Obstacle Avoidance Framework Based on Reach Sets

This paper describes the latest efforts to develop an Automated UAV Mission System (AUMS) for small vertical takeoff and landing (VTOL) unmanned air vehicles (UAVs). In certain applications such as force protection, perimeter security, and urban surveillance a VTOL UAV can provide far greater utility than fixed-wing UAVs or ground-based sensors. The VTOL UAV can operate much closer to an object of interest and can provide a hover-and-stare capability to keep its sensors trained on an object, while the fixed wing UAV would be forced into a higher altitude loitering pattern where its sensors would be subject to intermittent blockage by obstacles and terrain. The most significant disadvantage of a VTOL UAV when compared to a fixed-wing UAV is its reduced flight endurance. AUMS addresses this disadvantage by providing forward staging, refueling, and recovery capabilities for the VTOL UAV through a host unmanned ground vehicle (UGV), which serves as a launch/recovery platform and service station. The UGV has sufficient payload capacity to carry UAV fuel for multiple launch, recovery, and refuel iterations. The UGV also provides a highly mobile means of forward deploying a small UAV into hazardous areas unsafe for personnel, such as chemically or biologically contaminated areas. Teaming small UAVs with large UGVs can decrease risk to personnel and expand mission capabilities and effectiveness. There are numerous technical challenges being addressed by these development efforts. Among the challenges is the development and integration of a precision landing system compact and light enough to allow it to be mounted on a small VTOL UAV while providing repeatable landing accuracy to safely land on the AUMS. Another challenge is the design of a UGV-transportable, expandable, self-centering landing pad that contains hardware and safety devices for automatically refueling the UAV. A third challenge is making the design flexible enough to accommodate different types of VTOL UAVs, such as the AAI iSTAR ducted-fan vehicle and small helicopter UAVs. Finally, a common command-and-control architecture which supports the UAV, UGV, and AUMS must be developed and interfaced with these systems to allow fully autonomous collaborative behaviors. Funded by the Joint Robotics Program, AUMS is part of a joint effort with the Air Force Research Laboratory and the Army Missile Research Development and Engineering Command. The objective is to develop and demonstrate UGV-UAV teaming concepts and work with the warfighter to ensure that future upgrades are focused on operational requirements. This paper describes the latest achievements in AUMS development and some of the military program and first responder situations that could benefit from this system.

With the rapid development of 5G networks and the availability of small Unmanned Air Vehicles (UAVs), low cost and multi-view live drone broadcasting becomes possible. However, limited by the load capacity, small UAVs cannot uninterruptedly complete long-time and long-distance shooting tasks. This paper focuses on the energy circularly recharging strategy to solve the conflict between small UAV load capacity and mission execution capability in the live marathon broadcast scenario. Based on fixed flight paths of the UAV-T (Task UAV), we adopt a greedy strategy to deploy temporary energy supply stations along the marathon course tracks. UAV-Rs (Replenishment UAVs) depart from the energy supply stations and resupply the UAV-T with energy by air docking; then, the UAV-T needs not fly back to resupply the energy on the ground; and the live drone broadcast task is also uninterrupted. Simulations show that the cost of deploying temporary energy recharge stations can be greatly reduced by using the aerial recharge scheme.

The University of North Dakota has been actively developing enabling airborne sense and avoid (SAA) technologies under a series of projects funded by the Department of Defense, and has selected the Automatic Dependent Surveillance – Broadcast (ADS-B) transceiver as one of its central components for cooperative aircraft mid-air collision avoidance. The system integrates a 4D (i.e., latitude, longitude, altitude, and time) SAA algorithm dependent on UAS dynamic performance factors. This paper presents the results of implementation and flight testing of a miniaturized collision avoidance system using ADS-B derived information. The flight test results that took place onboard a small UAS in the summer of 2010 demonstrate that a complex airborne SAA system utilizing an ADS-B transceiver can be integrated into a small UAS vehicle to improve safety and eventually allow integration of UAS vehicles into the U.S. National Airspace System (NAS).

Cooperative and non-cooperative Sense-and-Avoid (SAA) capabilities are key enablers for Unmanned Aircraft Vehicle (UAV) to safely and routinely access all classes of airspace. In this paper state-of-the-art cooperative and non-cooperative SAA sensor/system technologies for small-to-medium size UAV are identified and the associated multi-sensor data fusion techniques are introduced. A reference SAA system architecture is presented based on Boolean Decision Logics (BDL) for selecting and sorting non-cooperative and cooperative sensors/systems including both passive and active Forward Looking Sensors (FLS), Traffic Collision Avoidance System (TCAS) and Automatic Dependent Surveillance - Broadcast (ADS-B). After elaborating the SAA system processes, the key mathematical models associated with both non-cooperative and cooperative SAA functions are presented. The analytical models adopted to compute the overall uncertainty volume in the airspace surrounding an intruder are described. Based on these mathematical models, the SAA Unified Method (SUM) for cooperative and non-cooperative SAA is presented. In this unified approach, navigation and tracking errors affecting the measurements are considered and translated to unified range and bearing uncertainty descriptors, which apply both to cooperative and non-cooperative scenarios. Simulation case studies are carried out to evaluate the performance of the proposed SAA approach on a representative host platform (AEROSONDE UAV) and various intruder platforms. Results corroborate the validity of the proposed approach and demonstrate the impact of SUM towards providing a cohesive logical framework for the development of an airworthy SAA capability, which provides a pathway for manned/unmanned aircraft coexistence in all classes of airspace.

The constraints of weight, volume and power for Small unmanned air vehicle (UAV) restrict the application of sensors with heavy and good performance and powerful processors. This paper presents a real-time solution of autonomous flight navigation and its results for small UAV by applying small, cheap, low precision and low-power integrated navigation system, which includes Strap-down Inertial Navigation System (SINS) based on Micro-electro-mechanical system (MEMS) inertial sensors, Global Positioning System (GPS) receiver and magnetometer. The Square-Root Unscented Kalman filter (SR-UKF) for data fusion using in this MEMS-SINS/GPS/ magnetometer integrated navigation system provides continuous and reliable navigation results for the loops of guidance and control for the small UAV with autonomous flight. The whole integrated navigation system algorithm is implemented within low-power embedded microprocessors. The real-time flight test results show that the MEMS-SINS/GPS/magnetometer integrated navigation system is effective and accurate.

Performance and stability are demonstrated for a nonlinear path-following guidance method for unmanned air vehicles. The method was adapted from a pure pursuit-based path following, which has been widely used in ground based robot applications. The method is known to approximate a proportional-derivative controller when following a straight line path, but it is shown that there is also an element of anticipatory control that enables tight tracking when following curved paths. Ground speed is incorporated into the computation of commanded lateral acceleration, which adds an adaptive capability to accommodate vehicle speed changes due to external disturbances such as wind. Asymptotic Lyapunov stability of the nonlinear guidance method is demonstrated when the unmanned air vehicle is following circular paths. The adaptive nature of the guidance method makes its stability independent of vehicle velocity. The stability analysis is also extended to show robust stability of the guidance law in the presence of saturated lateral acceleration, which is an inherent limitation of flight vehicles. Flight tests of the algorithm, using two small unmanned air vehicles, showed that each aircraft was controlled to within 1.6 m root mean square when following circular paths. The method was used to perform a rendezvous of the two aircraft, bringing them into very close proximity, within 12 m of along track separation and 1.4 m root mean square relative position errors.

Dr. Russ Kunz was known as an early pioneer of fuel cell research in the corporate research environment, carrying out and publishing basic and applied research toward the improvement of specific corporate applications. This talk will describe how similar research on polymer electrolyte membrane fuel cells (PEMFCs), or hydrogen fuel cells, is being developed at the U.S. Naval Research Laboratory (NRL), the corporate research laboratory for the U.S. Navy. The U.S. Navy is interested in unmanned systems for numerous reasons, such as offering reduced manning and creating the ability to carry out missions in environments hostile to humans. However, numerous challenges remain, such as the limited endurance of battery-electric vehicles. At NRL, we targeted the limited endurance of battery-electric unmanned air vehicles (UAVs) proposing hydrogen fuel cells as a path to long-endurance propulsion. NRL developed the Ion Tiger UAV, first using a 1.1-kg 550-Watt fuel cell built by Protonex Technology Corporation in the power train. The Ion Tiger flew for 26 hours on hydrogen fuel cell propulsion in November 2009 using compressed hydrogen gas as the fuel, and 48 hours in April 2013 using liquid hydrogen fuel. More recently, NRL has designed and built in house a flight-weight, modular 1.5 to 5 kW PEMFC using metal bipolar plates as the backbone – first with 3D printed titanium bipolar plates, and more recently using automotive-grade, formed stainless steel bipolar plates. We have also developed the methodology to make catalyst-coated membranes in house to speed development of new designs. The overall success of our hydrogen UAV program is due to advances in materials, electronics, and thermal management, integrated into an aerodynamically efficient package. The resulting fuel cell–powered UAVs are relatively small (35 to 100 lbs) but they are quiet and efficient, allowing them to do “big airplane” missions. The full system approach, with basic and applied research as its foundation, is clearly a tribute to the corporate research approach that Dr. Kunz espoused.

A variety of unmanned air vehicles (UAVs) have been developed for both military and civilian use. The typical large UAV is typically state owned, whereas small UAVs (SUAVs) may be in the form of remote controlled aircraft that are widely available. The potential threat of these SUAVs to both the military and civilian populace has led to research efforts to counter these assets via track, ID, and attack. Difficulties arise from the small size and low radar cross section when attempting to detect and track these targets with a single sensor such as radar or video cameras. In addition, clutter objects make accurate ID difficult without very high resolution data, leading to the use of an acoustic array to support this function. This paper presents a multi-sensor architecture that exploits sensor modes including EO/IR cameras, an acoustic array, and future inclusion of a radar. A sensor resource management concept is presented along with preliminary results from three of the sensors.

MEMS-Based Low-Cost Flight Control System for Small UAVs

An Unmanned Air Vehicle (UAV) is an unmanned, remotely controlled, small air vehicle. It has an important role in anti-surface warfare. This implies over-the-horizon detection, classification, targeting and battle damage assessment. To perform these tasks several UAVs are needed to assist or interchange with each other. An important problem is to determine how many UAVs are needed in this respect. The answer depends on the characteristics of the UAV and its mission. The UAV availability problem is very complex and the usual method to solve such a problem is simulation. A disadvantage of simulation is that it can be very time-consuming. Hence it is not very suitable for sensitivity analysis. Moreover, since simulation gives mere approximations and is not very generic, theoretical insights are hardly gained. In this paper we show how such a complex problem can still be tackled analytically by using a basic model from reliability theory, viz., a 1-out-of-n system with cold standby, ample repair facility and general life time and repair distributions.

Flapping wing unmanned air vehicles (UAVs) are small light weight vehicles that typically have short flight times due to the small size of the batteries that are used to power them. During longer missions, the batteries must be recharged. The lack of nearby electrical outlets severely limits the locations and types of missions that these UAVs can be flown in. To improve flight time and eliminate the need for electrical outlets, solar cells can be used to harvest energy and charge/power the UAV. Robo Raven III, a flapping wing UAV, was developed at the University of Maryland and consists of wings with integrated solar cells. This paper aims to investigate how the addition of solar cells affects the UAV. The changes in performance are quantified and compared using a load cell test as well as Digital Image Correlation (DIC). The UAV platform reported in this paper was the first flapping wing robotic bird that flew using energy harvested from on-board solar cells. Experimentally, the power from the solar cells was used to augment battery power and increase operational time.

Structural health and usage monitoring of unmanned air vehicles is of increasing interest not only for the evaluation of flight loads and the use of data in order to estimate the remaining lifetime of the structure but also for a fast and reliable assessment of structural impacts due to unexpected events that can produce significant loads on the structure, such as hard landings and gusts. This is especially important with composite material structures where damage is quite often barely visible. In-flight load and usage monitoring enables fast turnaround times and increases the availability of the unmanned air vehicle. The Spanish National Institute for Aerospace Technology has been developing, for over two decades now, unmanned air vehicles for surveillance, reconnaissance, and aerial targets. At the moment, a health and usage monitoring system for the Spanish National Institute for Aerospace Technology’s medium altitude and long endurance unmanned air vehicle called MILANO, based on fiber Bragg grating sensors that measure strain and temperature, is being developed. The sensors are integrated in the outer and central wing box and the central and rear fuselage. The health and usage monitoring system is based on the experience gained during flight test activities of the tactical unmanned air vehicle SIVA, an all-composite material unmanned air vehicle developed and assembled by Spanish National Institute for Aerospace Technology. One SIVA is instrumented with 15 fiber Bragg grating sensors that measure both strain and temperature in different structural points of interest, such as the outer wing, the wing attachment, the front and rear fuselage, a stabilizer, and the landing gear. The use of optical sensors instead of electrical sensors has the advantages of lower weight penalty, longer durability, and total immunity to electromagnetic interferences. Structural deformations of the unmanned air vehicle have been measured during flight and used to calculate structural flight loads. Temperature-induced thermal strains have been compensated for. This article explains the results and data evaluation of the performed flight tests, describes the equipment used and the sensor applications, and presents the health and usage monitoring of Spanish National Institute for Aerospace Technology’s tactical and medium altitude and long endurance unmanned air vehicles.

The problem of cooperative obstacle avoidance by a group of unmanned ground vehicles (UGVs) and unmanned air vehicles (UAVs) during a typical search mission is addressed in this paper. The group of UAVs and UGVs are performing a search operation in the designated area. All the UAVs and UGVs are equipped with a vision sensor/LIDAR to identify the possible obstacles in the search space. Due to their operation on the ground, UGVs are more likely to encounter obstacles. The obstacle avoidance for UGV under the event of sensor failure is done with environment information from the nearest UAV. The UAV plans its trajectory according to the UGV’s expected future trajectory, leading towards the base station. The UGV replans its trajectory to avoid obstacles after obtaining the information from its nearest UAV. A simulation study is performed with 10 UAVs and five UGVs performing a search mission in 1[Formula: see text]km[Formula: see text][Formula: see text][Formula: see text]1[Formula: see text]km area. The proposed obstacle avoidance method is experimentally validated in the outdoor environment with an autonomous UAV equipped with a camera and an autonomous UGV navigating based on GPS localization and environment information from the UAV.

Great efforts are devoted for integrating Renewable Energy Sources (RES) on the propulsion system of Unmanned Air Vehicles (UAVs). This is applicable to small UAVs, having a horizon to expand to large UAVs within the next decades. For the conventional propulsion systems to be replaced, the required power needs of the aircraft should first be examined. The required power depends on the UAV's flight stage from takeoff to landing, whereas its calculation varies with regard to the propulsion system type, which is separated into two main categories, the propelled‐driven engines and the jet engines. Two obstacles are arising. First, the parameters needed for the power estimation are commercially sensitive and second the analysis alters with respect to the engine's type. This paper focuses on the mathematical analysis of the required power for both the main propulsion systems, presented extensively for each flight phase, proposing, at the same time, a parameter estimation method which is applicable to any UAV type. The main purpose of this paper is to act as a guide for the calculation of any type UAV's required power at any flight stage. To validate the analysis, the MQ‐9 Reaper/Predator B, a High Altitude Long Endurance (HALE) turboprop UAV produced by General Atomics is analysed. Its unknown parameters are estimated and based on a selected flight profile, the required power in terms of horsepower from takeoff to landing is assessed, for two scenarios regarding the aircraft's initial weight. In the first scenario the UAV has the maximum gross takeoff weight, whereas in the second scenario it does not carry any payload. The estimated required shaft power per flight phase for each scenario is then marked into the TPE331‐10 turboprop engine characteristic curves.

In the era of Declining Defense Dollars, the cost of sophisticated aircraft and highly trained personnel has heightened interest in Unmanned Air Vehicles (UAVs). The obvious lure is the lower vehicle cost (no crew station and crew support systems) and reduced needs for highly skilled air crews. Reconnaissance (commonly called recce) aircraft and their missions are among the commonly sighted applications for UAVs. Today's UAV recce aircraft (such as the Predator) are the genesis of much more sophisticated UAVs of the future. The evolution of the UAV will not be constrained to recce aircraft, but the recce mission will be significant for UAVs. The recce hole has historically been that of a battlefield data collector for post mission review and planning. In the electronic battlefield of the future, that role will be expanded. Envisioned mission for future recce aircraft include real-time scout, target location and fire coordination, battle damage assessment, and large area surveillance. Associated with many of these new roles is the need to store or assess much higher volumes of data. The higher volume data requirements are the result of higher resolution sensors (the Advanced Helicopter Pilotage infrared sensor has a data rate of near 1.2 Gigabits per second) and multi-sensor applications (the Multi-Sensor Aided Targeting program considered infrared, TV, and radar). The evolution of the UAV recce role, and associated increased data storage needs (from higher data rates and increased coverage requirements), requires the development of new data storage equipment. One solution to the increased storage needs is solid-state memory. As solid-state memories become faster, smaller, and cheaper they will enable the UAV recce mission capability to expand. Because of the speed of the memory, it will be possible to buffer and assess (identify the existence of targets or other points of interest) data before committing to consumption of limited storage assets. Faster memory search times and random memory access will permit comparisons of iterative imagery to identify changes (moving-target-indicator or new/missing objects) in real-time. This has three positive effects: (1) Real-time assessment of enemy troops and weapon strengths (coupled with directional data) for planning or fire coordination. (2) Real-time battle damage assessment to accelerate determination of the allocation of friendly assets. (3) Selective storage of imagery (only that which contains points-of-interest) reduces the amount of data storage leading to increased area coverage and mission times. Orbital Sciences Corporation/Fairchild Defense is completing the development of a High-Speed Solid State Recorder which is a critical element of the Enhanced Recce Management System we are developing. The High-Speed Solid State Recorder will be a major part of defining the mission for future UAV recce aircraft.