UAS Sense and Avoid Development - the Challenges of Technology, Standards, and Certification

Cook, Stephen P.; Lacher, Andrew; Maroney, David; Zeitlin, Andrew

doi:10.2514/6.2012-959

Cited by 13 publications

(6 citation statements)

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“…In this framework, the Italian Aerospace Research Center (CIRA) is also developing a DAA system prototype, named ADAPT (Advanced Detect and Avoid ProTotype) [6], as part of a national funded research program on fully autonomous aerospace vehicles (PRORA). ADAPT includes a Collision Detection and Avoidance (CDA) function that leverages and extends some results of the most recent projects and research activities on the subject [7][8][9][10][11][12][13], trying to overcome some of the above-mentioned limitations of DAA systems.…”

Section: Introductionmentioning

confidence: 99%

Unmanned Aircraft Collision Detection and Avoidance for Dealing with Multiple Hazards

Corraro

Cuciniello

et al. 2022

Aerospace

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Collision Detection and Avoidance is one of the critical technologies for fully allowing Unmanned Aerial Systems to fly in civil airspaces. Current methods evaluate only potential conflicts with other aircraft using specific parameters (e.g., time or distance to closest point of approach) that can only be used for pair-wise encounters, not considering the surrounding environment. The present work proposes a new collision detection and avoidance concept to solve short-term conflicts in scenarios characterized by the simultaneous presence of aircraft and other path constraints (i.e., no-fly zones, bad weather areas and terrain) including geo-fencing limitations. Differently from other open literature methods, the proposed algorithm computes two parameters that synthetically describe the conflict hazard level of a given scenario and its possible evolution, independently from the type and the number of surrounding potential threats. Using such indices, a risk evaluation strategy is proposed that detects hazardous situations and generates an optimal maneuver avoiding potential collisions while not causing secondary conflicts. The effectiveness of the proposed algorithm is demonstrated by means of fast-time and real time simulations in some challenging conflict scenarios that cannot be solved by state of the art Detect and Avoid systems.

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Section: Introductionmentioning

confidence: 99%

Unmanned Aircraft Collision Detection and Avoidance for Dealing with Multiple Hazards

Corraro

Cuciniello

et al. 2022

Aerospace

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“…The emerging need to integrate Unmanned Aircraft Systems (UAS) traffic in the existing airspace bears notable challenges in terms of increased traffic density and complexity in low-altitude airspace [1,2]. In 2012, the Federal Aviation Administration's (FAA) Modernization and Reform Act prompted research into the field of managing small UAS (sUAS) as civilian demand for these systems had grown significantly [3]. This initiative was pertinent to urban operations [4][5][6] and set foundations for successive research efforts in this field.…”

Section: Introductionmentioning

confidence: 99%

A Performance-Based Airspace Model for Unmanned Aircraft Systems Traffic Management

et al. 2020

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Recent evolutions of the Unmanned Aircraft Systems (UAS) Traffic Management (UTM) concept are driving the introduction of new airspace structures and classifications, which must be suitable for low-altitude airspace and provide the required level of safety and flexibility, particularly in dense urban and suburban areas. Therefore, airspace classifications and structures need to evolve based on appropriate performance metrics, while new models and tools are needed to address UTM operational requirements, with an increasing focus on the coexistence of manned and unmanned Urban Air Mobility (UAM) vehicles and associated Communication, Navigation and Surveillance (CNS) infrastructure. This paper presents a novel airspace model for UTM adopting Performance-Based Operation (PBO) criteria, and specifically addressing urban airspace requirements. In particular, a novel airspace discretisation methodology is introduced, which allows dynamic management of airspace resources based on navigation and surveillance performance. Additionally, an airspace sectorisation methodology is developed balancing the trade-off between communication overhead and computational complexity of trajectory planning and re-planning. Two simulation case studies are conducted: over the skyline and below the skyline in Melbourne central business district, utilising Global Navigation Satellite Systems (GNSS) and Automatic Dependent Surveillance-Broadcast (ADS-B). The results confirm that the proposed airspace sectorisation methodology promotes operational safety and efficiency and enhances the UTM operators’ situational awareness under dense traffic conditions introducing a new effective 3D airspace visualisation scheme, which is suitable both for mission planning and pre-tactical UTM operations. Additionally, the proposed performance-based methodology can accommodate the diversity of infrastructure and vehicle performance requirements currently envisaged in the UTM context. This facilitates the adoption of this methodology for low-level airspace integration of UAS (which may differ significantly in terms of their avionics CNS capabilities) and set foundations for future work on tactical online UTM operations.

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“…Other informative sources include research into general multiagent systems, such as those discussed in artificial intelligence research [23]. There is also comprehensive research on related work involving collision avoidance, see [2], [7], [9], and [8], UAS swarms, see [26], [5], [4], and [22], and other interesting UAS developments, all of which tangentially influence and balance this research. These topics are not included in detail since this research is not focusing on collision avoidance systems, air space regulation and policy, or emerging techniques like sense and avoid.…”

Section: Introductionmentioning

confidence: 99%

Efficient partitioning of space for multiple UAS search in an unobstructed environment

Schuldt

Kurucar

2018

Int J Intell Robot Appl

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The advent of Unmanned Aerial Systems (UAS) has created opportunities to replace expensive capital assets with these small, yet capable, platforms. Tasks that are identified as able to be performed by UAS may also benefit from the ability of a collection of UAS to operate in a cooperative and parallel manner. Parallelization means that several parts of the search area can be covered at the same time, reducing the overall task completion time. In this paper we investigate how to divide the maritime search and rescue task in a way that it can be performed by a set of UAS. Our investigation covers the detection of multiple mobile objects by a collection of UAS. Three methods (two that are not informed by object location probabilities and one that is) for dividing the space are proposed, and their relative strengths and weaknesses investigated. A reduced aerial camera model facilitates the simulation of object detection. The topic is approached holistically to account for contingencies such as airspace deconfliction. Results are produced using simulation to verify the capability of the proposed method and to compare the various partitioning methods. Results from this simulation show that great gains in search efficiency can be made when the search space is partitioned using a method based on object location probability. For search ar-

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UAS Sense and Avoid Development - the Challenges of Technology, Standards, and Certification

Cited by 13 publications

References 0 publications

Unmanned Aircraft Collision Detection and Avoidance for Dealing with Multiple Hazards

Unmanned Aircraft Collision Detection and Avoidance for Dealing with Multiple Hazards

A Performance-Based Airspace Model for Unmanned Aircraft Systems Traffic Management

Efficient partitioning of space for multiple UAS search in an unobstructed environment

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