Trajectory Optimization of UAV SWARMs in Sensor Networks

University Of California-Irvine, Irvine CA

Investigators

Abstract

Uncrewed aerial vehicles (UAVs) are equipped with high-resolution cameras and a variety of sensors that can be used in different applications such as agriculture, environmental monitoring, disaster response, or infrastructure inspection. UAVs offer an excellent level of precision and adaptability, can be deployed easily, and provide access to remote regions. UAV-based sensing is a crucial asset in challenged settings, for example in remote regions with limited connectivity, during extreme events, and other similar situations. An optimized UAV trajectory ensures that data is collected in the most efficient and timely manner, from the most relevant locations, and at the most opportune times. This allows for real-time monitoring and quick response to dynamic environments or events of interest. This project develops the general mathematical framework that is needed to optimize UAV trajectories while incorporating practical operational constraints. To address the challenging problem of optimal UAV trajectory and deployment, various research communities have identified different objectives. For example, the communication community has focused on optimizing UAV trajectories to improve network capacity or data collection efficiency, often overlooking the UAV dynamics or the domain constraints. On the other hand, work from robotics and control communities tends to emphasize domain and maneuverability constraints with a focus on system dynamics. This project aims to develop a general framework, addressing the limitations of existing approaches, and incorporating the most critical challenges faced by UAVs. The project tackles several challenges related to trajectory optimization of a group of UAVs, including UAV dynamics, energy efficiency, heterogeneity, 3D sensing, collision and inaccessible region avoidance, and communication network connectivity. These are time-varying challenges, requiring dynamic and distributed solutions. To address these challenges, the project develops a mathematical framework with two related components. The first component develops and uses a heterogeneous quantization theory framework to formulate and solve the UAV trajectory optimization problem to find the desired trajectory. Then, the second component defines quadratic programming optimization problems and solves them to find the closest feasible trajectory to the desired trajectory, found in the first component, that satisfies all required constraints. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.