Adaptive Artificial Rabbit Optimization for 3D Localization of Swarm War Robots in Forested Terrain

Abstract This study introduces the adaptive artificial rabbit optimization (AARO) algorithm for the 3D localization of swarm war robots (SWRs) deployed in forested terrain, where traditional GPS-based solutions are unreliable. The aim is to utilize received signal strength indicator (RSSI) information from a mobile anchor node (drone) to determine the positions of the SWRs. The AARO algorithm improves localization accuracy and time efficiency by leveraging the foraging and hiding behaviors of rabbits, allowing it to dynamically adapt its search strategies in the presence of noise. Simulation studies demonstrate that AARO outperforms established algorithms such as particle swarm optimization (PSO) and artificial rabbit optimization (ARO), achieving results close to the theoretical Cramer–Rao Lower Bound (CRLB). The results indicate that AARO achieves a maximum improvement of approximately 1.15% over ARO in terms of the average localization error and 24.43% over ARO in terms of execution time. Additionally, a general execution time enhancement in performance of about 10.02% was observed across all results.