Real Time Data Downlink Device for Live Telemetry from Instrumented Vehicles

Real Time Data Downlink Device (RTDD) for Live Telemetry from Instrumented Vehicles Avinash Muthu Krishnan1, Marc D. Compere1, Kevin A. Adkins2 1 Department of Mechanical Engineering, Embry-Riddle Aeronautical University 2 Department of Aeronautical Science, Embry-Riddle Aeronautical University This paper presents a microcontroller and communications design that delivers real-time telemetry data over the cellular network from vehicles instrumented for scientific or engineering purposes. The Real Time Data Downlink (RTDD) device is being designed for atmospheric data collection on an aerial platform. While this application specifically pertains to the atmospheric sciences, the data collection technique is broadly applicable to ground, surface, or aerial platform data collection. The RTDD is implemented on four DJI Matrice-100 quadcopters that transmit real time position, wind speed, pressure, temperature and humidity over the cellular network. Each vehicle writes sensor data locally while simultaneously transmitting data samples to a data collection computer for real time experiment monitoring. The data collection computer runs an open-sourced software called the Mobility Virtual Environment (MoVE). MoVE aggregates all incoming data streams from each vehicle to provide a comprehensive picture of the scenario with a live 2D map display of all vehicles and a browser-based table to present the data. The RTDD provides real time data thus ensuring complete mission execution and confirmation of sensor performance. Therefore, the RTDD is a critical component of the instrumented aircraft and an overall successful multi-vehicle data collection effort

New Air Quality Measurement Method: Low-Cost Sensors on UAV’s

With the rapid industrialization and the current status of climate change, air pollution has become a global concern. However, detecting atmospheric pollutants is costly, time-consuming, and cumbersome. Currently, the Environmental Protection Agency (US EPA) utilizes filter-based techniques in their federal reference and federal equivalent methods (FRM and FEM, respectively) to measure ground-based particulate matter (PM) levels in the atmosphere. Recently, the development of low-cost sensors has helped in combatting the high cost associated with acquiring these measurements. These sensors allow for PM concentrations to be measured at high resolutions. Due to their surface mounted nature, the EPA’s methods are limited in measuring the concentrations of PM at the ground-level. Hence, they lack the ability of determining the concentrations at various altitudes, which is important in characterizing the origin and the formation pathway of such pollutants. To address these shortcomings, we propose placing multiple low-cost sensors on Unmanned Aerial Vehicles (UAVs) to measure the concentrations of PM in Daytona Beach, FL. Sampling will be conducted seasonally, and the PM concentrations will be compared to their counterpart observations obtained using the EPA’s methods. The findings of this study should aid in the development of low-cost air pollution sensors that can be hosted on UAVs. This work promises to be advantageous in detecting air pollutants in both congested and remote areas

Low-cost Sensors on Unmanned Aerial Vehicles: an Advancement in Air Quality Measurement

With the rapid industrialization and the current status of climate change, air pollution has become a global concern. However, detecting atmospheric pollutants is costly, time-consuming, and cumbersome. Currently, the Environmental Protection Agency (US EPA) utilizes filter-based techniques in their federal reference and federal equivalent methods (FRM and FEM, respectively) to measure ground-based particulate matter (PM) levels in the atmosphere. Recently, the development of low-cost sensors has helped in combatting the high cost associated with acquiring these measurements. These sensors allow for PM concentrations to be measured at high resolutions. Due to their surface mounted nature, the EPA’s methods are limited in measuring the concentrations of PM at the ground-level. Hence, they lack the ability of determining the concentrations at various altitudes, which is important in characterizing the origin and the formation pathway of such pollutants. To address these shortcomings, we propose placing multiple low-cost sensors on Unmanned Aerial Vehicles (UAVs) to measure the concentrations of PM in Daytona Beach, FL. Sampling will be conducted seasonally, and the PM concentrations will be compared to their counterpart observations obtained using the EPA’s methods. The findings of this study should aid in the development of low-cost air pollution sensors that can be hosted on UAVs. This work promises to be advantageous in detecting air pollutants in both congested and remote areas

Hyper-Local Weather Predictions with the Enhanced General Urban Area Microclimate Predictions Tool

This paper presents enhancements to, and the demonstration of, the General Urban area Microclimate Predictions tool (GUMP), which is designed to provide hyper-local weather predictions by combining machine-learning (ML) models and computational fluid dynamic (CFD) simulations. For the further development and demonstration of GUMP, the Embry–Riddle Aeronautical University (ERAU) campus was used as a test environment. Local weather sensors provided data to train ML models, and CFD models of urban- and suburban-like areas of ERAU’s campus were created and iterated through with a wide assortment of inlet wind speed and direction combinations. ML weather sensor predictions were combined with best-fit CFD models from a database of CFD flow fields, providing flight operational areas with a fully expressed wind flow field. This field defined a risk map for uncrewed aircraft operators based on flight plans and individual flight performance metrics. The potential applications of GUMP are significant due to the immediate availability of weather predictions and its ability to easily extend to arbitrary urban and suburban locations

Go with the Flow: Estimating Wind Using Uncrewed Aircraft

This paper presents a fundamentally different approach to wind estimation using Uncrewed Aircraft (UA) than the vast majority of existing methods. This method uses no on-board flow sensor and does not attempt to estimate thrust or drag forces. Using only GPS and orientation sensors, the strategy estimates wind vectors in an Earth-fixed frame during turning maneuvers. The method presented here is called the Wind-Arc method. The philosophy behind this method has been seen in practice, but this paper presents an alternative derivation with resulting performance evaluations in simulations and flight tests. The simulations verify the method provides perfect performance under ideal conditions using simulated GPS, heading angle, and satisfied assumptions. When applied to experimental flight test data, the method works and follows both the airspeed and wind speed trends, but improvements can still be made. Wind triangles are displayed at each instant in time along the flight path that illustrate the graphical nature of the approach and solution. Future work will include wind gust estimation and a Quality of Estimate (QoE) metric to determine what conditions provide good wind speed estimates while preserving the method’s generality and simplicity

Deconfliction of Simultaneous Multi-Vehicle Operations

Evolving technology and regulation is making package delivery via drone more feasible since Amazon first ignited the public imagination with the idea in 2013. However, additional progress needs to be made before package delivery on a large scale is viable. Some of these needs include the maturing of vehicle-to-vehicle communication, standards and decision laws that enable autonomous package delivery drones to come to consensus on delivery route selection and prioritization when multiple uncrewed aircraft (UA) converge on a single drop-off location. This work describes a two-part project that, first, simulates a package delivery scenario using the Mobility Virtual Environment (MoVE) and, secondly, physically executes the package delivery scenario on Embry-Riddle’s campus. Both the simulation and experimental execution involve solving the consensus problem with multiple unique UA, and payloads, arriving at the drop-off location at similar times

Hyper-Local Weather Predictions with the Enhanced General Urban Area Microclimate Predictions Tool

This paper presents enhancements to, and the demonstration of, the General Urban area Microclimate Predictions tool (GUMP), which is designed to provide hyper-local weather predictions by combining machine-learning (ML) models and computational fluid dynamic (CFD) simulations. For the further development and demonstration of GUMP, the Embry–Riddle Aeronautical University (ERAU) campus was used as a test environment. Local weather sensors provided data to train ML models, and CFD models of urban- and suburban-like areas of ERAU’s campus were created and iterated through with a wide assortment of inlet wind speed and direction combinations. ML weather sensor predictions were combined with best-fit CFD models from a database of CFD flow fields, providing flight operational areas with a fully expressed wind flow field. This field defined a risk map for uncrewed aircraft operators based on flight plans and individual flight performance metrics. The potential applications of GUMP are significant due to the immediate availability of weather predictions and its ability to easily extend to arbitrary urban and suburban locations