Development of an Automatic Aircraft Collision Avoidance System for Fighter Aircraft

Wadley, Jason; Jones, S.; Stoner, Dayton; Griffin, Ed; Swihart, Donald; Hobbs, Kerianne; Burns, Amy; Bier, James

doi:10.2514/6.2013-4727

Cited by 10 publications

(7 citation statements)

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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%

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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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Section: Module Of Maneuver Generationmentioning

confidence: 99%

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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“…The TCAS does not control the aircraft but only provides resolution advisories (RAs) in vertical directions for pilots [21]- [23]. Auto ACAS performs an automatic, aggressive strategy to avoid collisions with other fighters [24]- [26]. Both TCAS and Auto-ACAS are designed mainly for paired-manned-aircraft encounters.…”

Section: Literaturementioning

confidence: 99%

Distributed Conflict-Detection and Resolution Algorithm for UAV Swarms Based on Consensus Algorithm and Strategy Coordination

2019

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In this paper, we study the problem of conflict detection and resolution for unmanned aerial vehicle (UAV) swarms. Specially, we propose a distributed conflict detection and resolution method for multi-UAVs in formation based on consensus algorithm and strategy coordination. When encountering threat swarms, the UAVs in one swarm act as one unit and are together treated as one control object. Each swarm in conflict selects three candidate collision avoidance maneuvers from the preset strategy pool, generates the corresponding planned trajectories with an uncertainty trajectory modeling, and then broadcasts and shares them. All of the swarms in conflict coordinate and determine an optimal combination of strategies. When a collision is imminent, the primary strategy is activated. Each swarm adopts a ''leader-follower'' strategy, where the leader UAV is regarded as the controller and flies independently, and the others follow the leader UAV. During motion, a decentralized consensus algorithm is adopted for agents to converge to their positions for the desired formation and to maintain a stable geometric configuration. A temporal and spatially integrated conflict-detection model is improved, especially for UAV swarms. A token-allocation strategy is especially improved for distributed coordination to resolve the partial knowledge of the airspace condition. Damping of the coordination is proposed to address data dropouts and transmission delays. Two typical scenarios are conducted to test the methodology proposed in this paper. The simulation result demonstrates the effectiveness and rationality of the proposed methodology.

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“…Despite its ability of to deal with multi-aircraft encounters, TCAS sometimes issues improper RAs in complex situations [22]. Auto-ACAS was developed for fighter aircraft operating in an air combat training environment [23]- [25]. Auto ACAS performs an automatic, aggressive strategy to avoid collisions with other aircraft.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Fixed-wing aircraft have great flexibility and mobility and can change their flight state quickly through rolling and climbing. In relevant studies [23]- [25], strategies were chosen from a pre-set group of categories. Setting the categories in advance can reduce the system budget, improve the calculation speed, and ensure a rapid response.…”

Section: Strategy Generation Based On Maximum Robustnessmentioning

confidence: 99%

Distributed Conflict-Detection and Resolution Algorithms for Multiple UAVs Based on Key-Node Selection and Strategy Coordination

2019

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A distributed conflict-detection (CD) and resolution algorithm for multiple unmanned aerial vehicles based on key-node selection and strategy coordination are proposed in this paper. Each aircraft is regarded as a critical node in a complex network. A novel temporal and spatial integrated CD methodology is proposed for aircraft formations and clusters. Each node selects three candidate strategies from a pre-set pool of strategies and generates corresponding planned tracks using uncertainty modeling based on the 6 degreesof-freedom motion. The primary combination of strategies required for coordination of the aircraft in conflict is based on the maximum-robustness principle. To address partial knowledge of the environment, a special token-allocation strategy for coordination with incomplete information is proposed in this paper. To address potential data dropouts, this study damps the solution to achieve coordination. Two extreme scenarios are constructed to examine the proposed methodology. The results showed great collision-avoidance ability in continuous conflicts with multiple aircraft in a highly dynamic 3-D environment.

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Development of an Automatic Aircraft Collision Avoidance System for Fighter Aircraft

Cited by 10 publications

References 0 publications

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

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

Distributed Conflict-Detection and Resolution Algorithm for UAV Swarms Based on Consensus Algorithm and Strategy Coordination

Distributed Conflict-Detection and Resolution Algorithms for Multiple UAVs Based on Key-Node Selection and Strategy Coordination

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