1,759 research outputs found
Large-Scale Unmanned Aerial Systems Traffic Density Prediction and Management
In recent years, the applications of Unmanned Aerial Systems (UAS) has become more and more popular. We envision that in the near future, the complicated and high density UAS traffic will impose significant burden to air traffic management. Lot of works focus on the application development of individual Small Unmanned Aerial Systems (sUAS) or sUAS management Policy, however, the study of the UAS cluster behaviors such as forecasting and managing of the UAS traffic has generally not been addressed. In order to address the above issue, there is an urgent need to investigate three research directions. The first direction is to develop a high fidelity simulator for the UAS cluster behavior evaluation. The second direction to study real time trajectory planning algorithms to mitigate the high dense UAS traffic. The last direction is to investigate techniques that rapidly and accurately forecast the UAS traffic pattern in the future. In this thesis, we elaborate these three research topics and present a universal paradigm to predict and manage the traffic for the large-scale unmanned aerial systems.
To enable the research in UAS traffic management and prediction, a Java based Multi-Agent Air Traffic and Resource Usage Simulation (MATRUS) framework is first developed. We use two types of UAS trajectories, Point-to-Point (P2P) and Man- hattan, as the case study to describe the capability of presented framework. Various communication and propagation models (i.e. log-distance-path loss) can be integrated with the framework to model the communication between UASs and base stations. The results show that MATRUS has the ability to evaluate different sUAS traffic management policies, and can provide insights on the relationships between air traf- fic and communication resource usage for further studies. Moreover, the framework can be extended to study the effect of sUAS Detect-and-Avoid (DAA) mechanisms, implement additional traffic management policies, and handle more complex traffic demands and geographical distributions.
Based on the MATRUS framework, we propose a Sparse Represented Temporal- Spatial (SRTS) UAS trajectory planning algorithm. The SRTS algorithm allows the sUAS to avoid static no-fly areas (i.e. static obstacles) or other areas that have congested air traffic or communication traffic. The core functionality of the routing algorithm supports the instant refresh of the in-flight environment making it appropri- ate for highly dynamic air traffic scenarios. The characterization of the routing time and memory usage demonstrate that the SRTS algorithm outperforms a traditional Temporal-Spatial routing algorithm.
The deep learning based approach has shown an outstanding success in many areas, we first investigated the possibility of applying the deep neural network in predicting the trajectory of a single vehicle in a given traffic scene. A new trajectory prediction model is developed, which allows information sharing among vehicles using a graph neural network. The prediction is based on the embedding feature, which is derived from multi-dimensional input sequences including the historical trajectory of target and neighboring vehicles, and their relative positions. Compared to other existing trajectory prediction methods, the proposed approach can reduce the pre- diction error by up to 50.00%. Then, we present a deep neural network model that extracts the features from both spatial and temporal domains to predict the UAS traffic density. In addition, a novel input representation of the future sUAS mission information is proposed. The pre-scheduled missions are categorized into 3 types according to their launching times. The results show that our presented model out- performs all of the baseline models. Meanwhile, the qualitative results demonstrate that our model can accurately predict the hot spot in the future traffic map
Optimizing communication and computation for multi-UAV information gathering applications
Typical mobile agent networks, such as multi-UAV systems, are constrained by limited resources: energy, computing power, memory and communication bandwidth. In particular,
limited energy affects system performance directly, such as system lifetime. Moreover, it has been demonstrated experimentally in the wireless sensor network literature that the total energy consumption is often dominated by the communication cost, i.e. the computational and the sensing energy are small compared to the
communication energy consumption. For this reason, the lifetime of the network can be extended significantly by minimizing the
communication distance as well as the amount of communication data, at the expense of increasing computational cost. In this work, we aim at attaining an optimal trade-off between the communication and the computational energy. Specifically, we propose a mixed-integer optimization formulation for a multihop
hierarchical clustering-based self-organizing UAV network incorporating data aggregation, to obtain an energy-efficient information routing scheme. The proposed framework is tested on two applications, namely target tracking and area mapping. Based on simulation results, our method can significantly save energy compared to a baseline strategy, where there is no data aggregation and clustering scheme
Multi-Criteria Decision Making in Complex Decision Environments
In the future, many decisions will either be fully automated or supported by autonomous system. Consequently, it is of high importance that we understand how to integrate human preferences correctly. This dissertation dives into the research field of multi-criteria decision making and investigates the satellite image acquisition scheduling problem and the unmanned aerial vehicle routing problem to further the research on a priori preference integration frameworks. The work will aid in the transition towards autonomous decision making in complex decision environments. A discussion on the future of pairwise and setwise preference articulation methods is also undertaken. "Simply put, a direct consequence of the improved decision-making methods is,that bad decisions more clearly will stand out as what they are - bad decisions.
A Patient Risk Minimization Model for Post-Disaster Medical Delivery Using Unmanned Aircraft Systems
The purpose of this research was to develop a novel routing model for delivery of medical supplies using unmanned aircraft systems, improving existing vehicle routing models by using patient risk as the primary minimization variable.
The vehicle routing problem is a subset of operational research that utilizes mathematical models to identify the most efficient route between sets of points. Routing studies using unmanned aircraft systems frequently minimize time, distance, or cost as the primary objective and are powerful decision-making tools for routine delivery operations. However, the fields of emergency triage and disaster response are focused on identifying patient injury severity and providing the necessary care. This study addresses the misalignment of priorities between existing routing models and the emergency response industry by developing an optimization model with injury severity to measure patient risk.
Model inputs for this study include vehicle performance variables, environmental variables, and patient injury variables. These inputs are used to construct a multi-objective mixed-integer nonlinear programming (MOMINLP) optimization model with the primary objective of minimizing total risk for a set of patients. The model includes a secondary aim of route time minimization to ensure optimal fleet deployment but is constrained by the risk minimization value identified in the first objective. This multi-objective design ensures risk minimization will not be sacrificed for route efficiency while still ensuring routes are completed as expeditiously as possible.
The theoretical foundation for quantifying patient risk is based on mass casualty triage decision-making systems, specifically the emergency severity index, which focuses on sorting patients into categories based on the type of injury and risk of deterioration if additional assistance is not provided. Each level of the Emergency Severity Index is assigned a numerical value, allowing the model to search for a route that prioritizes injury criticality, subject to the appropriate vehicle and environmental constraints.
An initial solution was obtained using stochastic patient data and historical environmental data validated by a Monte Carlo simulation, followed by a sensitivity analysis to evaluate the generalizability and reliability of the model. Multiple what-if scenarios were built to conduct the sensitivity analysis. Each scenario contained a different set of variables to demonstrate model generalizability for various vehicle limitations, environmental conditions, and different scales of disaster response.
The primary contribution of this study is a flexible and generalizable optimization model that disaster planning organizations can use to simulate potential response capabilities with unmanned aircraft. The model also improves upon existing optimization tools by including environmental variables and patient risk inputs, ensuring the optimal solution is useful as a real-time disaster response tool
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