Enabling Operational Autonomy for Unmanned Aerial Vehicles with Scalability

Jung, Sunghun; Ariyur, Kartik B.

doi:10.2514/1.i010052

Cited by 5 publications

(2 citation statements)

References 30 publications

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“…Unmanned aerial vehicles (UAVs) have played an important role in both military and civilian applications in the previous two decades. 1–3 And nowadays, with the development of the inexpensive and easy-to-produce UAVs, a novel concept of “UAV swarm” has attracted much attention of researches. 4 The U.S. air force released the “Small unmanned aircraft systems (SUAS) flight plan: 2016–2036” which presented the operation concepts and operation planning of UAV swarm that consists of a large number of inexpensive and small-sized UAVs.…”

Section: Introductionmentioning

confidence: 99%

Offense–defense confrontation decision making for dynamic UAV swarm versus UAV swarm

Dongjing

Zhen

Gong

2019

Proceedings of the Institution of Mechanical Engineers, Part G:

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This paper studies a dynamic swarm versus swarm unmanned aerial vehicle (UAV) combat problem and proposes a self-organized offense–defense confrontation decision-making (ODCDM) algorithm. This ODCDM algorithm adopts the distributed architecture to account for real-time implementation, where each UAV is treated as an agent and able to solve its local decision problem through the information exchange with neighbors. At each decision making step, the swarm seeks an optimal target allocation scheme and each UAV further selects the corresponding behavioral rules, leading to emergent offensive and defensive behaviors. Therefore, the offense–defense confrontation decision-making process is divided into the target allocation decision based on distributed consensus-based auction algorithm (CBAA) and social-force-based swarm motion decision. An offense–defense preference is introduced to the target allocation optimization model, providing the tactics options for UAV to adopt more offensive or more defensive posture. On the basis of classic collective behaviors of cohesion, separation and alignment, a combat stimulus is considered to drive UAV towards the assigned target. Finally, simulation experiments are carried out to verify the effectiveness of the ODCDM algorithm, and analyze the influences of the external deployment and internal tactics on the combat results.

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Section: Introductionmentioning

confidence: 99%

Offense–defense confrontation decision making for dynamic UAV swarm versus UAV swarm

Dongjing

Zhen

Gong

2019

Proceedings of the Institution of Mechanical Engineers, Part G:

View full text Add to dashboard Cite

show abstract

“…UAVs follow predefined, collision-free, and time optimal waypoints which are generated by using the genetic algorithm (GA) based multiple traveling salesman problems (mTSP) algorithm [19]. Waypoints are drawn in green as shown in [Fig.…”

Section: Simulink Designmentioning

confidence: 99%