the goal of this paper, the Path Coordinator is proposed to have the capabilities of an obstacle avoidance strategy[3] and a traveling salesman problem strategy(TSP)[23]. The obstacle avoidance strategy is to plan the shortest collision-free path between each pair of n locations in 2D or in 3D. The TSP strategy is to compute a minimal system cost of a tour that is defined as a closed path navigating each location exactly once. The TSP strategy can be implemented by the Neural Network. The obstacle avoidance strategy in 2D can be implemented by the VGraph Algorithm. However, the VGraph Algorithm is not useful in 3D, because it can't compute the global optimality in 3D. Thus, the Path Coordinator is proposed to solve this problem, having the capabilities of selecting the optimal edges by the modified Genetic Algorithm[21] and computing the optimal nodes along the optimal edges by the Recursive Compensation Algorithm[5].

• 한국정보과학회논문지:소프트웨어및응용
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• 제34권3호
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• pp.235-245
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• 2007

무인수중로봇은 인간의 직접적인 접근이 제한되는 위험한 지역을 운항하기 때문에 인식, 결정, 그리고 행동과 같은 영역전문가의 고유능력을 수행하는 지능형 제어소프트웨어를 반드시 탑재해야한다. 본 논문에서는 다양한 무인항체에 적용 가능한 RVC 지능시스템 모델을 제안하며, 또한 충돌회피시스템, 항해 계획시스템, 그리고 충돌위험도산출시스템으로 구성된 무인수중로봇을 위한 지능형 자율운항시스템을 개발 한다. 충돌회피시스템에서는 퍼지관계곱에 기반한 장애물회피 알고리즘을 제안하는데 이는 생성경로 관점의 안전성과 효율성을 보장한다. 그리고 항해계획시스템에서는 폴리선을 이용한 항로계획 알고리즘을 제안 한다. 제안된 지능형 자율운항시스템의 성능검증을 위해 환경관리자, 객체, 그리고 3차원뷰어로 구성된 시뮬레이션시스템을 개발하여 시뮬레이션을 수행한다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 26-27 Oct. 1990
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• pp.1004-1008
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• 1990

This research focuses on developing a new and computationally efficient algorithm for free space structuring and planning collision free paths for an autonomous mobile robot working in an environment populated with polygonal obstacles. The algorithm constructs the available free space between obstacles in terms of free convex area. A collision free path can be efficiently generated based on a graph constructed using the midpoints of common free links between free convex area as passing points. These points correspond to nodes in a graph and the connection between them within each convex area as arcs in this graph. The complexity of the search for collision free path is greatly reduced by minimizing the size of the graph to be searched concerning the number of nodes and the number of arcs connecting them. The analysis of the proposed algorithm shows its efficiency in terms of computation ability, safety and optimality.

• 제어로봇시스템학회논문지
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• 제16권8호
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• pp.794-798
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• 2010

This paper presents a new obstacle-avoidance method for mobile robots. This approach, called the FEB (Fast Elastic Band) method, has been developed and successfully tested on the experimental mobile robot PHOPE-1S. The FEB method eliminates the shortcomings of the elastic band method previously introduced, yet retains all the advantages of its predecessor. The FEB algorithm is computationally efficient, and it allows continuous and fast motion of the mobile robot without stopping for obstacles. The FEB-controlled mobile robot traverses very densely cluttered obstacle courses and is able to pass through narrow openings or narrow corridors without oscillations. The results of the simulation and experiment have verified the validity of the proposed method.

• ETRI Journal
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• 제45권5호
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• pp.746-757
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• 2023

This study presents a novel safe landing algorithm for urban drone deliveries. The rapid advancement of drone technology has given rise to various delivery services for everyday necessities and emergency relief efforts. However, the reliability of drone delivery technology is still insufficient for application in urban environments. The proposed approach uses the "landing angle control" method to allow the drone to land vertically and a rapidly exploring random tree-based collision avoidance algorithm to generate safe and efficient vertical landing paths for drones while avoiding common urban obstacles like trees, street lights, utility poles, and wires; these methods allow for precise and reliable urban drone delivery. We verified the approach within a Gazebo simulation operated through ROS using a six-degree-of-freedom drone model and sensors with similar specifications to actual models. The performance of the algorithms was tested in various scenarios by comparing it with that of stateof-the-art 3D path planning algorithms.

• 한국정밀공학회지
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• 제16권12호
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• pp.182-189
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• 1999

In this paper, we present an ultrasonic sensor based path planning method using fuzzy logic for obstacle avoidance of an intelligent vehicle in unknown environments. Generally, Robot navigation in unknown terrains is a very complex task difficult to control because of the great amount of imprecise and ambiguous sensor information that has to be considered. In this case, fuzzy logic can satisfactorily deal with such information in quite efficient manner. In this study, we propose two fuzzy logic controller which is composed of steering controller and velocity controller respectively. Our object is to develop a fuzzy controller that can enable a mobile robot to navigate from a start point to a goal point without collisions, in the least possible travel time. The ability and effectiveness for the proposed algorithm will be demonstrated by simulation and expeiment.

• 전자공학회논문지B
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• 제33B권12호
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• pp.1-12
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• 1996

A new local path planning algorithm using DPH (distance profile histogram) is suggested in this paper. The proposed method makes a grid type world map using distance values from multiple ultrasonic sensors and genrates local points through which the mobile robot can avoid obstcles safely. The DPH (distance profile historgram) represents geometrical arrangement of obstacles around the robot in the local polar coordinate system which is assumed to be atached to the robot. To control robot's navigation, a three-layered control structure is adopted. The proposed local path planning algorithm is placed on the top level. And a point-to-point translation controller takes the middle level. The bottom level consists of a velcoity servo and sonar driver modules which take charge of driving physical hardwares. The validity of the propsoed method is demonstated through several experiments.

• 대한임베디드공학회논문지
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• 제13권2호
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• pp.53-63
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• 2018

A novel real-time path planning algorithm suitable for implementation on a small mobile robot is introduced. The algorithm can be used as the basis for mapping unknown or partially known environments and is tested in a specially developed simulation environment in Matlab(R). Simulations results are presented demonstrating that the algorithm can readily be implemented to allow a small robot to navigate in various unknown and partially known environments. The main characteristics of the algorithm include simplicity, ease of implementation, speed, and efficiency, thereby being especially suitable for small robots. Furthermore, for partially known environments, another algorithm is proposed to predefine an optimal path taking into account information provided regarding the environment.

• Journal of Mechanical Science and Technology
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• 제20권7호
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• pp.917-928
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• 2006

In recent decades, Artificial Neural Networks (ANNs) have become the focus of considerable attention in many disciplines, including robot control, where they can be used to solve nonlinear control problems. One of these ANNs applications is that of the inverse kinematic problem, which is important in robot path planning. In this paper, a neural network is employed to analyse of inverse kinematics of PUMA 560 type robot. The neural network is designed to find exact kinematics of the robot. The neural network is a feedforward neural network (FNN). The FNN is trained with different types of learning algorithm for designing exact inverse model of the robot. The Unimation PUMA 560 is a robot with six degrees of freedom and rotational joints. Inverse neural network model of the robot is trained with different learning algorithms for finding exact model of the robot. From the simulation results, the proposed neural network has superior performance for modelling complex robot's kinematics.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.30.6-30
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• 2001

We build near minimum-time trajectory planning algorithm for Wheeled mobile robots (WMRs) With Piece-Wise Constant control voltages satisfying i) initial and final postures and velocities as well as ii) voltage constraints We consider trajectory planning problem for cornering motion with a path-deviation requirement for obstacle avoidance. We divide our trajectory planning algorithm for cornering motion into five ordered sections: translational, transient, rotational, transient, and translational sections. Transforming dynamics into uncorrelated form with respect to translational and rotational velocities, we can make controls for translation/rotational velocities to be independent. By planning each section with constant voltages, and integrating five sections with adjustment of numbers of steps, the overall trajectory is planned. The performance is very close to the minimum-time solution, which is validated via simulation studies.