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<p>Swarm-based UAV applications encounter significant challenges in safety, legality, and computational efficiency-particularly in GPS-denied environments. This study presents a novel approach integrating Received Signal Strength Indicator (RSSI)-based relative localisation with Fractional Order Proportional-Integral-Derivative (FOPID) control to enable autonomous navigation for CoDrones (Robolink Inc.) in constrained indoor settings such as rescue missions, greenhouse monitoring, and pipeline inspection. Unlike prior approaches reliant on external systems like motion capture or GPS, this method exploits onboard sensors and RSSI to achieve precise, collision-free swarm coordination. The integration of RSSI with FOPID not only eliminates the need for external localisation infrastructure but also enhances control precision over traditional PID and RSSI-enabled PID methods. Simulations and hardware validations on square and helical trajectories with two and three CoDrones confirm reduced velocity tracking error, improved stability, and real-time responsiveness. This work advances the field by demonstrating a scalable, infrastructure-free swarm control strategy using only onboard resources-a capability not achieved in prior literature.</p>

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Trajectory optimisation for swarm UAVs in constrained environments with RSSI-based FOPID control

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月15日

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  • Ghulam E Mustafa Abro

摘要

Swarm-based UAV applications encounter significant challenges in safety, legality, and computational efficiency-particularly in GPS-denied environments. This study presents a novel approach integrating Received Signal Strength Indicator (RSSI)-based relative localisation with Fractional Order Proportional-Integral-Derivative (FOPID) control to enable autonomous navigation for CoDrones (Robolink Inc.) in constrained indoor settings such as rescue missions, greenhouse monitoring, and pipeline inspection. Unlike prior approaches reliant on external systems like motion capture or GPS, this method exploits onboard sensors and RSSI to achieve precise, collision-free swarm coordination. The integration of RSSI with FOPID not only eliminates the need for external localisation infrastructure but also enhances control precision over traditional PID and RSSI-enabled PID methods. Simulations and hardware validations on square and helical trajectories with two and three CoDrones confirm reduced velocity tracking error, improved stability, and real-time responsiveness. This work advances the field by demonstrating a scalable, infrastructure-free swarm control strategy using only onboard resources-a capability not achieved in prior literature.