Dynamic Voronoi Diagram for Moving Disks

et al. 2022

Soft Comput

The trajectory planning for UAVs in the dynamic environments is a challenging task. Many restrictions should be taken into consideration, including dynamic terrain collision, no-fly zone criteria, power and fuel criteria and so on. However, some methods treat dynamic restrictions as static in order to reduce cost and obtain efficient and acceptable paths. To achieve optimal, efficient and acceptable paths, we first use a high-dimension matrix, Extended Hierarchical Graph (EHG), to model the unexplored dynamic grid environment and to convert weather conditions to passable coefficient. The original shortest path planning task is then translated to plan the safest path. Therefore, we propose a new forward-exploration and backward-navigation algorithm, Matrix Alignment Dijkstra (MAD), to pilot UAVs. We use matrix alignment operation to simulate the parallel exploration process of all cells from time t to t+1. This exploration process can be accelerated on a GPU. Finally, we recall the optimal path according to the navigator matrix. In addition, we can achieve UAV path planning from one source cell to multiple target cells within one single run. We validate our method on a real dynamic weather dataset and get competitive results in both efficiency and accuracy. We analyse the performance of MAD on a grid-based benchmark dataset and artificial maze data. Simulated results show MAD is especially helpful when the grid-based environment is large-scale and dynamic.

Section: Related Workmentioning

confidence: 99%

Trajectory planning for UAV navigation in dynamic environments with matrix alignment Dijkstra

et al. 2022

Soft Comput

“…e Voronoi diagram was proposed with the famous structure of computational geometry. It is widely used in many fields such as geometry, architecture, and geography [35][36][37][38]. Voronoi diagrams can partition data into set spaces and are effective in the study of local neighborhoods for each partition [39].…”

Section: Related Workmentioning

confidence: 99%

Partition Selection for Large-Scale Data Management Using KNN Join Processing

Mathematical Problems in Engineering

et al. 2020

For the data processing with increasing avalanche under large datasets, the k nearest neighbors (KNN) algorithm is a particularly expensive operation for both classification and regression predictive problems. To predict the values of new data points, it can calculate the feature similarity between each object in the test dataset and each object in the training dataset. However, due to expensive computational cost, the single computer is out of work to deal with large-scale dataset. In this paper, we propose an adaptive vKNN algorithm, which adopts on the Voronoi diagram under the MapReduce parallel framework and makes full use of the advantages of parallel computing in processing large-scale data. In the process of partition selection, we design a new predictive strategy for sample point to find the optimal relevant partition. Then, we can effectively collect irrelevant data, reduce KNN join computation, and improve the operation efficiency. Finally, we use a large number of 54-dimensional datasets to conduct a large number of experiments on the cluster. The experimental results show that our proposed method is effective and scalable with ensuring accuracy.

“…Wenzheng Chi et al [9] have presented a generalized-Voronoi-diagram (GVD)-based heuristic pathplanning algorithm to generate a heuristic path, guide the sampling process of RRTs, and further improve the motion-planning efficiency of RRTs. To solve the problems in dynamic environments, Chanyoung Song et al [10] have presented a data structure and algorithm to construct a Voronoi diagram composed of moving circular disks (dynamic Voronoi diagram of moving disks) in the plane.…”

Section: Introductionmentioning

confidence: 99%

A Stealth–Distance Dynamic Weight Deep Q-Network Algorithm for Three-Dimensional Path Planning of Unmanned Aerial Helicopter

Huang

2023

Aerospace

Unmanned aerial helicopters (UAHs) have been widely used recently for reconnaissance operations and other risky missions. Meanwhile, the threats to UAHs have been becoming more and more serious, mainly from radar and flights. It is essential for a UAH to select a safe flight path, as well as proper flying attitudes, to evade detection operations, and the stealth abilities of the UAH can be helpful for this. In this paper, a stealth–distance dynamic weight Deep Q-Network (SDDW-DQN) algorithm is proposed for path planning in a UAH. Additionally, the dynamic weight is applied in the reward function, which can reflect the priorities of target distance and stealth in different flight states. For the path-planning simulation, the dynamic model of UAHs and the guidance model of flight are put forward, and the stealth model of UAHs, including the radar cross-section (RCS) and the infrared radiation (IR) intensity of UAHs, is established. The simulation results show that the SDDW-DQN algorithm can be helpful in the evasion by UAHs of radar detection and flight operations, and the dynamic weight can contribute to better path-planning results.