Route Optimization for Cooperative Aerial Reconnaissance

Stodola, Petr

doi:10.1007/978-3-319-76072-8_6

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…Sun et al presented a predictive-control-based algorithm to generate and track the path based on the requirements for the security and the path length for an unmanned surface vehicle [33]. In more general cases, Stodola proposed a smooth algorithm to connect a series of waypoints and generate a feasible path for UAVs in reconnaissance missions [34]. Shao et al proposed a path planning method based on the particle swarm optimization algorithm on three dimensional UAV formation tasks [35].…”

Section: Introductionmentioning

confidence: 99%

Cooperative Path Planning for Aerial Recovery of a UAV Swarm Using Genetic Algorithm and Homotopic Approach

Liu

Yao

et al. 2020

Applied Sciences

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To maximize the advantages of being low-cost, highly mobile, and having a high flexibility, aerial recovery technology is important for unmanned aerial vehicle (UAV) swarms. In particular, the operation mode of “launch-recovery-relaunch” will greatly improve the efficiency of a UAV swarm. However, it is difficult to realize large-scale aerial recovery of UAV swarms because this process involves complex multi-UAV recovery scheduling, path planning, rendezvous, and acquisition problems. In this study, the recovery problem of a UAV swarm by a mother aircraft has been investigated. To solve the problem, a recovery planning framework is proposed to establish the coupling mechanism between the scheduling and path planning of a multi-UAV aerial recovery. A genetic algorithm is employed to realize efficient and precise scheduling. A homotopic path planning approach is proposed to cover the paths with an expected length for long-range aerial recovery missions. Simulations in representative scenarios validate the effectiveness of the recovery planning framework and the proposed methods. It can be concluded that the recovery planning framework can achieve a high performance in dealing with the aerial recovery problem.

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Section: Introductionmentioning

confidence: 99%