Multi-Mode Guidance for an Independent Multicopter Geofencing System

Stevens, Mia N.; Atkins, Ella M.

doi:10.2514/6.2016-3150

Cited by 26 publications

(8 citation statements)

References 7 publications

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“…Geofencing is the creation of virtual fences around areas or points of interest to keep drones away from no-fly zones [ 32 ]. Substantial work has already been published to define and realize geofencing systems for small UAS [ 32 , 33 , 34 , 35 , 36 ]. Popular autopilot systems currently offer simple containment volume geofences over critical areas, including airports [ 33 ].…”

Section: Literature Review On Counter-drone (C-uas) Technologiesmentioning

confidence: 99%

“…Popular autopilot systems currently offer simple containment volume geofences over critical areas, including airports [ 33 ]. A geofence could be dynamically generated, as in a radius around a location point with predefined set of boundaries [ 34 , 35 , 36 ]. It is an effective preventing measure when built into a UAV’s navigation software [ 37 ].…”

Section: Literature Review On Counter-drone (C-uas) Technologiesmentioning

confidence: 99%

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Defending Airports from UAS: A Survey on Cyber-Attacks and Counter-Drone Sensing Technologies

Lykou

Moustakas

Gritzalis

2020

Sensors

178

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As the fastest growing segment of aviation, unmanned aerial systems (UAS) continue to increase in number, technical complexity and capabilities. Numerous civilian and commercial uses are drastically transforming civil protection, asset delivery, commercial and entertaining activities. However, UAS pose significant challenges in terms of safety, security and privacy within society. An increasing phenomenon, nowadays, is drone-related incidents near airport facilities, which are expected to proliferate in frequency, complexity and severity, as drones become larger and more powerful. Critical infrastructures need to be protected from such aerial attacks, through effective counteracting technologies, risk management and resilience plans. In this paper, we present a survey of drone incidents near airports and a literature review of sensor technologies, able to prevent, detect, identify and mitigate rogue drones. We exhibit the benefits and limitations of available counter-drone technologies (C-UAS); however, defending airports against misused drone activity is a hard problem. Therefore, we analyze three realistic attack scenarios from malicious drones and propose an effective C-UAS protection plan for each case. We discuss applicability limitations of C-UAS in the aviation context and propose a resilience action plan for airport stakeholders for defending against airborne threats from misused drones.

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Section: Literature Review On Counter-drone (C-uas) Technologiesmentioning

confidence: 99%

Section: Literature Review On Counter-drone (C-uas) Technologiesmentioning

confidence: 99%

Defending Airports from UAS: A Survey on Cyber-Attacks and Counter-Drone Sensing Technologies

Lykou

Moustakas

Gritzalis

2020

Sensors

178

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“…If the multicopter loses a navigation (e.g., GPS) signal or detects a critically low battery level, the multicopter will typically initiate an automatic landing protocol. 2 Electronic geofencing has also been proposed to prevent fly-away, 3,4 but to-date has relied on user entry or a sparse database of restricted sites, e.g., the White House lawn. This paper proposes a set of map-based fail-safe protocols that enable a multicopter experiencing an anomaly requiring a near-term landing, e.g., low battery energy, to safely terminate its flight in an urban environment where an immediate fail-safe landing poses risk to people on the ground.…”

Section: Introductionmentioning

confidence: 99%

Fail-Safe Navigation for Autonomous Urban Multicopter Flight

Ochoa

Atkins

2017

AIAA Information Systems-Aiaa Infotech @ Aerospace

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Current multicopter fail-safe protocols respond with pre-programmed behaviors that aim to minimize possible damage to the aircraft or avoid risk to the nearby population. Existing fail-safe protocols for multicopters involve either returning to a pre-designated home point or executing an automatic landing protocol. Such strategies are rigid and can even increase risk in cases with complex terrain or high overflown population densities. This paper examines three alternative fail-safe protocols for autonomous urban multicopter flight that trade the simplicity of existing protocols with responsiveness to terrain, multicopter state, and overflown population. We utilize nontraditional data and sensor fusion to make an informed landing site selection with a focus on building rooftops as safe urban landing sites. The first protocol assigns a "Clearance to Land" flag to all buildings to determine the nearest rooftop in the area that can serve as a feasible landing site. The second protocol first determines a possible landing site within the multicopter's reachable footprint using lot types provided by a property database. During traversal to that site the multicopter examines the rooftops of overflown buildings and generates an alternative landing plan if it discovers a viable rooftop not previously mapped. The third protocol utilizes the known building locations and their approximate geometries as specified by the database to generate a spanning tree coverage solution followed by the multicopter to search for a viable landing site. A feature-based map of Manhattan generated from the PLUTO and MapPLUTO tax lot databases is used for simulations of all three protocols.

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“…For example, geofences used in commercial UAS are typically embedded with the autopilot, using the same computer processor and operating system. For autopilots that lack the provenance of conventional avionics systems (e.g., open source autopilots), establishing confidence in the reliability of the geofence can be challenging [11], [12]. Further, most geofencing functions rely on global navigation satellite systems (GNSS) for positioning.…”

Section: Introductionmentioning

confidence: 99%

SAFEGUARD: Progress and test results for a reliable independent on-board safety net for UAS

Gilabert

Dill

Hayhurst

et al. 2017

2017 IEEE/AIAA 36th Digital Avionics Systems Conference (DASC)

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As demands increase to use unmanned aircraft systems (UAS) for a broad spectrum of commercial applications, regulatory authorities are examining how to safely integrate them without compromising safety or disrupting traditional airspace operations. For small UAS, several operational rules have been established; e.g., do not operate beyond visual line-ofsight, do not fly within five miles of a commercial airport, do not fly above 400 ft above ground level. Enforcing these rules is challenging for UAS, as evidenced by the number of incident reports received by the Federal Aviation Administration (FAA). This paper reviews the development of an onboard system -Safeguarddesigned to monitor and enforce conformance to a set of operational rules defined prior to flight (e.g., geospatial stay-out or stay-in regions, speed limits, and altitude constraints). Unlike typical geofencing or geo-limitation functions, Safeguard operates independently of the off-the-shelf UAS autopilot and is designed in a way that can be realized by a small set of verifiable functions to simplify compliance with existing standards for safety-critical systems (e.g. for spacecraft and manned commercial transportation aircraft systems). A framework is described that decouples the system from any other devices on the UAS as well as introduces complementary positioning source(s) for applications that require integrity and availability beyond what can be provided by the Global Positioning System (GPS). This paper summarizes the progress and test results forSafeguard research and development since presentation of the design concept at the 35th DASC (2016). Significant accomplishments include completion of software verification and validation in accordance with NASA standards for spacecraft systems (to Class B), development of improved hardware prototypes, development of a simulation platform that allows for hardware-in-the-loop testing and fast-time Monte Carlo evaluations, and flight testing on multiple air vehicles. Integration testing with NASA's UAS Traffic Management (UTM) service-oriented architecture was also demonstrated.

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Multi-Mode Guidance for an Independent Multicopter Geofencing System

Cited by 26 publications

References 7 publications

Defending Airports from UAS: A Survey on Cyber-Attacks and Counter-Drone Sensing Technologies

Defending Airports from UAS: A Survey on Cyber-Attacks and Counter-Drone Sensing Technologies

Fail-Safe Navigation for Autonomous Urban Multicopter Flight

SAFEGUARD: Progress and test results for a reliable independent on-board safety net for UAS

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