This study focuses on the path planning algorithm for large-scale autonomous delivery using drones and robots in urban environments. When generating delivery routes in urban environments, it is essential that avoid obstacles such as buildings, parking lots, or any other obstacles that could cause property damage. A commonly used method for obstacle avoidance is the grid-based A* algorithm. However, in large-scale urban environments, it is not feasible to set the resolution of the grid too high. If the grid cells are not sufficiently small during path planning, inefficient paths might be generated when avoiding obstacles, and smaller obstacles might be overlooked. To solve these issues, this study proposes a method that initially creates a low-resolution wide-area grid and then progressively reduces the grid cell size in areas containing registered obstacles to maintain real-time efficiency in generating paths. To implement this, obstacles in the operational area must first be registered on the map. When obstacle information is updated, the cells containing obstacles are processed as a primary subdivision, and cells closer to the obstacles are processed as a secondary subdivision. This approach is validated in a simulation environment and compared with the previous research according to the computing time and the path distance.

Ship collision accidents not only endanger the safety of ships and personnel, but also may cause serious marine environmental pollution. To solve this problem, advanced technologies have been developed and applied in the field of intelligent ships in recent years. In this paper, a novel path planning algorithm is proposed based on particle swarm optimization (PSO) to construct a decision-making system for ship's autonomous collision avoidance using the process analysis which combines with the ship encounter situation and the decision-making method based on ship collision avoidance responsibility. This algorithm is designed to avoid both static and dynamic obstacles by judging the collision risk considering bad weather conditions by using BP neural network. When the two ships enter a certain distance, the optimal collision avoidance course and speed of the ship are obtained through the improved collision avoidance decision-making method. Finally, through MATLAB and Visual C++ platform simulations, the results show that the ship collision avoidance decision-making scheme can obtain reasonable optimal collision avoidance speed and course, which can ensure the safety of ship path planning and reduce energy consumption.

보다 사실적인 대규모 게임 환경을 구축하기 위해서는 NPC(Non-Player Character)의 지능적인 경로 계획 기법이 필수적이다. 본 논문에서는 필드 기반 경로 계획 기법의 하나로 지금까지 기하 모델링에 사용되어 왔던 적분형 MLS(Moving Least Squares) 기법의 적용을 제안한다. 이 기법은 다른 필드 방식(부호 거리장 기법, SDF)에 비해서 모든 지점에서 연속, 미분 가능(C1)한 부드러운 경로를 제공하며 간단한 매개변수 하나만으로 지형 장애물과의 상대적인 거리에 따른 자연스러운 동선을 형성할 수 있다. 적분형 MLS는 GPU 기반 병렬 기법과 2차원 및 3차원에서의 해석이 상당히 진행되었으며 비교적 어려운 3차원 공간 상의 경로 계획에도 적용할 수 있다.

An unmanned aerial vehicle (UAV) is a powered aerial vehicle that does not carry a human operator, uses aerodynamic forces to provide vehicle lift, can fly autonomously or be piloted remotely, can be expendable or recoverable, and can carry a lethal or no

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

This research is to select a path planning algorithm to maximize survivability for Unmanned Aerial Vehicle(UAV). An UAV is a powered pilotless aircraft, which is controlled remotely or autonomously. UAVs are currently employed in many military missions(surveillance, reconnaissance, communication relay, targeting, strike etc.) and a number of civilian applications(communication service, broadcast service, traffic control support, monitoring, measurement etc.). In this research, a mathematical programming model is suggested by using MRPP(Most Reliable Path Problem) and verified by using ILOG CPLEX. A path planning algorithm for UAV is selected by comparing of SPP(Shortest Path Problem) algorithms which transfer MRPP into SPP.

RRT* (Rapidly exploring Random Tree*) based algorithms are widely used for path planning. Informed RRT* uses RRT* for generating an initial path and optimizes the path by limiting sampling regions to the area around the initial path. RRT* algorithms have several limitations such as slow convergence speed, large memory requirements, and difficulties in finding paths when narrow aisles or doors exist. In this paper, we propose an algorithm to deal with these problems. The proposed algorithm applies the image skeletonization to the gridmap image for generating an initial path. Because this initial path is close to the optimal cost path even in the complex environments, the cost can converge to the optimum more quickly in the proposed algorithm than in the conventional Informed RRT*. Also, we can reduce the number of nodes and memory requirement. The performance of the proposed algorithm is verified by comparison with the conventional Informed RRT* and Informed RRT* using initial path generated by A*.

Obstacle avoidance is one of the most important parts of autonomous mobile robot. In this study, we proposed safe and efficient local path planning of robot for obstacle avoidance. The proposed method detects and tracks obstacles using the 3D depth information of an RGB-D sensor for path prediction. Based on the tracked information of obstacles, the paths of the obstacles are predicted with probability circle-based spatial search (PCSS) method and Gaussian modeling is performed to reduce uncertainty and to create the cost function of caution. The possibility of collision with the robot is considered through the predicted path of the obstacles, and a local path is generated. This enables safe and efficient navigation of the robot. The results in various experiments show that the proposed method enables robots to navigate safely and effectively.

Recent studies on automatic parking have actively adopted the technology developed for mobile robots. Among them, the path planning scheme plans a route for a vehicle to reach a target parking position while satisfying the kinematic constraints of the vehicle. However, previous methods require a large amount of computation and/or cannot be easily applied to different environmental conditions. Therefore, there is a need for a path planning scheme that is fast, efficient, and versatile. In this study, we use a multi-dimensional path grid map to solve the above problem. This multi-dimensional path grid map contains a route which has taken a vehicle's kinematic constraints into account; it can be used with the A* algorithm to plan an efficient path. The proposed method was verified using Prescan which is a simulation program based on MATLAB. It is shown that the proposed scheme can successfully be applied to both parallel and vertical parking in an efficient manner.

본 논문은 재난 발생 감시 및 정찰을 위한 수상 로봇(USV, Unmanned Surface Vehicle)의 경로계획법을 다룬다. 수상에서 로봇을 운용하기 위해서는 수상 로봇의 자율적인 장애물 회피와 목적지 이동이 보장되어야한다. 본 연구에서 수상 로봇은 자율 주행을 위해 포텐셜 필드(Potential Field)를 사용한다. 포텐셜 필드는 인력과 척력의 합으로 구성된다. 포텐셜 필드는 간단한 수학적 모델로 만들 수 있고 시스템 적용에 용이하다. 하지만 기존의 포텐셜 필드를 이용한 연구는 전역좌표를 기반으로 하기 때문에 로봇이 목적지에 도달하지 못하는 지역 최소점 문제가 발생 할 수 있다. 지역 최소점은 척력 포텐셜 필드가 인력 포텐셜 필드에 영향을 미쳐서 로봇이 목적지에 도착하지 못하게 로봇의 이동을 방해한다. 본 논문에서는 이러한 점을 해결하기 위해 목적지와 주변의 장애물을 로봇을 중심으로 판단하는 지역 좌표계를 사용한다. 제안된 방법은 매트랩 시뮬레이션 환경에서 평가된다.

A* algorithm is a global path generation algorithm, and typically create a path using only the distance information. Therefore along the path, a moving vehicle is usually not be considered by driving characteristics. Deceleration at the corner is one of the driving characteristics of the vehicle. In this paper, considering this characteristic, a new evaluation function based path algorithm is proposed to decrease the number of driving path corner, in order to reduce the driving cost, such as driving time, fuel consumption and so on. Also the potential field method is applied for driving of UGV, which is robust against static and dynamic obstacle environment during following the generated path of the mobile robot under. The driving time and path following test was occurred by experiments based on a pseudo UGV, mobile robot in downscaled UGV’s maximum and driving speed in corner. The experiment results were confirmed that the driving time by the proposed algorithm was decreased comparing with the results from A* algorithm.

In this paper, we propose a cost-aware Rapidly-exploring Random Tree (RRT) path planning algorithm for mobile robots. A mobile robot is presented with a cost map of the field of interest and assigned to move from one location to another. As a robot moves, the robot is penalized by the cost at its current location according to the cost map. The overall cost of the robot is determined by the trajectory of the robot. The goal of the proposed cost-aware RRT algorithm is to find a trajectory with the minimal cost. The cost map of the field can represent environmental parameters, such as temperature, humidity, chemical concentration, wireless signal strength, and stealthiness. For example, if the cost map represents packet drop rates at different locations, the minimum cost path between two locations is the path with the best possible communication, which is desirable when a robot operates under the environment with weak wireless signals. The proposed cost-aware RRT algorithm extends the basic RRT algorithm by considering the cost map when extending a motion segment. We show that the proposed algorithm gives an outstanding performance compared to the basic RRT method. We also demonstrate that the use of rejection sampling can give better results through extensive simulation.

Many intelligent robots have to be given environmental information to perform tasks. In this paper an intelligent robot, that is, a cleaning robot used a sensor fusing method of two sensors: LRF and StarGazer, and then was able to obtain the information. Throughout wall following using laser displacement sensor, LRF, the working area is built during the robot turn one cycle around the area. After the process of wall following, a path planning which is able to execute the work effectively is established using flow network algorithm. This paper describes an algorithm for minimal turning complete coverage path planning for intelligent robots. This algorithm divides the whole working area by cellular decomposition, and then provides the path planning among the cells employing flow networks. It also provides specific path planning inside each cell guaranteeing the minimal turning of the robots. The proposed algorithm is applied to two different working areas, and verified that it is an optimal path planning method.

This paper presents a study of path-planning method for AGV(automated guided vehicle) based on path-tracking. It is important to find an optimized path among the AGV techniques. This is due to the fact that the AGV is conditioned to follow the predetermined path. Consequently, the path-planning method is implemented directly affects the whole AGV operation in terms of its performance efficiency. In many existing methods are used optimization algorithms to find optimized path. However, such methods are often prone with problems in handling the issue of inefficiency that exists in system's operation due to inherent undue time delay created by heavy load of complex computation. To solve such problems, we offer path-planning method using modified binary tree. For the purpose of our experiment, we initially designed a AGV that is equiped with laser navigation, two encoders, a gyro sensor that is meant to be operated within actual environment with given set of constrictions and layout for the AGV testing. The result of our study reflects the fact that within such environments, the proposed method showed improvement in its efficiency in finding optimized path.