Smart Search System of Autonomous Flight UAVs for Disaster Rescue

To optically capture and analyze the structure and changes of the flow field of a weak airflow object with high accuracy, this study proposes novel weak flow field extraction methods based on background-oriented schlieren. First, a fine background pattern texture and a sensor network layout were designed to satisfy the requirement of weak flow field extraction. Second, the image displacement was extracted by calculating the correlation matrix in the frequency domain for a particle image velocimetry algorithm, and further calculations were performed for the density field using Poisson’s equation. Finally, the time series baseline stacking method was proposed to obtain the flow field changes of weak airflow structures. A combustion experiment was conducted to validate the feasibility and accuracy of the proposed method. The results of a quad-rotor unmanned aerial vehicle experiment showed that the clear, uneven, and continuous quantitative laminar flow field could be obtained directly, which overcame the interference of the weak airflow, large field of view, and asymmetrical steady flow.

“…By combining Equations (3) and (5), the relationship between the displacement ∆y of the background pattern and the refractive index n of the flow field can be obtained:…”

Section: Bos Technology Measurement Principlementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Extraction of a Weak Flow Field for a Multi-Rotor Unmanned Aerial Vehicle (UAV) Using High-Speed Background-Oriented Schlieren (BOS) Technology

Liu

Guo

Zhang

et al. 2021

“…Hassan et al [ 5 ] detect and count rice plants by using an unmanned aerial vehicle. Oh et al [ 6 ] proposed an autonomous UAV smart search system method to search for persons in disaster sites. However, positioning UAVs in the GNSS-denied environment is still a significant challenge.…”

Section: Introductionmentioning

confidence: 99%

UAV’s Status Is Worth Considering: A Fusion Representations Matching Method for Geo-Localization

Zhu

Yang

Yin

et al. 2023

Visual geo-localization plays a crucial role in positioning and navigation for unmanned aerial vehicles, whose goal is to match the same geographic target from different views. This is a challenging task due to the drastic variations in different viewpoints and appearances. Previous methods have been focused on mining features inside the images. However, they underestimated the influence of external elements and the interaction of various representations. Inspired by multimodal and bilinear pooling, we proposed a pioneering feature fusion network (MBF) to address these inherent differences between drone and satellite views. We observe that UAV’s status, such as flight height, leads to changes in the size of image field of view. In addition, local parts of the target scene act a role of importance in extracting discriminative features. Therefore, we present two approaches to exploit those priors. The first module is to add status information to network by transforming them into word embeddings. Note that they concatenate with image embeddings in Transformer block to learn status-aware features. Then, global and local part feature maps from the same viewpoint are correlated and reinforced by hierarchical bilinear pooling (HBP) to improve the robustness of feature representation. By the above approaches, we achieve more discriminative deep representations facilitating the geo-localization more effectively. Our experiments on existing benchmark datasets show significant performance boosting, reaching the new state-of-the-art result. Remarkably, the recall@1 accuracy achieves 89.05% in drone localization task and 93.15% in drone navigation task in University-1652, and shows strong robustness at different flight heights in the SUES-200 dataset.

“…Sudden catastrophic events, such as earthquakes, floods, terrorist attacks, etc., are always random, dynamic, destructive, and emergent. Unmanned aerial vehicles (UAVs) are flexible, low-cost, and high-efficient and can be applicable to emergency disaster relief [1][2][3][4][5][6]. In the pre-disaster stage, sensors installed on UAVs can predict the disaster's time, geographical location, and magnitude by cooperating with current wireless sensor networks [7].…”

Section: Introductionmentioning

confidence: 99%

“…However, there are still many challenges for multi-UAV emergency scheduling, which are as follows: (1) heterogeneous UAVs are required to complete multiple tasks, such as communication, supply delivery, mapping, and monitoring, because emergencies are often diverse and unpredicted; (2) Multiple UAVs should cooperate with each other to complete the complicated tasks; (3) Dynamic scheduling should be real-time to guarantee the successful accomplishment of tasks; (4) Energy efficiency should be taken into consideration, such as energy consumption of sensors in UAV-enabled wireless sensor networks [11,12] or fuel consumption of UAVs during the flight. Note that in this paper, we generate the short paths in two-dimensional scenarios by the APPATT [13] to reduce fuel consumption.…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Task Scheduling Method for Multiple UAVs Based on Contract Net Protocol

Zhang

Liu

2022

Unmanned aerial vehicles are becoming promising platforms for disaster relief, such as providing emergency communication services in wireless sensor networks, delivering some living supplies, and mapping for disaster recovery. Dynamic task scheduling plays a very critical role in coping with emergent tasks. To solve the multi-UAV dynamic task scheduling, this paper constructs a multi-constraint mathematical model for multi-UAV dynamic task scheduling, involving task demands and platform capabilities. Three objectives are considered, which are to maximize the total profit of scheduled tasks, to minimize the time consumption, and to balance the number of scheduled tasks for multiple UAVs. The multi-objective problem is converted into single-objective optimization via the weighted sum method. Then, a novel dynamic task scheduling method based on a hybrid contract net protocol is proposed, including a buy-sell contract, swap contract, and replacement contract. Finally, extensive simulations are conducted under three scenarios with emergency tasks, pop-up obstacles, and platform failure to verify the superiority of the proposed method.