299 research outputs found
A deep reinforcement learning based homeostatic system for unmanned position control
Deep Reinforcement Learning (DRL) has been proven to be capable of designing an optimal control theory by minimising the error in dynamic systems. However, in many of the real-world operations, the exact behaviour of the environment is unknown. In such environments, random changes cause the system to reach different states for the same action. Hence, application of DRL for unpredictable environments is difficult as the states of the world cannot be known for non-stationary transition and reward functions. In this paper, a mechanism to encapsulate the randomness of the environment is suggested using a novel bio-inspired homeostatic approach based on a hybrid of Receptor Density Algorithm (an artificial immune system based anomaly detection application) and a Plastic Spiking Neuronal model. DRL is then introduced to run in conjunction with the above hybrid model. The system is tested on a vehicle to autonomously re-position in an unpredictable environment. Our results show that the DRL based process control raised the accuracy of the hybrid model by 32%.N/
Assuring safe and efficient operation of UAV using explainable machine learning
The accurate estimation of airspace capacity in unmanned traffic management (UTM) operations is critical for a safe, efficient, and equitable allocation of airspace system resources. While conventional approaches for assessing airspace complexity certainly exist, these methods fail to capture true airspace capacity, since they fail to address several important variables (such as weather). Meanwhile, existing AI-based decision-support systems evince opacity and inexplicability, and this restricts their practical application. With these challenges in mind, the authors propose a tailored solution to the needs of demand and capacity management (DCM) services. This solution, by deploying a synthesized fuzzy rule-based model and deep learning will address the trade-off between explicability and performance. In doing so, it will generate an intelligent system that will be explicable and reasonably comprehensible. The results show that this advisory system will be able to indicate the most appropriate regions for unmanned aerial vehicle (UAVs) operation, and it will also increase UTM airspace availability by more than 23%. Moreover, the proposed system demonstrates a maximum capacity gain of 65% and a minimum safety gain of 35%, while possessing an explainability attribute of 70%. This will assist UTM authorities through more effective airspace capacity estimation and the formulation of new operational regulations and performance requirements
Autonomous Search and Rescue with Modeling and Simulation and Metrics
Unmanned Aerial Vehicles (UAVs) provide rapid exploration capabilities in search and rescue missions while accepting more risks than human operations. One limitation in that current UAVs are heavily manpower intensive and such manpower demands limit abilities to expand UAV use. In operation, manpower demands in UAVs range from determining tasks, selecting waypoints, manually controlling platforms and sensors, and tasks in between. Often, even a high level of autonomy is possible with human generated objectives and then autonomous resource allocation, routing, and planning. However, manually generating tasks and scenarios is still manpower intensive. To reduce manpower demands and move towards more autonomous operations, the authors develop an adaptive planning system that takes high level goals from a human operator and translates them into situationally relevant tasking. For expository simulation, the authors further describe constructing a scenario around the 2018 Hawaii Puna lava natural disaster
Unlocking the capabilities of explainable fewshot learning in remote sensing
Recent advancements have significantly improved the efficiency and
effectiveness of deep learning methods for imagebased remote sensing tasks.
However, the requirement for large amounts of labeled data can limit the
applicability of deep neural networks to existing remote sensing datasets. To
overcome this challenge, fewshot learning has emerged as a valuable approach
for enabling learning with limited data. While previous research has evaluated
the effectiveness of fewshot learning methods on satellite based datasets,
little attention has been paid to exploring the applications of these methods
to datasets obtained from UAVs, which are increasingly used in remote sensing
studies. In this review, we provide an up to date overview of both existing and
newly proposed fewshot classification techniques, along with appropriate
datasets that are used for both satellite based and UAV based data. Our
systematic approach demonstrates that fewshot learning can effectively adapt to
the broader and more diverse perspectives that UAVbased platforms can provide.
We also evaluate some SOTA fewshot approaches on a UAV disaster scene
classification dataset, yielding promising results. We emphasize the importance
of integrating XAI techniques like attention maps and prototype analysis to
increase the transparency, accountability, and trustworthiness of fewshot
models for remote sensing. Key challenges and future research directions are
identified, including tailored fewshot methods for UAVs, extending to unseen
tasks like segmentation, and developing optimized XAI techniques suited for
fewshot remote sensing problems. This review aims to provide researchers and
practitioners with an improved understanding of fewshot learnings capabilities
and limitations in remote sensing, while highlighting open problems to guide
future progress in efficient, reliable, and interpretable fewshot methods.Comment: Under review, once the paper is accepted, the copyright will be
transferred to the corresponding journa
Explainable Artificial Intelligence (xAI) Approaches and Deep Meta-Learning Models
The explainable artificial intelligence (xAI) is one of the interesting issues that has emerged recently. Many researchers are trying to deal with the subject with different dimensions and interesting results that have come out. However, we are still at the beginning of the way to understand these types of models. The forthcoming years are expected to be years in which the openness of deep learning models is discussed. In classical artificial intelligence approaches, we frequently encounter deep learning methods available today. These deep learning methods can yield highly effective results according to the data set size, data set quality, the methods used in feature extraction, the hyper parameter set used in deep learning models, the activation functions, and the optimization algorithms. However, there are important shortcomings that current deep learning models are currently inadequate. These artificial neural network-based models are black box models that generalize the data transmitted to it and learn from the data. Therefore, the relational link between input and output is not observable. This is an important open point in artificial neural networks and deep learning models. For these reasons, it is necessary to make serious efforts on the explainability and interpretability of black box models
Swarm of UAVs for Network Management in 6G: A Technical Review
Fifth-generation (5G) cellular networks have led to the implementation of
beyond 5G (B5G) networks, which are capable of incorporating autonomous
services to swarm of unmanned aerial vehicles (UAVs). They provide capacity
expansion strategies to address massive connectivity issues and guarantee
ultra-high throughput and low latency, especially in extreme or emergency
situations where network density, bandwidth, and traffic patterns fluctuate. On
the one hand, 6G technology integrates AI/ML, IoT, and blockchain to establish
ultra-reliable, intelligent, secure, and ubiquitous UAV networks. 6G networks,
on the other hand, rely on new enabling technologies such as air interface and
transmission technologies, as well as a unique network design, posing new
challenges for the swarm of UAVs. Keeping these challenges in mind, this
article focuses on the security and privacy, intelligence, and
energy-efficiency issues faced by swarms of UAVs operating in 6G mobile
networks. In this state-of-the-art review, we integrated blockchain and AI/ML
with UAV networks utilizing the 6G ecosystem. The key findings are then
presented, and potential research challenges are identified. We conclude the
review by shedding light on future research in this emerging field of research.Comment: 19,
Unmanned Vehicle Systems & Operations on Air, Sea, Land
Unmanned Vehicle Systems & Operations On Air, Sea, Land is our fourth textbook in a series covering the world of Unmanned Aircraft Systems (UAS) and Counter Unmanned Aircraft Systems (CUAS). (Nichols R. K., 2018) (Nichols R. K., et al., 2019) (Nichols R. , et al., 2020)The authors have expanded their purview beyond UAS / CUAS systems. Our title shows our concern for growth and unique cyber security unmanned vehicle technology and operations for unmanned vehicles in all theaters: Air, Sea and Land â especially maritime cybersecurity and China proliferation issues. Topics include: Information Advances, Remote ID, and Extreme Persistence ISR; Unmanned Aerial Vehicles & How They Can Augment Mesonet Weather Tower Data Collection; Tour de Drones for the Discerning Palate; Underwater Autonomous Navigation & other UUV Advances; Autonomous Maritime Asymmetric Systems; UUV Integrated Autonomous Missions & Drone Management; Principles of Naval Architecture Applied to UUVâs; Unmanned Logistics Operating Safely and Efficiently Across Multiple Domains; Chinese Advances in Stealth UAV Penetration Path Planning in Combat Environment; UAS, the Fourth Amendment and Privacy; UV & Disinformation / Misinformation Channels; Chinese UAS Proliferation along New Silk Road Sea / Land Routes; Automaton, AI, Law, Ethics, Crossing the Machine â Human Barrier and Maritime Cybersecurity.Unmanned Vehicle Systems are an integral part of the US national critical infrastructure The authors have endeavored to bring a breadth and quality of information to the reader that is unparalleled in the unclassified sphere. Unmanned Vehicle (UV) Systems & Operations On Air, Sea, Land discusses state-of-the-art technology / issues facing U.S. UV system researchers / designers / manufacturers / testers. We trust our newest look at Unmanned Vehicles in Air, Sea, and Land will enrich our students and readers understanding of the purview of this wonderful technology we call UV.https://newprairiepress.org/ebooks/1035/thumbnail.jp
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