UAS Collision Avoidance, Navigation, and Target Assignment in a Congested Airspace Using Fuzzy Logic

Cook, Brandon; Arnett, Timothy; Rich, B.L.; Kivelevitch, Elad H.

doi:10.2514/6.2015-2031

Cited by 2 publications

(2 citation statements)

References 2 publications

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“…The avoidance controller was developed with inspiration from the methods used in Ref. [13,14]. It uses a similar logic based on the intersection point of the UAS.…”

Section: A Avoidancementioning

confidence: 99%

A Supervisory Neural Network and Fuzzy Inference System Approach to an Unmanned Aerial Vehicle Tail-Chase Scenario

Arnett

Cohen

Cook

et al. 2019

AIAA Scitech 2019 Forum

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As the capabilities and implementation of multiple Unmanned Aerial Systems (UAS) operations increase, the need to develop reliable, verifiable, and high-performing vehicle control algorithms arises. There are several methods for achieving near optimal navigation control in multiple UAS scenarios. However, many approaches have difficulty identifying boundaries in the state-space where discontinuities in the control signal exist. Neural Networks (NNs) have been shown to be universal approximators of these decision boundaries and can therefore classify the space into discrete regions with differing lower level actions. In this study, a control system for a one-on-one UAS Tail-Chase scenario is developed. An NN was used to define a decision boundary for discrete selection of two different Fuzzy Inference Systems (FISs) for navigation and avoidance control. The parameters for both the NN and FISs are found using a Genetic Algorithm (GA) inside a custom simulation environment. After initial training, uncertainty was included in vehicle movements to improve generalization. The simulation results show that the system was successful in all test cases after adding uncertainty and demonstrate the efficacy of this approach.

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“…The avoidance controller was developed with inspiration from the methods used in Ref. [13,14]. It uses a similar logic based on the intersection point of the UAS.…”

Section: A Avoidancementioning

confidence: 99%

A Supervisory Neural Network and Fuzzy Inference System Approach to an Unmanned Aerial Vehicle Tail-Chase Scenario

Arnett

Cohen

Cook

et al. 2019

AIAA Scitech 2019 Forum

Self Cite

View full text Add to dashboard Cite

show abstract

“…For more information about the various aspects of this simulation environment refer to Ref. 4. Once the data acquisition is complete, the aircraft position data will be stored in an external file so that it can be used in the radar tracking simulation environment.…”

Section: A Data Acquisition and Simulation Assumptionsmentioning

confidence: 99%

Real-Time Radar-Based Tracking and State Estimation of Multiple Non-Conformant Aircraft

Cook

Arnett

Macmann

et al. 2017

AIAA Information Systems-Aiaa Infotech @ Aerospace

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In this study, a novel solution for automated tracking of multiple unknown aircraft is proposed. Many current methods use transponders to self-report state information and augment track identification. While conformant aircraft typically report transponder information to alert surrounding aircraft of its state, vehicles may exist in the airspace that are non-compliant and need to be accurately tracked using alternative methods. In this study, a multi-agent tracking solution is presented that solely utilizes primary surveillance radar data to estimate aircraft state information. Main research challenges include state estimation, track management, data association, and establishing persistent track validity. In an effort to realize these challenges, techniques such as Maximum a Posteriori estimation, Kalman filtering, degree of membership data association, and Nearest Neighbor Spanning Tree clustering are implemented for this application.

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UAS Collision Avoidance, Navigation, and Target Assignment in a Congested Airspace Using Fuzzy Logic

Cited by 2 publications

References 2 publications

A Supervisory Neural Network and Fuzzy Inference System Approach to an Unmanned Aerial Vehicle Tail-Chase Scenario

A Supervisory Neural Network and Fuzzy Inference System Approach to an Unmanned Aerial Vehicle Tail-Chase Scenario

Real-Time Radar-Based Tracking and State Estimation of Multiple Non-Conformant Aircraft

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