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.