A Fuzzy Logic Approach for Low Altitude UAS Traffic Management (UTM)

Cook, Brandon; Cohen, Kelly; Kivelevitch, Elad H.

doi:10.2514/6.2016-1905

Cited by 10 publications

(9 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Eqs. (10) and (11) describe the input-output relationships for modes 1 and 3, respectively, for the go behind FIS.…”

Section: Modern Fuzzy Control Systems and Its Applicationsmentioning

confidence: 99%

“…For more on the mission types, please refer to Refs. [11,12]. A Fuzzy Logic Approach for Separation Assurance and Collision Avoidance for Unmanned Aerial Systems http://dx.doi.org/10.5772/68126…”

Section: Mission Types and Objectivesmentioning

confidence: 99%

“…However, most methods necessitate the communication of state information between the vehicles in order to properly select resolution actions [6]. For collision avoidance, several intelligent systems have been developed with promising results [7][8][9][10][11][12], but few have also shown behavioral verification using formal methods [13,14].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Fuzzy Logic Approach for Separation Assurance and Collision Avoidance for Unmanned Aerial Systems

Cook¹,

Arnett²,

Cohen³

2017

Modern Fuzzy Control Systems and Its Applications

Self Cite

View full text Add to dashboard Cite

In the coming years, operations in low altitude airspace will vastly increase as the capabilities and applications of small unmanned aerial systems (sUAS) continue to multiply. Therefore, finding solutions to managing sUAS in highly congested airspace will facilitate sUAS operations. In this study, a fuzzy logic-based approach was used to help mitigate the risk of collisions between aircraft using separation assurance and collision avoidance techniques. The system was evaluated for its effectiveness at mitigating the risk of mid-air collisions between aircraft. This system utilizes only current state information and can resolve potential conflicts without knowledge of intruder intent. The avoidance logic was verified using formal methods and shown to select the correct action in all instances. Additionally, the fuzzy logic controllers were shown to always turn the vehicles in the correct direction. Numerical testing demonstrated that the avoidance system was able to prevent a mid-air collision between two sUAS in all tested cases. Simulations were also performed in a three-dimensional environment with a heterogeneous fleet of sUAS performing a variety of realistic missions. Simulations showed that the system was 99.98% effective at preventing mid-air collisions when separation assurance was disabled (unmitigated case) and 100% effective when enabled (mitigated case).

show abstract

“…Eqs. (10) and (11) describe the input-output relationships for modes 1 and 3, respectively, for the go behind FIS.…”

Section: Modern Fuzzy Control Systems and Its Applicationsmentioning

confidence: 99%

Section: Mission Types and Objectivesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Fuzzy Logic Approach for Separation Assurance and Collision Avoidance for Unmanned Aerial Systems

Cook¹,

Arnett²,

Cohen³

2017

Modern Fuzzy Control Systems and Its Applications

Self Cite

View full text Add to dashboard Cite

show abstract

“…al. 11 defined a set of conflict resolution rules and tested them in simulations with multiple sUASs. Jenie et.…”

Section: Literature Reviewmentioning

confidence: 99%

“…These unique characteristics demand both sUASs' dynamic sytem models and controller models for an effective UTM fast-time simulation platform, such that evaluations on this platform can provide sufficient accuracy, especially for dense operations. Although recent research 11,12,15,16 started to expand vehicles' speed ranges in an attempt to represent small UASs or multi-rotor vehicles, negligence of modeling sUAS controllers will yield inaccurate trajectory predictions, especially at low altitude airspace where wind changes very often, eventually lead to invalid simulations. Therefore, besides the vehicle dynamic sysmte, vehicle's control system needs to be modeled as well.…”

Section: Literature Reviewmentioning

confidence: 99%

Initial Study of An Effective Fast-time Simulation Platform for Unmanned Aircraft System Traffic Management

Xue

Rios

2017

17th AIAA Aviation Technology, Integration, and Operations Conference

View full text Add to dashboard Cite

Small unmanned aircraft systems are envisioned to play a major role in surveilling critical assets, collecting data, and delivering goods. Large scale operations are expected to happen in low altitude airspace in the near future, where many static and dynamic constraints exist. High sensitivity to wind and high maneuverability are unique characteristics of these vehicles, which bring great challenges to effective system evaluations and mandate such a simulation platform different from existing simulations that were built for manned air traffic system and large unmanned fixed-wing aircraft. NASA's Unmanned aircraft system Traffic Management (UTM) research initiative focuses on enabling fair, safe, and efficient unmanned aircraft system operations in the future. In order to help define requirements and policies for a safe and efficient UTM system to accommodate a large amount of unmanned aerial vehicle operations, it is necessary to develop a fast-time simulation platform that can effectively evaluate policies and concepts, and perform parameter studies in a close-to-reality environment. This work analyzed the impacts of some key factors and demonstrated the importance of these factors in a successful UTM fast-time simulation platform. Preliminary experiments were also conducted to show potentail applications of such a platform. I. IntroductionThe volume of small Unmanned Aircraft System(sUAS) operations is expected to increase dramatically in the near future. 1 Potential sUAS applications include, but not limited to, search and rescue, inspection and surveillance, aerial photography and video, precision agriculture, and parcel delivery. According to the marketing analysis 2 , the global small UAS market is anticipated to hit 10 billion by 2020. FAA also forecasted that over 7 million sUASs will be sold annually by 2020. 1 The sUAS's low operational altitude, small size, and envisioned scale of operations make Unmanned aircraft system Traffic Management (UTM) quite different from conventional aviation traffic management. In the low altitude airspace, besides fast-changing wind conditions, restricted areas, manned aircraft/helicopters, and tall buildings/terrain impose many constraints in sUAS operations. The sensitive trajectory response and high maneuverability make sUAS different from manned or unmanned large-size fixed-wing aircraft and dramatically change the way traffic system operates. These characteristics and the predicted large scale operations 1 present great challenges in managing safe and efficient traffic operations in low altitude airspace.NASA's UTM research initiative 3, 4 is researching and defining requirements and policies for the UTM system to ensure fair, safe, and efficient UAS operations in the future. In order to investigate the impacts of various device parameters, traffic system rules and policies, operational schedules, and wind conditions, especially in dense operations, it is necessary to build an effective fast-time simulation platform that can incorporate different parameters, rules, ...

show abstract

A Fuzzy Inference System for an Optimal Spacecraft Attitude State Trajectory

Walker

2023

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

A Fuzzy Logic Approach for Low Altitude UAS Traffic Management (UTM)

Cited by 10 publications

References 3 publications

A Fuzzy Logic Approach for Separation Assurance and Collision Avoidance for Unmanned Aerial Systems

A Fuzzy Logic Approach for Separation Assurance and Collision Avoidance for Unmanned Aerial Systems

Initial Study of An Effective Fast-time Simulation Platform for Unmanned Aircraft System Traffic Management

A Fuzzy Inference System for an Optimal Spacecraft Attitude State Trajectory

Contact Info

Product

Resources

About