DAIDALUS: Detect and Avoid Alerting Logic for Unmanned Systems

Muñoz, César; Narkawicz, Anthony; Hagen, George; Upchurch, Jason M.; Dutle, Aaron; Consiglio, Maria; Chamberlain, James P.

doi:10.1109/dasc.2015.7311421

Cited by 82 publications

(63 citation statements)

References 4 publications

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“…[4][5][6][7] Prototype DAA algorithms have also been developed to alerting and guidance research. [8][9][10] These developments enabled the RTCA Special Committee 228 (SC-228) to publish the Minimum Operational Performance Standards (MOPS) for DAA systems 11 and air-to-air radar 12 in 2017. The corresponding Technical Standard Orders (TSO), TSO-C211 and TSO-C212, were published by the Federal Aviation Administration (FAA) in October 2017.…”

Section: Introductionmentioning

confidence: 99%

Well Clear Trade Study for Unmanned Aircraft System Detect And Avoid with Non-Cooperative Aircraft

Cone

Lee

et al. 2018

2018 Aviation Technology, Integration, and Operations Conference

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This paper presents a process for selecting candidate Detect-and-Avoid Well Clear definitions for Unmanned Aircraft Systems and non-cooperative aircraft. The selection is based on considerations of the unmitigated collision risk, maneuver initiation range, and others. Operational assumptions related to low cost, size, weight, and power sensors equipped by the Unmanned Aircraft Systems are applied to set the scope of aircraft performance and drive metrics computation. Four candidate Well Clear definitions, two primary and two secondary, varying from 1500 to 2500 ft in their horizontal miss distance and from 0 to 25 sec in modified tau, are selected for future analyses.

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Section: Introductionmentioning

confidence: 99%

Well Clear Trade Study for Unmanned Aircraft System Detect And Avoid with Non-Cooperative Aircraft

Cone

Lee

et al. 2018

2018 Aviation Technology, Integration, and Operations Conference

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“…ICAROUS is also being developed in a verification-driven manner, as discussed in [11] and [20]. The two portions of ICAROUS that are fully formally verified at the time of writing are DAIDALUS [19], which handles the detection and avoidance of collisions, and PolyCARP, which handles computation behind the exact location of the points in polygons.…”

Section: Limitations Of Uxasmentioning

confidence: 99%

Collaborative UAV Surveillance

Smith

Hexmoor

2019

2019 IEEE International Symposium on Measurement and Control in Robotics (ISMCR)

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“…The resulting flight paths range from flight paths that are tightly controlled by ATC (e.g., "turn heading 300, turn downwind, I will call your base, slow to 70 knots, turn base now") to flight paths whose broad outline is guided by ATC but involve onboard decision making and discretion (e.g., "turn crosswind at your discretion, you are following a Cessna midfield downwind, make room for two departures"). Stratway and AOW are capabilities for detecting and self-spacing from other traffic, built on DAIDALUS [8]. They originated as detect and avoid capabilities, our implementation is based on simulated traffic with ADS-B sensing.…”

Section: Amendment(id(amendment 131) Aircraft_id(n007jb) Enter_at_lmentioning

confidence: 99%

Design Considerations for a Variable Autonomy Exeuctive for UAS in the NAS

Lowry

Bajwa

Pressburger

et al. 2018

2018 AIAA Information Systems-Aiaa Infotech @ Aerospace

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This paper describes research targeted towards an autonomy executive (AOS) for UAS in the National Air Space (NAS). The project goal is to incrementally provide the knowledge and intelligence onboard a UAS to safely fly in the National Air Space, eventually autonomous from remote human ground crews and communicating directly with air traffic control. Longer-term, the goal is to provide the capability for pilotless air vehicles such as air taxis that will be key for new transportation concepts such as air mobility-on-demand. For both of these targeted applications, AOS is incorporating artificial intelligence capabilities that operationally meet human pilot competencies. Even when autonomy is achieved from a remote human ground crew, AOS will have variable degrees of autonomy with respect to air traffic control (ATC), just as human pilots do now. AOS has the capability of interacting in natural language with ATC, as well as through data link protocols. AOS can adapt to varying levels of autonomy and control directed by ATC in standard and relaxed FAA phraseology-from being vectored moment by moment, to accepting broad directives such as following a specified aircraft or sighting and avoiding traffic. AOS can autonomously manage contingencies such as vehicle systems degradations and failures. It incorporates a decision maker that takes information from multiple diagnostic reasoners, disambiguates (if needed) sensor results to specific failures using active mode changes, then projects forward the impact of the degradation on the nominal plan. If the nominal plan is no longer viable, then alternative plans are formulated, and subsequently selected and executed, including abort options. https://ntrs.nasa.gov/search.jsp?R=20180004247 2019-07-07T08:54:28+00:00Z I. Nomenclature DF = a leg type for waypoint to waypoint navigation, part of ARINC 424 HIL = Hardware Integration Laboratory IVHM = Integrated Vehicle Health Management MTL = Metric Temporal Logic NAS = National Air Space SIL = Software Integration Laboratory UAS = Unmanned Aircraft System UTM = UAS Traffic Management VI = a leg type for intercept to course, part of ARINC 424

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DAIDALUS: Detect and Avoid Alerting Logic for Unmanned Systems

Cited by 82 publications

References 4 publications

Well Clear Trade Study for Unmanned Aircraft System Detect And Avoid with Non-Cooperative Aircraft

Well Clear Trade Study for Unmanned Aircraft System Detect And Avoid with Non-Cooperative Aircraft

Collaborative UAV Surveillance

Design Considerations for a Variable Autonomy Exeuctive for UAS in the NAS

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