• 로봇학회논문지
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• 제18권4호
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• pp.487-495
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• 2023

This paper presents a method for autonomous exploration of indoor environments using a 2-dimensional Light Detection And Ranging (LiDAR) scanner. The proposed frontier-based exploration method considers navigability from the current robot position to extracted frontier targets. An approach to constructing the point cloud grid map that accurately reflects the occupancy probability of glass obstacles is proposed, enabling identification of safe frontier grids on the safety grid map calculated from the point cloud grid map. Navigability, indicating whether the robot can successfully navigate to each frontier target, is calculated by applying the skeletonization-informed rapidly exploring random tree algorithm to the safety grid map. While conventional exploration approaches have focused on frontier detection and target position/direction decision, the proposed method discusses a safe navigation approach for the overall exploration process until the completion of mapping. Real-world experiments have been conducted to verify that the proposed method leads the robot to avoid glass obstacles and safely navigate the entire environment, constructing the point cloud map and calculating the navigability with low computing time deviation.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제5권12호
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• pp.653-662
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• 2016

본 논문에서는 경로탐색 분야에서 많이 사용되는 RRT 알고리즘을 기반으로 한 테스트 케이스 생성 알고리즘을 제안한다. RRT 알고리즘 성능에 영향을 주는 가장 중요한 요소는 RRT 공간 내 노드 사이의 거리를 계산하는 거리 함수이다. Simulink/Stateflow (SL/SF) 모델의 테스트 케이스는 모델의 특정 상태에서 특정한 조건(본 논문에서 테스트 타겟이라 명명함)을 검사하기 위해 필요한 입력 시퀀스이기 때문에, 특정 조건을 검사하기 위해서는 먼저 모델을 특정 상태로 이끌어가는 것이 필요하다. 여기서 모델의 상태는 RRT의 노드로 표현된다. 일반적으로 어느 한 상태의 경우 다수의 조건을 검사할 필요가 있다. 예를 들어, 모델의 특정 상태가 다수의 전이가 발생 가능한 SL/SF model의 한 상태로 표현될 때, 전이 커버리지를 측정하기 위해서는 반드시 다수의 조건을 모두 검사해야 한다. 본 논문에서는 테스트 타겟들이 키 노드라 불리는 SL/SF 상태로 표현되는 특정 상태에서 다수 발견되는 점에 착안해서 만든 거리 계산 함수를 제안한다. 제안된 거리 함수는 키 노드가 아닌 노드에 페널티를 부과해서 RRT가 키 노드로부터 확장될 확률을 증가시킨다. 본 논문에서는 제안된 거리 함수를 이용한 테스트 케이스 생성 알고리즘을 제안한다. 성능 평가를 위해 상업용 자동차에 들어가는 3가지 전자제어장치 모델이 사용된다. 제안된 테스트 케이스 생성 알고리즘의 성능은 페널티 측면에서 평가되고 기존의 RRT 알고리즘을 사용한 테스트 케이스 생성 알고리즘의 성능과 비교한다.

• 한국항공운항학회지
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• 제27권2호
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• pp.1-8
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• 2019

Optimal path planning refers to find the safe route to the destination at a low cost, is a major problem with regard to autonomous navigation. Sampling Based Planning(SBP) approaches, such as Rapidly-exploring Random Tree Star($RRT^*$), are the most influential algorithm in path planning due to their relatively small calculations and scalability to high-dimensional problems. $RRT^*$-Smart introduced path optimization and biased sampling techniques into $RRT^*$ to increase convergent rate. This paper presents an improvement plan that has changed the biased sampling method to increase the initial convergent rate of the $RRT^*$-Smart, which is specified as m$RRT^*$-Smart. With comparison among $RRT^*$, $RRT^*$-Smart and m$RRT^*$-Smart in 2 & 3-D environments, m$RRT^*$-Smart showed similar or increased initial convergent rate than $RRT^*$ and $RRT^*$-Smart.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제2권7호
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• pp.471-478
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• 2013

본 논문은 Rapidly-exploring Random Tree(RRT) 알고리즘을 이용한 Simulink/Stateflow 모델 기반의 블랙박스 테스트 케이스 자동 생성 기법을 제안한다. RRT는 복잡한 시스템의 경로 계획을 효율적으로 해결하는 좋은 방법으로 널리 사용되고 있다. 본 논문에서 제안하는 기법은 블랙박스 테스트 케이스 생성 시 해결해야 되는 도달 가능 문제를 RRT를 통해 해결하고자 한다. RRT를 이용하여 테스트 케이스를 생성 할 때의 가장 큰 단점은 Stateflow 모델의 내부 상태가 복잡한 시스템을 위한 RRT 확장 시 시간과 메모리 측면에서 많은 비용이 발생하게 된다는 점이다. 일반적인 RRT 기법이 대상 시스템을 단일한 RRT 공간으로 구성 하는 반면 제안된 기법에서는 대상 시스템을 Stateflow의 상태를 기준으로 동적 분할하여 RRT 공간을 모델링 구성 함으로써 RRT 확장 시 필요한 비용을 감소시켰다. 본 논문에서는 분할 RRT 공간을 위한 RRT 공간의 정의와, 거리 측정 기법, 테스트 케이스 생성 알고리즘을 제시한다. 또한, 예제 Stateflow 모델을 기반으로 한 테스트 케이스 생성실험을 통해 제안된 알고리즘의 성능을 보인다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제7권2호
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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 $MatLab^{TM}\;and\;RecurDyn^{TM}$.

• 제어로봇시스템학회논문지
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• 제22권3호
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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.

• 한국군사과학기술학회지
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• 제20권6호
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• pp.803-812
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• 2017

This paper proposes a local path planning method for stable autonomous driving in rough terrain. There are various path planning techniques such as candidate paths, star algorithm, and Rapidly-exploring Random Tree algorithms. However, such existing path planning has limitations to reflecting the stability of unmanned ground vehicles. This paper suggest a path planning algorithm that considering the stability of unmanned ground vehicles. The algorithm is based on the genetic algorithm and assumes to have probability based obstacle map and elevation map. The simulation result show that the proposed algorithm can be used for real-time local path planning in rough terrain.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2019년도 춘계학술발표대회
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• pp.76-78
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• 2019

Sampling-based algorithms are one of the most commonly approaches which give good results in robot path planning with many degree of freedom. So that many proposed methods as well as their improvement based on these approaches have been proposed. The purpose of this paper is to survey some current algorithms using for path planning, the original proposed methods as well as their improvement. Some advantages and disadvantages of these algorithms will be also mentioned, how the improved version of the proposed methods overcome the original proposed methods' drawback.

• 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.

• 로봇학회논문지
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• 제16권3호
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• pp.250-259
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• 2021

Path planning is one of the important technologies for automated parking. It requires to plan a collision-free path considering the vehicle's kinematic constraints such as minimum turning radius or steering velocity. In a complex parking lot, Rapidly-exploring Random Tree^* (RRT^*) can be used for planning a parking path, and Reeds-Shepp or Hybrid Curvature can be applied as a tree-extension method to consider the vehicle's constraints. In this case, each of these methods may affect the computation time of planning the parking path, path-tracking error, and parking success rate. Therefore, in this study, we conduct comparative analysis of two tree-extension functions: Reeds-Shepp (RS) and Hybrid Curvature (HC), and show that HC is a more appropriate tree-extension function for parking path planning. The differences between the two functions are introduced, and their performances are compared by applying them with RRT^*. They are tested at various parking scenarios in simulation, and their advantages and disadvantages are discussed by computation time, cross-track error while tracking the path, parking success rate, and alignment error at the target parking spot. These results show that HC generates the parking path that an autonomous vehicle can track without collisions and HC allows the vehicle to park with lower alignment error than those of RS.