Unmanned Aircraft Systems

This paper presents a novel indoor navigation technique using acoustic sensors, with emphasis on performance modeling and the system layout. Acoustic transmitters are arranged at known fixed locations and broadcast their respective signals following a Time Division Multiple Access (TDMA) scheme. The receiver position is calculated based on Time of Arrival (TOA) based ranging measurements from a minimum of four transmitters. The transmitters are arranged optimally to minimize Position Dilution of Precision (PDOP) as well as maximizing sensor availability. The proposed technique can provide accurate navigation observables in indoor environments, where conventional satellite-based navigation systems cannot deliver the required Position, Velocity, and Time (PVT) data. Additionally, being based on acoustic signals, it is immune to signal-in-space electromagnetic interferences. The TDMA based acoustic navigation system is described, with potential errors in ranging due to Doppler effect, multipath, atmospheric propagation, and signal delays. The error in positioning due to platform dynamics is also discussed. This analysis will lead to an optimized arrangement of transmitters supporting the future ground and flight experimental activities.

Unmanned Aircraft Systems (UAS) offer many opportunities in a wide range of industries to support remote sensing and surveillance. While platform autonomy and intelligence have seen large advances in recent decades, a key challenge is the operation of multiple Unmanned Aerial Vehicles (UAV) by a single operator in 'one-to-many' operations. To support one-to-many operations, higher levels of human-autonomy teaming are required, where human operators collaborate with autonomous agents through the use of adaptive Human-Machine Interfaces and Interactions (HMI2). In this paper, the one-to-many concept is applied to a bushfire-fighting scenario. The paper presents the UAV avionics systems design along with the Ground Control Station (GCS) design, which features a number of emerging HMI2 concepts.

Unmanned Aircraft Systems (UAS), also known as drones, are capable of autonomously carrying out an increasing number of duties with substantially greater efficiency compared to human intervention. UAS researchers aim to enhance the levels of safety, mission effectiveness and autonomy, as well as explore additional capabilities. Crop health monitoring is a very promising application of UAS as the early detection of plant diseases can supplement traditional crop monitoring techniques (such as visual observation) to support timely mitigation, thereby offering substantial benefits to crop yield and quality. This paper presents new UAS-borne Electro-Optics/Infrared (EO/IR) systems based on bistatic Light Detection and Ranging (LIDAR) for early detection of crop diseases. The proposed bistatic LIDAR system measures over extended observation periods above the crop canopy anomalies in carbon dioxide (CO 2) concentration potentially associated with disruptions in the plant photosynthesis. In particular, this paper discusses the application of the bistatic LIDAR to the early detection of powdery mildew in strawberry crops and presents the initial modelling and simulation activities performed to predict the sensor performance. Based on the results of this analysis, a plan for future experimental activities is also developed.

Unmanned aerial systems (UASs) are powerful tools for research and monitoring of wildlife. However, the effects of these systems on most marine mammals are largely unknown, preventing the establishment of guidelines that will minimize animal disturbance. In this study, we evaluated the behavioral responses of coastal bottlenose dolphins (Tursiops truncatus) and Antillean manatees (Trichechus manatus manatus) to small multi-rotor UAS flight. From 2015 to 2017, we piloted 211 flights using DJI quadcopters (Phantom II Vision +, 3 Professional and 4) to approach and follow animals over shallow-water habitats in Belize. The quadcopters were equipped with high-resolution cameras to observe dolphins during 138 of these flights, and manatees during 73 flights. Aerial video observations of animal behavior were coded and paired with flight data to determine whether animal activity and/or the UAS's flight patterns caused behavioral changes in exposed animals. Dolphins responded to UAS flight at altitudes of 11–30 m and responded primarily when they were alone or in small groups. Single dolphins and one pair responded to the UAS by orienting upward and turning toward the aircraft to observe it, before quickly returning to their pre-response activity. A higher number of manatees responded to the UAS, exhibiting strong disturbance in response to the aircraft from 6 to 104 m. Manatees changed their behavior by fleeing the area and sometimes this elicited the same response in nearby animals. If pursued post-response, manatees repeatedly responded to overhead flight by evading the aircraft's path. These findings suggest that the invasiveness of UAS varies across individuals, species, and taxa. We conclude that careful exploratory research is needed to determine the impact of multi-rotor UAS flight on diverse species, and to develop best practices aimed at reducing the disturbance to wildlife that may result from their use.