Unmanned Aircraft Systems Platforms Research Articles

Hardware Design of a Small UAS Helicopter for Remote Sensing Operations

This paper presents the hardware design and integration process employed to develop an Unmanned Aircraft System (UAS) helicopter. The design process evolves from the bare airframe (without any electronics), to become a complete and advanced UAS platform for remote sensing applications. The improvements, design decisions and justifications are described throughout the paper. Two airframes have been used during the design and integration process: the AF25B model and the more advanced AF30 model, from the Copterworks company. The airframe engine reliability and fuel economy have been improved by adding an Electronic Fuel Injection (EFI) and Capacitor Discharge Ignition (CDI), both managed by an Engine Control Unit (ECU). On-board power supply generation and regulation have also been designed and validated. Finally, the integration process incorporates on-board mission computation to improve the concept of operation in remote sensing applications. Several flight tests have been performed to verify the reliability of the whole system. The flight test results demonstrate the correct process of integration and the feasibility of the UAS.

Assessing Lodging Severity over an Experimental Maize (Zea mays L.) Field Using UAS Images

Lodging has been recognized as one of the major destructive factors for crop quality and yield, resulting in an increasing need to develop cost-efficient and accurate methods for detecting crop lodging in a routine manner. Using structure-from-motion (SfM) and novel geospatial computing algorithms, this study investigated the potential of high resolution imaging with unmanned aircraft system (UAS) technology for detecting and assessing lodging severity over an experimental maize field at the Texas A&M AgriLife Research and Extension Center in Corpus Christi, Texas, during the 2016 growing season. The method was proposed to not only detect the occurrence of lodging at the field scale, but also to quantitatively estimate the number of lodged plants and the lodging rate within individual rows. Nadir-view images of the field trial were taken by multiple UAS platforms equipped with consumer grade red, green, and blue (RGB), and near-infrared (NIR) cameras on a routine basis, enabling a timely observation of the plant growth until harvesting. Models of canopy structure were reconstructed via an SfM photogrammetric workflow. The UAS-estimated maize height was characterized by polygons developed and expanded from individual row centerlines, and produced reliable accuracy when compared against field measures of height obtained from multiple dates. The proposed method then segmented the individual maize rows into multiple grid cells and determined the lodging severity based on the height percentiles against preset thresholds within individual grid cells. From the analysis derived from this method, the UAS-based lodging results were generally comparable in accuracy to those measured by a human data collector on the ground, measuring the number of lodging plants (R2 = 0.48) and the lodging rate (R2 = 0.50) on a per-row basis. The results also displayed a negative relationship of ground-measured yield with UAS-estimated and ground-measured lodging rate.

Path-following control of small fixed-wing unmanned aircraft systems with [formula omitted] type performance

The focus of this paper is on the design of path-following controllers for a small fixed-wing unmanned aircraft system (UAS) using the H∞ robust control framework. The robust controllers are synthesized based on a lumped path-following and UAS dynamics formulation, effectively combining the six degree-of-freedom aircraft dynamics with the established parallel transport frame virtual vehicle dynamics. Two path-following controllers with a conventional cascaded architecture consisting of an outer guidance loop and an inner stabilization loop are also considered as points of reference. The robustness and performance of these controllers are tested in a rigorous MATLAB simulation environment that includes steady winds, turbulence, measurement noise, and time delays. Finally, flight experiments are conducted on a small fixed-wing UAS platform, and the controllers are compared in terms of tracking performance, control effort, and ease of implementation.

Implementation of Brain Emotional Learning-Based Intelligent Controller for Flocking of Multi-Agent Systems

The Brain Emotional Learning Based Intelligent Controller (BELBIC) is a neurobiologically-motivated intelligent controller based on a computational model of emotional learning in mammalian limbic system. The learning capabilities, multi-objective properties, and low computational complexity of BELBIC make it a very promising tool for implementation in real-time applications.Our research combines, in an original way, the BELBIC methodology with a flocking control strategy, in order to perform real-time coordination of multiple Unmanned Aircraft Systems (UAS). The characteristics of BELBIC fit well in this scenario, since almost always the dynamics of the autonomous agents are not fully known, and furthermore, since they operate in close proximity, they are subjected to aggressive external disturbances. Numerical and experimental results based on the coordination of multiple quad rotorcraft UAS platforms demonstrate the applicability and satisfactory performance of the proposed method.

Seeing the Threat: Pilot Visual Detection of Small Unmanned Aircraft Systems in Visual Meteorological Conditions

One key challenge of integrating Unmanned Aircraft Systems (UAS) platforms into the National Airspace System (NAS) is the potential for midair collisions between manned aircraft and the unmanned system. The lack of an established UAS benchmark for Detect, Sense & Avoid Systems put the preponderance of avoidance efforts on manned aircraft pilots to visually see and avoid potential collision threats. The small size, unusual configurations, and diverse operational applications of unmanned systems make UAS platforms difficult to visually identify. This paper sought to determine the mean visibility distance of small UAS systems (sUAS) to an alerted pilot flying a general aviation aircraft in visual meteorological conditions (VMC). The study evaluated mean visibility distance to various sUAS platforms based on a scripted set of UAS convergence conditions. The study utilized a mixed method design in which a general aviation aircraft was flown into a UAS operations area. Study pilots were instructed to locate a flying UAS aircraft without bearing assistance. Both the UAS and manned aircraft were assigned vertically de-conflicted altitudes with the UAS aircraft executing a series of converging and crossing courses relative to the manned aircraft. The distance at which the pilot visually located the UAS platform was timestamped and electronically recorded via a GPS tracking device. The various conditions were analyzed to determine significant visibility differences among the various convergence conditions. Qualitative data was collected from participant comments and observations recorded by an in-flight safety observer.

Testing and Evaluating Low Altitude Unmanned Aircraft System Technology for Maritime Domain Awareness and Oil Spill Response in the Arctic

AbstractNational and international policies and treaties require the protection and exploration of the Arctic. The maritime services play a primary role in pursuing responsible Arctic stewardship by protecting the environment and the personnel conducting operations and research in this harsh environment. The National Oceanic and Atmospheric Administration (NOAA) is an important partner to the U.S. Coast Guard (USCG) in hazard response and mitigation (including oil spills and search and rescue). During Arctic Shield exercises, as part of the USCG Research and Development Center's Arctic Technology Evaluation, manned and unmanned systems including the AeroVironment Puma™ All Environment (AE) (Puma) Unmanned Aircraft System (UAS), were used to provide real-time information for maritime domain awareness and oil spill response in the Arctic. Real-time information distribution and maritime domain awareness are critical to prepare for and respond to potential environmental disasters in the Arctic. Additionally, the Puma was assessed for shipboard operations capabilities, Arctic air space coordination, deconfliction and safety issues, and real-time data visualization through the Arctic Environmental Response Management Application® as part of a larger data management plan. The results are provided from the successful Puma testing during the Arctic Shield 2013 and 2014 exercises aboard the USCG Cutter (USCGC; Icebreaker) Healy. An overview of these operations is given with recommendations for future testing and technology assessments of small UAS platforms for Arctic shipboard operational deployments. These findings are put into context for utilization in the field to support operations and decision making in the case of a real oil spill in the Arctic region.

Detection of a surrogate biological agent with a portable surface plasmon resonance sensor onboard an unmanned aircraft system

A system was developed to perform near real-time biological threat agent (BTA) detection with a small autonomous unmanned aircraft system (UAS). Biological sensors recently reached a level of miniaturization and sensitivity that have made UAS integration a feasible task. A surface plasmon resonance (SPR) biosensor was integrated into a small UAS platform for the first time, providing the UAS with the capability to collect and then quantify the concentration of a surrogate biological agent in near realtime. The sensor operator ran the SPR unit through a ground-station laptop, viewing the sensor data in real time during flight. An aerial sampling mechanism was also developed for use with the SPR sensor. The sampling system utilized a custom impinger setup to collect and concentrate aerosolized particles. The SPR and sampling system's feasibility was demonstrated using an aerosolized sucrose solution as a mock BTA. Three field experiments were carried out to test and validate the biological sampling system. In the first field experiment, the collection system was tested by flying the UAS through a ground-based plume of water-soluble blue dye. In the second field experiment, a sucrose solution was autonomously aerosolized, collected, and then detected by the combined sampling and SPR sensor subsystems onboard the UAS. In the third field experiment, a dye was released from one UAS (the leader) and captured by another UAS (the follower). Together, these field experiments illustrate the capability of the UAS to detect and quantify the concentration of a BTA released at altitude. Our integrated SPR system sets the stage for future work to detect and track BTAs in the atmosphere and assist in localizing their sources.

Reconfigurable automated behavior for UAS applications

Unmanned Aircraft Systems (UAS) have great potential to be used in a wide variety of applications, such as environmental applications, emergency situations and surveillance tasks, among others. However, most current UAS solutions, if not remotely piloted, rely on waypoint based flight control systems for their navigation and are unable to coordinate the aircraft flight with payload operation. We believe that increased automation, together with reconfiguration capabilities and cost-effectiveness, are key requirements for UAS to be successful in a commercial domain. The resulting platform should be affordable and able to operate in different application scenarios with reduced development effort and human intervention.In this paper, an architecture for providing UAS platforms with reconfigurable automated behavior is presented. The desired automation and reconfiguration capabilities are built upon the core sub-systems in that architecture: the Flight Plan Manager and the Mission Manager. Both systems are part of a wider set of embarked services that manage UAS operations during the mission. The Flight Plan Manager and the Mission Manager are respectively responsible for governing the UAS flight and orchestrating operation of other services on-board the UAS. There are two distinct features of the system: firstly, UAS behavior is not hard-coded into its software components, but described using specification formalisms; secondly, the UAS flight path is described by means of an XML based language specifically designed for this purpose. With this approach, flight and payload behavior are described in separate documents that complement each other. Prototype implementations of these services have been implemented and used to validate the proposed specification and execution methods with an application example devoted to the monitoring of wildfires.

Operation of small sensor payloads on tactical sized unmanned air vehicles

AbstractThe miniaturisation of sensors has in recent years led to the ability to provide multiple sensor operations from a single Unmanned Aircraft System (UAS) platform. Multiple UAS platforms can be synchronised to link devices from separate UAS platforms thus proving a powerful capability for data collection, while opening up interesting opportunities in the way data is retrieved and used. A range of new sensors being investigated will be discussed with reference to selected case studies that have taken place. As we move into an increasingly growing, data rich environment, data management, quality and pedigree will become of increasing importance. Operations for both defence and non-defence applications will be discussed with reference to the present capability and what is required in future systems. This paper describes some of the sensors currently being evaluated. In the coming years it is expected that we will see a sharp increase in the use of small tactical sized autonomous vehicles in general and a large growth in the capability of the payloads being used.