Abstract
To address the problem of low positioning accuracy for long-distance static targets, we propose an optimized algorithm for long-distance target localization (LTLO) based on single-robot moving path planning. The algorithm divides the robot's movement area into hexagonal grids and introduces constraints on stopping position selection and non-redundant locations. Based on image parallelism, we propose a method for calculating the relative position of the target using sensing information from two positions. Additionally, an improved hierarchical density-Based spatial clustering of applications with noise (HDBSCAN) algorithm is developed to fuse the relative coordinates of multiple targets. Furthermore, we establish the corresponding constraints for long-distance target localization and construct a target localization optimization model based on single-robot path planning. To solve this model, we employ a double deep Q-network and propose a reward strategy based on coordinate fusion error. This approach solves the optimization model and obtains the optimal target positions and path trajectories, thereby improving the positioning accuracy for long-distance targets. Experimental results demonstrate that for static targets at distances ranging from 100 to 500 meters, LTLO outperforms traditional monocular visual localization (TMVL), monocular global geolocation (MGG) and long-range binocular vision target geolocation (LRBVTG) by obtaining an optimal path to identify target positions, maintaining a relative localization error within 4% and an absolute localization error within 6%.