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<p>Unmanned aerial vehicles (UAVs) and micro-aerial vehicles (MAVs) are becoming more popular in swarm applications due to their decreasing costs and wider availability. Swarm systems composed of different types of aerial vehicles operating in the same environment are particularly valuable for tasks like reconnaissance, surveillance, and collaborative navigation. For these mixed-type swarms, quickly finding a collision-free, optimal positioning in complex, obstacle-filled environments is a key challenge. Particle swarm optimization (PSO) techniques are widely used for this purpose, with the nPSO variant offering faster convergence than traditional PSO. This paper extends the challenge of optimal, collision-free positioning for heterogeneous swarms containing varying numbers of UAVs and MAVs in the presence of obstacles using the nPSO algorithm in 3D environments. The optimization focuses on both the area covered and the number of vehicles. Results show that with less than 200 iterations, an optimal positioning for UAV and MAV swarms can be achieved in 3D environments dense with obstacles.</p>

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Positioning solution for heterogeneous swarm of UAVs and MAVs in 3D crowded environment

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月5日

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  • Sotirios Spanogianopoulos,
  • Kenan Ahiska

摘要

Unmanned aerial vehicles (UAVs) and micro-aerial vehicles (MAVs) are becoming more popular in swarm applications due to their decreasing costs and wider availability. Swarm systems composed of different types of aerial vehicles operating in the same environment are particularly valuable for tasks like reconnaissance, surveillance, and collaborative navigation. For these mixed-type swarms, quickly finding a collision-free, optimal positioning in complex, obstacle-filled environments is a key challenge. Particle swarm optimization (PSO) techniques are widely used for this purpose, with the nPSO variant offering faster convergence than traditional PSO. This paper extends the challenge of optimal, collision-free positioning for heterogeneous swarms containing varying numbers of UAVs and MAVs in the presence of obstacles using the nPSO algorithm in 3D environments. The optimization focuses on both the area covered and the number of vehicles. Results show that with less than 200 iterations, an optimal positioning for UAV and MAV swarms can be achieved in 3D environments dense with obstacles.