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

Cook, Brandon; Arnett, Timothy; Cohen, Kelly

doi:10.5772/68126

Cited by 8 publications

(8 citation statements)

References 13 publications

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“…In this situation DAA systems should give directive maneuvers, such that the sUAS regains its previous state. Both of these functions are related to a well clear notion, which is mainly viewed as protection volume around UAS, referred to as well clear volume (WCV) [9], [35], [36]. This volume can be specified by spatial thresholds, temporal thresholds, or both at the same time, referred to as separation minima in this work.…”

Section: The Well Clear Concept and Near Mid Air Collisionmentioning

confidence: 99%

Quantifying Well Clear for Autonomous Small UAS

2022

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Air traffic safety is the primary concern when it comes to the integration of Unmanned Aircraft Systems in the civil airspace. At the tactical level, the goal is to quantify en-route safety through pairwise separation minima. Unmanned Aircraft Systems make use of Detect and Avoid systems to remain, or regain well clear from other aircraft. There is already a well clear definition adopted for large Unmanned Aircraft Systems, however, this definition is not adequate and applicable to small Unmanned Aircraft Systems operating in low altitudes. In order to ensure the safe separation of various types of sUAS with different performances, a self-separation method is proposed. The method is based on dynamic protection zone, an early concept developed by US Air Force and later adapted into a time-based Detect and Avoid self-separation method. This paper outlines an engineering approach on designing a generic methodology, to define well clear among small Unmanned Aircraft Systems. For this purpose, both unmanned aircraft and systems performance are considered. Furthermore, we specify and recommend appropriate well clear thresholds and Detect and Avoid alerting times, based on the results the severity of loss of well clear. Several influencing factors such encounter geometry, speed and uncertainties in Communication, Navigation and Surveillance systems are examined to obtain efficient separation criteria.

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Section: The Well Clear Concept and Near Mid Air Collisionmentioning

confidence: 99%

Quantifying Well Clear for Autonomous Small UAS

2022

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“…The proposed method predicts the distance by using fuzzy time crowding to identify the intentions of a human being walking on a specific track, and this information is later used to avoid rear-end collisions. Cook et al 17 present a fuzzy logic-based controller to alleviate the risk of collisions in many Small Unmanned Aircraft Systems (SUAS). Moon et al 18 propose an Adaptive Cruise Control (ACC) system comprising different driving modes according to the real-time road environment to avoid collisions and to improve driver comfort.…”

Section: Literature Reviewmentioning

confidence: 99%

REEEC-AGENT: human driver cognition and emotions-inspired rear-end collision avoidance method for autonomous vehicles

et al. 2021

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Rear-end collision detection and avoidance is one of the most crucial driving tasks of self-driving vehicles. Mathematical models and fuzzy logic-based methods have recently been proposed to improve the effectiveness of the rear-end collision detection and avoidance systems in autonomous vehicles (AVs). However, these methodologies do not tackle real-time object detection and response problems in dense/dynamic road traffic conditions due to their complex computation and decision-making structures. In our previous work, we presented an affective computing-inspired Enhanced Emotion Enabled Cognitive Agent (EEEC_Agent), which is capable of rear-end collision avoidance using artificial human driver emotions. However, the architecture of the EEEC_Agent is based on an ultrasonic sensory system which follows three-state driving strategies without considering the neighbor vehicles types. To address these issues, in this paper we propose an extended version of the EEEC_Agent which contains human driver-inspired dynamic driving mode controls for autonomous vehicles. In addition, a novel end-to-end learning-based motion planner has been devised to perceive the surrounding environment and regulate driving tasks accordingly. The real-time in-field experiments performed using a prototype AV demonstrate the effectiveness of this proposed rear-end collision avoidance system.

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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%

“…The distance to the intersection point, d I j , is then calculated by Eq. (12)- (14). A visualization of the intersection point (x I , y I ) and the distances d I P and d I T are shown in Figure 2.…”

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

Cited by 8 publications

References 13 publications

Quantifying Well Clear for Autonomous Small UAS

Quantifying Well Clear for Autonomous Small UAS

REEEC-AGENT: human driver cognition and emotions-inspired rear-end collision avoidance method for autonomous vehicles

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

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