• Journal of Aerospace System Engineering
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• v.13 no.3
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• pp.15-22
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• 2019

In this paper, a global and local flight path system for autonomous flight of the UAV is proposed. The overall system is based on the ROS robot operating system. The UAV in-built computer detects obstacles through 2-D Lidar and generates real-time local path and global path based on VFH and Modified $RRT^*$-Smart, respectively. Additionally, a movement command is issued based on the generated path on the UAV flight controller. The ground station computer receives the obstacle information and generates a 2-D SLAM map, transmits the destination point to the embedded computer, and manages the state of the UAV. The autonomous UAV flight system of the is verified through a simulator and actual flight.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.05a
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• pp.79-80
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• 2023

본 연구에서는 자율운항선박이 복잡한 항계 내에서 다양한 해상 객체와의 충돌을 회피하기 위하여 계층적 경로 생성 기법을 연계하는 프레임워크를 제안한다. 항계 내에는 항로를 항행하는 선박 외에도 정박 영역 내 정박 또는 묘박 중인 선박뿐만 아니라 항로 표지나 부표와 같은 정적 객체들이 다양하게 분포되어 있다. 자율운항선박의 효율적 운항을 위해서는 운항 중에 조우하게 되는 객체의 정적/동적 속성에 따라 경로 생성 기법이 달리 적용되어야 한다. 본 연구에서 제안한 경로 생성 프레임워크는 항계 내의 정적 객체나 항행 가항 영역 및 항행 불가항 영역 등에 대한 위치 정보들은 사전적 정보로 활용 가능하므로, 샘플링 기반의 전역 경로 생성 기법을 적용하여, 초기 출발지에서 최종 목적지까지의 예상 경로를 생성한다. 그리고 생성된 전역 경로를 추종하며 운항하는 과정에서 조우하게 되는 동적 객체들과의 조우 상황별 국제해상 충돌예방규칙(COLREGs)을 고려한 지역 경로를 생성한다. 샘플링 기반의 전역 경로와 국소 영역에서의 충돌 회피를 위한 지역 경로를 연계하기 위한 계층적 경로 생성 프레임워크를 설계하고, 수치 시뮬레이션을 통해 제안한 프레임워크의 유용성을 검증하였다.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.9
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• pp.369-374
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• 2014

Path generation methods are required for safe and efficient driving in unmanned autonomous vehicles. There are two kinds of paths: global and local. A global path consists of all the way points including the source and the destination. A local path is the trajectory that a vehicle needs to follow from a way point to the next in the global path. In this paper, we propose a novel method for local path generation through machine learning, with an effective curve function used for initializing the trajectory. First, reinforcement learning is applied to a set of candidate paths to produce the best trajectory with maximal reward. Then the optimal steering angle with respect to the trajectory is determined by training an artificial neural network. Our method outperformed existing approaches and successfully found quality paths in various experimental settings, including the cases with obstacles.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.687-693
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• 2012

In this paper, we propose a optimal deployment method for cooperative exploration of swarm robots. The proposed method consists of two parts such as optimal deployment and path planning. The optimal area deployment is proposed by the K-mean Algorithm and Voronoi tessellation. The path planning is proposed by the potential field method and A* Algorithm. Finally, the numerical experiments demonstrate the effectiveness and feasibility of the proposed method.

• Journal of KIISE
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• v.41 no.12
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• pp.1145-1151
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• 2014

In this paper, we propose MPLO(Multi-Path routing with Local Optimization) for load balancing in the tactical backbone network. The MPLO manages global metric and local metric separately. The global metric is propagated to other routers via a routing protocol and is used for configuring loop-free multi-path. The local metric reflecting link utilization is used to find an alternate path when congestion occurs. We verified MPLO could effectively distribute user traffic among available routers by simulation.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06b
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• pp.486-488
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• 2012

본 논문에서는 로보틱스나 컴퓨터 게임 등의 다양한 분야에서 일반적으로 존재하는 동적인 환경에서 장애물 회피를 위한 경로 계획 알고리즘을 연구하였다. 제안하는 알고리즘은 전역 경로계획과 지역 경로계획의 두 단계로 이루어져 있고, A*와 가시성 그래프에 의해 구해진 전역 경로를 이동할 때에 동적 장애물을 감지하게 되면 이를 회피하기 위해 포텐샬장으로 지역 경로를 구성한다. 이 알고리즘은 포텐샬장을 A*와 결합함으로써 지역 최적화 문제를 해결하고 포텐샬장만으로 이동하는 경우에 비해 시간적인 측면에서도 더욱 효율적이다.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1896-1897
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• 2011

본 논문에서는 동적환경의 잠재적 위험 정의와 안전한 전역경로 계획 알고리즘을 제안 하였다. 대부분의 전역경로계획 연구는 시간과 거리의 비용을 최적화 시키는데 집중하고 있다. 하지만 동적환경으로 우리 주변에 많은 위험요소가 작용하고 있다. 본 논문에서 안전한 자율주행을 위한 경로계획방법으로 위험지역에 의해 정의 된 반발력과 S*알고리즘을 이용하여 안전하고 빠른 최적의 경로계획을 이루었다.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.2
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• pp.268-275
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• 2011

This paper proposes two optimal path planning methods of a mobile robot using uDEAS (univariate Dynamic Encoding Algorithm for Searches). Before start of autonomous traveling, a self-controlled mobile robot must generate an optimal global path as soon as possible. To this end, numerical optimization method is applied to real time path generation of a mobile robot with an obstacle avoidance scheme and the basic path generation method based on the concept of knot and node points between start and goal points. The first improvement in the present work is to generate diagonal paths using three node points in the basic path. The second innovation is to make a smooth path plotted with the blending polynomial using uDEAS. Effectiveness of the proposed schemes are validated for several environments through simulation.

• Journal of the Korea Society of Computer and Information
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• v.18 no.1
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• pp.11-19
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• 2013

For an autonomous multi-mobile robot system, path planning and collision avoidance are important functions used to perform a given task collaboratively and cooperatively. This study considers these important and challenging problems. The proposed approach is based on a potential field method and fuzzy logic system. First, a global path planner selects the paths of the robots that minimize the cost function from each robot to its own target using a potential field. Then, a local path planner modifies the path and orientation from the global planner to avoid collisions with static and dynamic obstacles using a fuzzy logic system. In this paper, each robot independently selects its destination and considers other robots as dynamic obstacles, and there is no need to predict the motion of obstacles. This process continues until the corresponding target of each robot is found. To test this method, an autonomous multi-mobile robot simulator (AMMRS) is developed, and both simulation-based and experimental results are given. The results show that the path planning and collision avoidance strategies are effective and useful for multi-mobile robot systems.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.4
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• pp.27-35
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• 1998

Navigation is a method to direct a mobile robot without collision when traversing the environment. This is to reach a destination without getting lost. In this paper, global and local path planning in fixed obstacle and moving obstacle using genetic algorithm are presented. First, mobile robot searches optimal global path using genetic algorithm without falling into local minima. Then if it finds a unknown obstacle, it searches new path without crashing obstacle. Also if there is a moving obstacle, mobile robot searches new optimal path without colliding with the obstacles. Various simulation results show the proposed algorithm can search a shortest path effectively.