Semi-Automated Emergency Landing Site Selection Approach for UAVs

2021

Drones

Self Cite

Unmanned air vehicles (UAVs) or drones have gained popularity in recent years. However, the US Federal Aviation Administration (FAA) is still hesitant to open up the national air space (NAS) to UAVs due to safety concerns because UAVs have several orders of magnitude of more accidents than manned aircraft. To limit the scope in this paper, we focus on large, heavy, and expensive UAVs that can be used for cargo transfer and search and rescue operations, not small radio-controlled toy drones. We first present a general architecture for enhancing the safety of UAVs. We then illustrate how signal processing technologies can help enhance the safety of UAVs. In particular, we provide a bird’s eye view of the application of signal processing algorithms on condition-based maintenance, structural health monitoring, fault diagnostics, and fault mitigation, which all play critical roles in UAV safety. Some practical applications are used to illustrate the importance of the various algorithms.

“…In our recent papers [56][57][58][59][60], we have provided detailed procedures for contingency planning for engine failures.…”

Section: Contingency Planningmentioning

confidence: 99%

Safety Enhancement of UAVs from the Signal Processing’s Perspectives: A Bird’s Eye View

2021

Drones

Self Cite

“…The contingency point itself is a waypoint along the primary flight path and is abbreviated by the notation, CP. The emergency landing site for a contingency plan is selected using the method in [37] with consideration of five safety criterions, the surface type of emergency landing site, and its reachability. Based on the orientation of the selected emergency landing site, three waypoints of the final approach path that form a straight line are identified.…”

Section: Proposed Approachmentioning

confidence: 99%

“…Within the theater of the primary flight path, a list of emergency landing site candidates is identified using our Semi-Automated Emergency Landing Site Selection (SAELSS) method [37]. This method automates the job of finding crashing/ditching sites in a given geographical area of interest.…”

Section: Proposed Approachmentioning

confidence: 99%

Preflight Contingency Planning Approach for Fixed Wing UAVs with Engine Failure in the Presence of Winds

Ayhan

Budavari

et al. 2019

Sensors

Self Cite

Preflight contingency planning that utilizes wind forecast in path planning can be highly beneficial to unmanned aerial vehicles (UAV) operators in preventing a possible mishap of the UAV. This especially becomes more important if the UAV has an engine failure resulting in the loss of all its thrust. Wind becomes a significant factor in determining reachability of the emergency landing site in a contingency like this. The preflight contingency plans can guide the UAV operators about how to glide the aircraft to the designated emergency landing site to make a safe landing. The need for a preflight or in-flight contingency plan is even more obvious in the case of a communication loss between the UAV operator and UAV since the UAV will then need to make the forced landing autonomously without the operator. In this paper, we introduce a preflight contingency planning approach that automates the forced landing path generation process for UAVs with engine failure. The contingency path generation aims true reachability to the emergency landing site by including the final approach part of the path in forecast wind conditions. In the contingency path generation, no-fly zones that could be in the area are accounted for and the contingency flight paths do not pass through them. If no plans can be found that fulfill reachability in the presence of no-fly zones, only then, as a last resort, the no-fly zone avoidance rule is relaxed. The contingency path generation utilizes hourly forecast wind data from National Oceanic and Atmospheric Administration for the geographical area of interest and time of the flight. Different from past works, we use trochoidal paths instead of Dubins curves and incorporate wind as a parameter in the contingency path design.

“…Land cover classification has been used in change monitoring [1], construction surveying [2], agricultural management [3], digital terrain model (DTM) generation [4], and identifying emergency landing sites for UAVs during engine failures [5,6]. Some other uses of land cover classification are for biodiversity conservation [7], land-use [8], and urban planning [9].…”

Section: Introductionmentioning

confidence: 99%

“…For example, people have utilized shrub information for assessing the condition of grassland to determine whether a grassland has become unusable because of shrub encroachment or not [3]. In emergency landing of unmanned air vehicles (UAVs), it is critical to land on grassland rather than on trees or shrubs [5,6]. Removing tall vegetation from the digital surface model (DSM) such as trees and shrub is an important step in developing an accurate digital terrain model (DTM) [2].…”

Section: Introductionmentioning

confidence: 99%

Tree, Shrub, and Grass Classification Using Only RGB Images

Ayhan

2020

Remote Sensing

Self Cite

In this work, a semantic segmentation-based deep learning method, DeepLabV3+, is applied to classify three vegetation land covers, which are tree, shrub, and grass using only three band color (RGB) images. DeepLabV3+'s detection performance has been studied on low and high resolution datasets that both contain tree, shrub, and grass and some other land cover types. The two datasets are heavily imbalanced where shrub pixels are much fewer than tree and grass pixels. A simple weighting strategy known as median frequency weighting was incorporated into DeepLabV3+ to mitigate the data imbalance issue, which originally used uniform weights. The tree, shrub, grass classification performances are compared when all land cover types are included in the classification and also when classification is limited to the three vegetation classes with both uniform and median frequency weights. Among the three vegetation types, shrub is found to be the most challenging one to classify correctly whereas correct classification accuracy was highest for tree. It is observed that even though the median frequency weighting did not improve the overall accuracy, it resulted in better classification accuracy for the underrepresented classes such as shrub in our case and it also significantly increased the average class accuracy. The classification performance and computation time comparison of DeepLabV3+ with two other pixel-based classification methods on sampled pixels of the three vegetation classes showed that DeepLabV3+ achieves significantly higher accuracy than these methods with a trade-off for longer model training time.