A Decentralized Swarm Intelligence Algorithm for Resilient UAV Coordination in Environmental Monitoring: A Python Simulation and Performance Analysis

The coordination of Unmanned Aerial Vehicle (UAV) swarms for environmental monitoring faces significant challenges due to the limitations of centralized control, including single points of failure and high communication latency in dynamic environments. This study addresses the need for robust, scalable, and adaptive coordination without relying on a central controller. Methods: We propose a decentralized swarm intelligence algorithm based on local interaction rules, including separation, alignment, and cohesion, to govern collective agent behavior. The model was implemented and validated using a custom Python simulation environment, focusing on formal stability metrics that link local agent rules to global swarm dynamics. Results: Performance analysis using Swarm Performance Indicators (SPIs) demonstrates that the decentralized approach ensures high swarm stability and resilient network connectivity. Quantitative evaluations show that the system maintains operational integrity even under partial agent failure, outperforming traditional centralized architectures in scalability and fault tolerance. Specifically, the algorithm optimizes the trade-off between tracking accuracy and communication link quality, maintaining stable coordination with linear computational complexity. Conclusions: The findings highlight the efficacy of decentralized algorithms for enhancing the autonomy and resilience of mechatronic systems. This research provides a scalable analytical framework for next-generation autonomous systems in complex monitoring tasks, directly contributing to the field of automation and machine design.