• 한국전자통신학회논문지
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• 제18권5호
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• pp.981-988
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• 2023

무인 비행체의 활용범위가 확대됨에 따라 관련 기술의 발전과 기술 수요도 증가하는 추세이다. 무인 비행체의 운영빈도가 늘어나고 운영의 편리성이 강조됨에 따라 관련 자율비행 기술도 중요성이 주목받고 있다. 무인 비행체의 자율 비행에 있어 목적지에 도달하는 경로계획을 세우는 일은 유도제어에서 중요하며 무인화의 효과를 극대화하기 위해서는 경로계획 역시 자동으로 생성하는 기술이 필요하다. 본 논문에서는 무인 비행체의 자율운영 효과를 높이기 위해서 급속탐색랜덤트리기법으로 생성된 경로를 무인기의 특성에 맞게 최적화하는 기법에 관한 연구를 수행하였다. 최적 거리, 최단 시간, 임무점 통과 등의 지표를 달성하기 위해 경로계획을 무인 비행체의 임무 목표와 동적 특성을 고려하여 최적화하였다. 제안한 기법은 장애물 상황에 대한 성능검증을 통해 무인 비행체 경로계획 생성에 적용 가능성을 확인하였다.

• 산업경영시스템학회지
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• 제34권3호
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• pp.24-34
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• 2011

An Unmanned Aerial Vehicle (UAV) is a powered pilotless aircraft, which is controlled remotely or autonomously. UAVs are an attractive alternative for many scientific and military organizations. UAVs can perform operations that are considered to be risky or uninhabitable for human. UA V s are currently employed in many military missions such as reconnaissance, surveillance, enemy radar jamming, decoying, suppression of enemy air defense (SEAD), fixed and moving target attack, and air-to-air combat. UAVs also are employed in a number of civilian applications such as monitoring ozone depletion, inclement weather, traffic congestion, and taking images of dangerous territory. For accomplishing the UAV's missions, guarantee of survivability should be preceded. The main objective of this study is to suggest a mathematical programming model and a $A^*PS$-PGA (A-star with Post Smoothing-Parallel Genetic Algorithm) for an UAV's path planning to maximize survivability. A mathematical programming model is composed by using MRPP (Most Reliable Path Problem) and TSP (Traveling Salesman Problem). A path planning algorithm for UAV is applied by transforming MRPP into SPP (Shortest Path Problem).

• 제어로봇시스템학회논문지
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• 제5권5호
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• pp.593-599
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• 1999

A mobile robot in FMS environment should be able to nevigate itself. Therefore, path planning is necessary for the mobile robot to perform its tasks without being lost. Path planning using a network model gives oprimal paths to every pair of nodes but building this model demands accurate information of environments. In this paper, a method to build a network model using sonar sensors is presented. The main idea is to build a quad tree model by using sonar sensors and convert the model to a network model for path planning. The new method has been implemented on a mobile robot. Experimental results show that the mobile robot constructs an accurate network model using inaccurate sonar data.

• 제어로봇시스템학회논문지
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• 제2권3호
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• pp.200-208
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• 1996

This paper describes the mechanism, guidance, sensor system, and navigation algorithm of METRO, a free ranging mobile robot. METRO is designed for use in structured surroundings or factory environments rather than unstructured natural environments. An overview of the physical configuration of the mobile robot is presented as well as a description of its sensor system, an omnidirectional laser range finder. Except for the global path planning algorithm, a guidance and a navigation algorithm with a local path planning algorithm are used to navigate the mobile robot. In METRO the computer support is divided into a supervisor with image processing and local path planning and a slave with motor control. The free ranging mobile robot is self-controlled and all processing being performed on board.

• 한국군사과학기술학회지
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• 제14권3호
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• pp.327-334
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• 2011

It is necessary that UGV(Unmanned Ground Vehicle) should generate a real-time travesability index map by analyzing raw terrain information to travel autonomously tough terrain which has various slope and roughness values. In this paper, we propose a local path planning method, $MA^*$(Modified $A^*$) algorithm, using DVGM (Directional Velocity Grid Map) for unmanned ground vehicle. We also present a path optimization algorithm and a path smoothing algorithm which regenerate a pre-planned local path by $MA^*$ algorithm into the reasonable local path considering the mobility of UGV. Field test is conducted with UGV in order to verify the performance of local path planning method using DVGM. The local path planned by $MA^*$ is compared with the result of $A^*$ to verify the safety and optimality of proposed algorithm.

• Nuclear Engineering and Technology
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• 제51권3호
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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.

• 한국항행학회논문지
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• 제20권5호
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• pp.401-407
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• 2016

본 논문에서는 태양광 장기체공 무인기의 전력모델을 포함한 3차원 경로계획과 유도에 대하여 기술한다. 본 논문에서 사용한 Dubins curve는 계산속도가 빨라 경로계획에 곧바로 적용이 가능하다는 장점이 있다. 하지만 Dubins curve의 경로생성 문제는 2차원 평면에서 정의되기 때문에 실제 항공기의 경로계획을 위해 Randal W. Beard에 의해 수행된 비행 경로각의 한계를 고려하여, 고도 차이에 따라 선회경로를 추가하는 방식의 3차원 Dubins 경로생성 알고리즘을 활용하였다. 본 논문에서 사용한 항공기 모델은 Aileron이 없기 때문에 Rudder를 사용하여 횡축 방향 제어기를 설계하였으며, 비선형 경로추종 유도기법을 사용하여 경로추종 시뮬레이션을 수행하였다. 고도조건에 따른 예제를 생성하였으며, 시뮬레이션 결과 생성된 경로를 잘 추종하는 것을 확인하였다. 마지막으로 태양에너지 수율에 대한 계산식을 통해 태양광 장기체공 무인기의 전력 시스템을 모델링하여 48시간 연속비행 시뮬레이션을 실시하였고, 이에 대한 시뮬레이션 결과를 제시하였다.

• 제어로봇시스템학회논문지
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• 제10권8호
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• pp.711-717
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• 2004

We propose a path planning method to improve the productivity of SMT (surface mount technology) inspection machines with an area camera. A unified method is newly proposed to determine the FOV clusters and camera sequence simultaneously. The proposed method is implemented by a hybrid genetic algorithm to increase the convergence speed. Comparative simulation results are then presented to verify the usefulness of the proposed algorithm.

• 제어로봇시스템학회논문지
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• 제18권4호
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• pp.365-370
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• 2012

Most path planning algorithms for a marine robot in the ocean environment have been developed without considering the robot's heading angle. As a result, the robot has a difficulty in following the path correctly. In this paper, we propose a limit-cycle circle set that applies to the $Theta^*$ algorithm. The minimum turning radius of a marine robot is calculated using a limit-cycle circle set, and circles of this radius is used to generate a configuration space of an occupancy grid map. After applying $Theta^*$ to this configuration space, the limit-cycle circle set is also applied to the start and end nodes to find the appropriate path with specified heading angles. The benefit of this algorithm is its fast computation time compared to other 3-D ($x,y,{\theta}$) path planning algorithms, along with the fact that it can be applied to the 3-D kinematic state of the robot. We simulate the proposed algorithm and compare it with 3-D $A^*$ and 3-D $A^*$ with post smoothing algorithms.

• 제어로봇시스템학회논문지
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• 제12권5호
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• pp.473-479
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• 2006

A global path planning algorithm using modified self-organizing feature map(SOFM) which is a method among a number of neural network is presented. The SOFM uses a randomized small valued initial weight vectors, selects the neuron whose weight vector best matches input as the winning neuron, and trains the weight vectors such that neurons within the activity bubble are move toward the input vector. On the other hand, the modified method in this research uses a predetermined initial weight vectors of the 2-dimensional mesh, gives the systematic input vector whose position best matches obstacles, and trains the weight vectors such that neurons within the activity bubble are move toward the opposite direction of input vector. According to simulation results one can conclude that the modified neural network is useful tool for the global path planning problem of a mobile robot.