Enhancing Multi-UAV Path Planning Efficiency with Security through Integration of Priority-Based Ant Lion Optimization Algorithm

The research endeavors to revolutionize Multi-Robot Systems (MRS) through a cutting-edge hybrid strategy, amalgamating Priority-Based Algorithms (PBA) and Ant Lion Optimization (ALO) algorithms. This innovative approach aims to elevate path planning efficacy and coordination within dynamic and intricate environments. Leveraging PBA for collision-free path prioritization and ALO for agile and dynamic decision-making, the methodology promises to significantly enhance MRS's operational efficiency in real-world scenarios. The proposed Hybrid of Asynchronous Centralized and Decentralized (HACD) algorithm introduces a pioneering framework for fostering robot collaboration and resolving conflicts via distributed leader strategies. By employing grid-based decomposition and leader election mechanisms, it ensures seamless navigation in multi-robot setups by segmenting paths and imposing conflict limits. Integrating prioritized planning with trajectory optimization using ALO emulates the efficiency of ant lions' hunting behaviors, yielding adaptive solutions for dynamic operational landscapes. In addition, adding the help of Secret Cryptographic Key as the compartment of SSL for the proposed algorithm. MATLAB simulations validate the algorithm's scalability, efficiency, and effectiveness across diverse robot configurations, underscoring its potential for applications spanning search and rescue operations, military maneuvers, and warehouse logistics, where optimized Multi-Robot Path Planning (MRPP) is paramount.