Automatic air collision avoidance system

Ikeda, Yutaka; Nguyen, Ba; Barfield, A. F.; Sundqvist, Bengt-Göran; Jones, S.

doi:10.1109/sice.2002.1195482

Cited by 10 publications

(4 citation statements)

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“…The process and observation noise vectors also have the same characteristics shown in (34)- (38) for the classical Kalman Filter: they have zero mean, are uncorrelated to themselves in time, and are uncorrelated to each other in time.…”

Section: The Extended Kalman Filtermentioning

confidence: 95%

Cooperative decentralized intersection collision avoidance using Extended Kalman Filtering

Farahmand

Mili

2009

2009 IEEE Intelligent Vehicles Symposium

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Automobile accidents are one of the leading causes of death and claim more than 40,000 lives annually in the US alone. A substantial portion of these accidents occur at road intersections. Stop signs and traffic signals are some of the intersection control devices used to increase safety and prevent collisions. However, these devices themselves can contribute to collisions, are costly, inefficient, and are prone to failure. This thesis proposes an adaptive, decentralized, cooperative collision avoidance (CCA) system that optimizes each vehicle's controls subject to the constraint that no collisions occur. Three major contributions to the field of collision avoidance have resulted from this research. iii

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Section: The Extended Kalman Filtermentioning

confidence: 95%

Cooperative decentralized intersection collision avoidance using Extended Kalman Filtering

Farahmand

Mili

2009

2009 IEEE Intelligent Vehicles Symposium

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“…Fixed-wing aircrafts can generate a maneuver style (MS) in almost all directions for collision avoidance. However, in relevant studies [24][25][26], the MS is set to a group of specific categories. In this paper, according to the characteristics of fixed-wing aircraft, the MS is defined to nine types.…”

Section: Module Of Maneuver Generationmentioning

confidence: 99%

“…Sharma R K [23] applied swarm intelligence techniques to collision avoidance between UAVs. Turner R [24] Ikeda Y [25] Wadley J [26] introduced an automatic air collision avoidance system (Auto ACAS) that coordinates maneuver trajectories between fighters. Although Auto-ACAS applies to fighters, it provides a good concept for the collision avoidance of fixed-wing UAVs.…”

Section: Introductionmentioning

confidence: 99%

Research on the Collision Avoidance Algorithm for Fixed-Wing UAVs Based on Maneuver Coordination and Planned Trajectories Prediction

2019

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This paper presents a novel collision avoidance (CA) algorithm for a cooperative fixed-wing unmanned aerial vehicle (UAV). The method is based on maneuver coordination and planned trajectory prediction. Each aircraft in a conflict generates three available maneuvers and predicts the corresponding planned trajectories. The algorithm coordinates planned trajectories between participants in a conflict, determines which combination of planned trajectories provides the best separation, eventually makes an agreement on the maneuver for collision avoidance and activates the preferred maneuvers when a collision is imminent. The emphasis is placed on providing protection for UAVs, while activating maneuvers late enough to reduce interference, which is necessary for collision avoidance in the formation and clustering of UAVs. The CA has been validated with various simulations to show the advantage of collision avoidance for continuous conflicts in multiple, high-dynamic, high-density and three-dimensional (3D) environments. It eliminates the disadvantage of traditional CA, which has high uncertainty, and takes the performance parameters of different aircraft into consideration and makes full use of the maneuverability of fixed-wing aircraft.

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“…a r e f e r e n c e model c . a performance index c o n c e r n i n g t h e d i f f e r e n c e between t h e b e h a v i o r s of t h e r e f e r e n c e model and t h e a d j u s t a b l e system: J = F ( t , e , F -p )(9) are g i v e n , t h e t o t a l system which minimizes J u s i n g t h e l o c a l a d a p t i o n l a w s i s s a i d t o b e a d e c e n t r a l i z e d model r e f e r e n c e a d a p t i v e c o n t r o l system ( d e c e n t r a l i z e d MRACS).…”

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confidence: 99%

Decentralized adaptive control of large‐scale systems—a parameter adaption method

Yoshikawa

Ikeda

1983

Electron. Comm. Jpn. Pt. I

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A way of controlling large‐scale systems that have unknown parameters is a decentralized version of the model reference adaptive control. the idea is to construct adaptive controllers for each subsystem so that the overall system becomes an adaptive control system. the strategy does not always work successfully, however, because the information structure for control is decentralized. This paper aims to provide conditions under which we can construct such decentralized adaptive control systems. We first clarify the constraints to which the given system must conform whichever scheme of adaptive control we employ at the subsystem level, and present conditions which must be satisfied between the given system and the reference model. Then we consider adaptive control laws of the parameter adaption type and give classes of reference models with which they can be implemented.

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Automatic air collision avoidance system

Cited by 10 publications

References 0 publications

Cooperative decentralized intersection collision avoidance using Extended Kalman Filtering

Cooperative decentralized intersection collision avoidance using Extended Kalman Filtering

Research on the Collision Avoidance Algorithm for Fixed-Wing UAVs Based on Maneuver Coordination and Planned Trajectories Prediction

Decentralized adaptive control of large‐scale systems—a parameter adaption method

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