Cluster-Based Genetic Algorithm Path Planning for Cooperative UGV and UAV Operations in Energy-efficient Wireless Sensor Networks

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This paper introduces a novel algorithm for efficient path planning of Unmanned Ground Vehicles (UGVs) and Unmanned Aerial Vehicles (UAVs) to monitor and serve IoT sensors deployed in agricultural fields. The proposed method uses clustering and Genetic Algorithm (GA) to solve the energy consumption optimization problem. Sensors are initially clustered based on proximity and then iteratively combined into larger-size clusters. Tasks are assigned to UGVs and UAVs, with UGVs covering some of the clusters and UAVs handling the rest. The GA determines the optimal paths within each cluster. This approach ensures that the overall resource usage is optimized, outperforming existing methods by achieving higher energy efficiency with lower time complexity.

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