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Collection of theoretical and experimental studies covering basic mechanisms of fatigue in the subcreep and creep ranges, fatigue damage detection, field practices for fatigue damage repair in jet engine components, and fatigue damage avoidance and control. Individual items are abstracted in this issue.
The DAIDALUS Detect and Avoid Algorithm [1] was developed to address the operational needs of Unmanned Aerial Systems (UAS) and meet the Minimum Operational Performance Standards for Detect and Avoid [2]. The DANTi (Detect and Avoid iN the cockpit) concept [3], developed at the National Aeronautics and Space Administration (NASA) Langley Research Center, leverages advancements achieved in surveillance and Detect and Avoid technologies for unmanned aircraft systems as a safety enhancing capability for pilots of manned aircraft. Pilots operating under Visual Flight Rules and not receiving Air Traffic Control radar services rely on see and avoid to remain well clear of other aircraft and avoid collisions. The DANTi concept has been conceived as a safety enhancement capability to remain well clear and avoid potential collisions. The DANTi concept uses a traffic display to provide situational awareness, conflict detection, alerting, and guidance to remain well clear.
The Detect and Avoid (DAA) capability of a recent version (Run 3) of the Airborne Collision Avoidance System-Xu (ACAS-Xu) is measured against that of the Detect and AvoID Alerting Logic for Unmanned Systems (DAIDALUS), a reference algorithm for the Phase 1 Minimum Operational Performance Standards (MOPS) for DAA. This comparative analysis of the two systems' alerting and horizontal guidance outcomes is conducted through the lens of the Detect and Avoid mission using flight data of scripted encounters from a recent flight test. Results indicate comparable timelines and outcomes between ACAS-Xu's Remain Well Clear alert and guidance and DAIDALUS's corrective alert and guidance, although ACAS-Xu's guidance appears to be more conservative. ACAS-Xu's Collision Avoidance alert and guidance occurs later than DAIDALUS's warning alert and guidance, and overlaps with DAIDALUS's timeline of maneuver to remain Well Clear. Interesting discrepancies between ACAS-Xu's directive guidance and DAIDALUS's "Regain Well Clear" guidance occur in some scenarios.
Aircraft not receiving radar services rely on see and avoid and radio coordination via Common Traffic Advisory Frequencies to remain well clear of each other and avoid mid-air collisions. Radio coordination is usually performed in the vicinity of non-towered airports whereas non-radar services en-route operations rely solely on see and avoid. This paper presents the results of a simulation study of the effectiveness of assistive detect and avoid technologies when used to enhance pilots’ ability to see and avoid nearby traffic. Three different experimental conditions are modeled, representing “unaided see and avoid”, “see and avoid with traffic advisories”, and “see and avoid with assistive detect and avoid technology”. The effectiveness of see and avoid is evaluated using a set of head-on, crossing, and overtaking encounter scenarios and a model of visual acquisition embedded in a Monte Carlo simulation. The effectiveness of assistive detect and avoid is estimated for the same encounter scenarios. A prototype system for detect and avoid and a summary of results are presented. Preliminary results strongly suggest that assistive detect and avoid could greatly enhance the capabilities of flight crews to avoid traffic and remain well clear.
Aircraft not receiving radar services rely on see and avoid and radio coordination via Common Traffic Advisory Frequencies to remain well clear of each other and avoid mid-air collisions. Radio coordination is usually performed in the vicinity of non-towered airports whereas non-radar services en-route operations rely solely on see and avoid. This paper presents the results of a simulation study of the effectiveness of assistive detect and avoid technologies when used to enhance pilots’ ability to see and avoid nearby traffic. Three different experimental conditions are modeled, representing “unaided see and avoid”, “see and avoid with traffic advisories”, and “see and avoid with assistive detect and avoid technology”. The effectiveness of see and avoid is evaluated using a set of head-on, crossing, and overtaking encounter scenarios and a model of visual acquisition embedded in a Monte Carlo simulation. The effectiveness of assistive detect and avoid is estimated for the same encounter scenarios. A prototype system for detect and avoid and a summary of results are presented. Preliminary results strongly suggest that assistive detect and avoid could greatly enhance the capabilities of flight crews to avoid traffic and remain well clear.
This paper describes a Detect and Avoid (DAA) concept for integration of UAS into the NAS developed by the National Aeronautics and Space Administration (NASA) and provides results from recent human-in-the-loop experiments performed to investigate interoperability and acceptability issues associated with these vehicles and operations. The series of experiments was designed to incrementally assess critical elements of the new concept and the enabling technologies that will be required.
Unmanned Aircraft Systems need to be able to comply with manned aviation ‘see and avoid’ separation requirements. A Detect and Avoid (DAA) System includes sensors, a tracker, and alerting and guidance algorithms that assist a remote pilot in maintaining separation from airborne traffic. To date, DAA system requirements development has focused on operations transiting to and from Class A or special use airspace. Current efforts are defining DAA system requirements for operations in and around terminal airspace. As a contribution to the current efforts, this paper highlights results from a Human in the Loop (HiTL) experiment comparing methods of changing from the transit-specific alerting and guidance criteria to proposed terminal-specific alerting and guidance criteria. It discusses operational considerations that are beyond the HiTL.
Landing hazard detection and avoidance technology is being pursued within NASA to improve landing safety and increase access to sites of interest on the lunar surface. The performance of a hazard detection and avoidance system depends on properties of the terrain, sensor performance, algorithm design, vehicle characteristics and the overall all guidance navigation and control architecture. This paper analyzes the size of the region that must be imaged, sensor performance parameters and the impact of trajectory angle on hazard detection performance. The analysis shows that vehicle hazard tolerance is the driving parameter for hazard detection system design.
Java Architecture for Detect-And-Avoid (DAA) Extensibility and Modeling (JADEM) was developed at NASA Ames Research Center as a research and modeling tool for Unmanned Aircraft Systems (UAS) Integration in the National Airspace System (NAS). UAS will be required to have DAA systems in order to fulfill the regulatory requirement to remain well clear'' of other traffic. JADEM supports research on technological requirements and Minimum Operational Performance Standards (MOPS) for UAS DAA systems by providing a flexible and extensible software platform that includes models and algorithms for all major DAA functions. This paper describes one of these algorithms, the Generic Resolution Advisor and Conflict Evaluator (GRACE). GRACE supports two core DAA functions: threat evaluation and guidance. GRACE is generic in the sense that it is designed to work with any aircraft or sensor type (both cooperative and non-cooperative), and to be used in various applications and DAA guidance concepts, thus supporting evolving MOPS requirements and research. GRACE combines flexibility, robustness, and computational efficiency. It has modest memory requirements and can handle multiple cooperative and noncooperative intruders. GRACE has been used as a core JADEM component in several real-time and fast-time experiments, including human-in-the-loop simulations and live flight tests.
The latest variant of the Federal Aviation Administration’s Airborne Collision Avoidance System (ACAS X) is being designed for both crewed and uncrewed rotorcraft. Referred to as ACAS Xr, the system joins a suite of other ACAS X variants poised to replace the Traffic Alert and Collision Avoidance System (TCAS II). ACAS Xr is tuned to support current-day helicopter platforms as well as electric Vertical Takeoff and Landing (eVTOL) vehicles that are still under development. Given this flexibility, ACAS Xr may be used by helicopter crews currently in operation or by remotely-operated eVTOL aircraft in the emerging Advanced Air Mobility (AAM) market. To cover the range of potential uses, two distinct configurations are being proposed for ACAS Xr: Collision Avoidance System (CAS) and Detect and Avoid (DAA). Under the CAS configuration, ACAS Xr provides minimal caution-level alerting but issues directive warning-level alerting and guidance. The DAA configuration, by contrast, provides caution-level alerting and guidance, in addition to the warning-level alerting and guidance. The current study was performed as part of the National Aeronautics and Space Administration’s AAM project. Six helicopter pilots were recruited to fly a variety of scripted traffic scenarios in a full-motion, crewed eVTOL simulator. Participants flew 60 encounters over two days, reacting to pre-recorded intruder aircraft that were scripted to fly into the participant’s aircraft from different approach angles, relative altitudes, and during different phases of flight. The pilots flew half of the encounters with the CAS configuration and half with the DAA configuration. Within each block of 30 encounters, pilots experienced 10 conflicts while in cruise, 10 in hover, and 10 while on approach to a heliport. Results showed that pilot response times were consistently under 5 seconds for RAs and under 10 seconds for DAA alerts, when present, during all three phases of flight. Unsurprisingly, the DAA configuration was associated with lower rates of en-route and high-severity losses of DAA well clear compared to the CAS configuration in all phases of flight except for the terminal area. Rates of losses of DAA well clear were found to be substantially higher in the Hover scenario, compared to Cruise. Pilots failed to fully comply with RAs at a rate of 0.10-0.18 in all conditions except for the DAA configuration in the Hover scenario, which was associated with a higher non-compliance rate of 0.4 due to Descend RAs issued at low altitudes. The implications of these results with regards to the ongoing development of ACAS Xr is discussed.
The latest variant of the Federal Aviation Administration’s Airborne Collision Avoidance System (ACAS X) is being designed for both crewed and uncrewed rotorcraft. Referred to as ACAS Xr, the system joins a suite of other ACAS X variants poised to replace the Traffic Alert and Collision Avoidance System (TCAS II). ACAS Xr is tuned to support current-day helicopter platforms as well as electric Vertical Takeoff and Landing (eVTOL) vehicles that are still under development. Given this flexibility, ACAS Xr may be used by helicopter crews currently in operation or by remotely-operated eVTOL aircraft in the emerging Advanced Air Mobility (AAM) market. To cover the range of potential uses, two distinct configurations are being proposed for ACAS Xr: Collision Avoidance System (CAS) and Detect and Avoid (DAA). Under the CAS configuration, ACAS Xr provides minimal caution-level alerting but issues directive warning-level alerting and guidance. The DAA configuration, by contrast, provides caution-level alerting and guidance, in addition to the warning-level alerting and guidance. The current study was performed as part of the National Aeronautics and Space Administration’s AAM project. Six helicopter pilots were recruited to fly a variety of scripted traffic scenarios in a full-motion, crewed eVTOL simulator. Participants flew 60 encounters over two days, reacting to pre-recorded intruder aircraft that were scripted to fly into the participant’s aircraft from different approach angles, relative altitudes, and during different phases of flight. The pilots flew half of the encounters with the CAS configuration and half with the DAA configuration. Within each block of 30 encounters, pilots experienced 10 conflicts while in cruise, 10 in hover, and 10 while on approach to a heliport. Results showed that pilot response times were consistently under 5 seconds for RAs and under 10 seconds for DAA alerts, when present, during all three phases of flight. Unsurprisingly, the DAA configuration was associated with lower rates of en-route and high-severity losses of DAA well clear compared to the CAS configuration in all phases of flight except for the terminal area. Rates of losses of DAA well clear were found to be substantially higher in the Hover scenario, compared to Cruise. Pilots failed to fully comply with RAs at a rate of 0.10-0.18 in all conditions except for the DAA configuration in the Hover scenario, which was associated with a higher non-compliance rate of 0.4 due to Descend RAs issued at low altitudes. The implications of these results with regards to the ongoing development of ACAS Xr is discussed.
Most unmanned aircraft systems will be required to be equipped with a detect-and-avoid system that is capable of maintaining appropriate separation from other aircraft. One of the critical components of detect-and-avoid systems is a surveillance system that identifies potential threat aircraft in real time and tracks these aircraft so that their future trajectories may be used to predict conflicts. The performance of the detect-and-avoid system generally depends on technical parameters of the surveillance system, such as the surveillance range. The quantitative requirements for detect-and-avoid systems will be determined to meet safety metrics for the operation of unmanned aircraft systems in the National Airspace System. This study employs a sensor model comprised of the surveillance range, and horizontal and vertical fields of regard that mainly characterize the overall performance of a surveillance system. In this study, potential metrics for evaluating the performance of a surveillance system were investigated through fast-time simulation with a traffic scenario that included both proposed unmanned aircraft flights and historical visual flight rule aircraft tracks. Using the simulation results, an overall analysis of encounter geometry highlights the encounter characteristics that relate surveillance parameters to safety metrics and detect-and-avoid system performance. Then, given several candidate surveillance volumes, performance and safety metrics are derived; these metrics include the ratio of undetected and late-detected violations and the time to violation at first detection. These example metrics demonstrate the utility of the database of encounters created in this work, a database which will be useful in the derivation of required detect-and-avoid surveillance system requirements.
This presentation is a review of detect and avoid (DAA) research conducted over the last five years in support of RTCCA SC 228.
Unmanned aircraft will equip with a detect-and-avoid (DAA) system that enables them to comply with the requirement to "see and avoid" other aircraft, an important layer in the overall set of procedural, strategic and tactical separation methods designed to prevent mid-air collisions. This paper describes a capability called Java Architecture for Detect and Avoid Extensibility and Modeling (JADEM), developed to prototype and help evaluate various DAA technological requirements by providing a flexible and extensible software platform that models all major detect-and-avoid functions. Figure 1 illustrates JADEM's architecture. The surveillance module can be actual equipment on the unmanned aircraft or simulators that model the process by which sensors on-board detect other aircraft and provide track data to the traffic display. The track evaluation function evaluates each detected aircraft and decides whether to provide an alert to the pilot and its severity. Guidance is a combination of intruder track information, alerting, and avoidance/advisory algorithms behind the tools shown on the traffic display to aid the pilot in determining a maneuver to avoid a loss of well clear. All these functions are designed with a common interface and configurable implementation, which is critical in exploring DAA requirements. To date, JADEM has been utilized in three computer simulations of the National Airspace System, three pilot-in-the-loop experiments using a total of 37 professional UAS pilots, and two flight tests using NASA's Predator-B unmanned aircraft, named Ikhana. The data collected has directly informed the quantitative separation standard for "well clear", safety case, requirements development, and the operational environment for the DAA minimum operational performance standards. This work was performed by the Separation Assurance/Sense and Avoid Interoperability team under NASA's UAS Integration in the NAS project.
Minimum display requirements for Detect-and-Avoid (DAA) systems are being developed in order to support the expansion of Unmanned Aircraft Systems (UAS) into the National Airspace System (NAS). The present study examines UAS pilots subjective assessments of four DAA display configurations with varying forms of maneuver guidance. For each configuration, pilots rated the intuitiveness of the display and how well it supported their ability to perform the DAA task. Responses revealed a clear preference for the DAA displays that presented suggestive maneuver guidance in the form of banding compared to an Information Only display, which lacked any maneuver guidance. Implications on DAA display requirements, as well as the relation between the subjective evaluations and the objective performance data from previous studies are discussed.
This paper presents DAIDALUS (Detect and Avoid Alerting Logic for Unmanned Systems), a reference implementation of a detect and avoid concept intended to support the integration of Unmanned Aircraft Systems into civil airspace. DAIDALUS consists of self-separation and alerting algorithms that provide situational awareness to UAS remote pilots. These algorithms have been formally specified in a mathematical notation and verified for correctness in an interactive theorem prover. The software implementation has been verified against the formal models and validated against multiple stressing cases jointly developed by the US Air Force Research Laboratory, MIT Lincoln Laboratory, and NASA. The DAIDALUS reference implementation is currently under consideration for inclusion in the appendices to the Minimum Operational Performance Standards for Unmanned Aircraft Systems presently being developed by RTCA Special Committee 228.
Impact of sensor noise on the performance of Detect-And-Avoid (DAA) systems can be reduced by implementing various mitigation schemes. This paper evaluates the Sensor Uncertainty Mitigation (SUM) method, implemented in the Detect and Avoid Alerting Logic for Unmanned Systems (DAIDALUS) algorithm, a reference implementation in the DAA minimum operational performance standards. DAIDALUS SUM performance is evaluated using a few safety and operational suitability metrics and compared with more traditional approaches using static safety buffers. A large number of encounters representative of low-speed unmanned aircraft against non-cooperative manned aircraft are simulated and evaluated. An air-to-air radar model produces representative sensor noise for the DAA system. Results show that increasing the tunable parameters for horizontal and vertical uncertainty in DAIDALUS SUM improves the safety metric at the cost of increasing the number of system alerts leading to increased workload. A range of SUM parameters is recommended as suitable values for the type of operations considered for this work. General trends and optimal SUM configurations were found to be nearly the same for two large and very different encounter data sets.