Long-Distance Target Localization Optimization Algorithm Based on Single Robot Moving Path Planning

Strategic Insights -

Researchers from Huzhou University have developed an optimized algorithm for long-distance target localization (LTLO) based on single-robot moving path planning to address the problem of low positioning accuracy for long-distance static targets. By introducing constraints on stopping position selection and non-redundant locations, the algorithm improves the positioning accuracy for long-distance targets. The research concluded that the proposed LTLO algorithm outperforms traditional localization methods, achieving a relative localization error within 4% and an absolute localization error within 6% for targets at distances ranging from 100 to 500 meters.

Key Takeaways:

  • The optimized algorithm for LTLO is based on single-robot moving path planning, dividing the robot's movement area into hexagonal grids.
  • The algorithm introduces constraints on stopping position selection and non-redundant locations to improve positioning accuracy.
  • The proposed method for calculating the relative position of the target uses sensing information from two positions based on image parallelism.
  • A double deep Q-network is employed to solve the optimization model and obtain the optimal target positions and path trajectories.
  • The research concludes that LTLO outperforms traditional localization methods, achieving a relative localization error within 4% and an absolute localization error within 6% for targets at distances ranging from 100 to 500 meters.
  • The algorithm is developed to fuse the relative coordinates of multiple targets using an improved hierarchical density-Based spatial clustering of applications with noise (HDBSCAN) algorithm.
  • The research establishes corresponding constraints for long-distance target localization and constructs a target localization optimization model based on single-robot path planning.

Statistics:

  • The research achieved a relative localization error within 4% for targets at distances ranging from 100 to 500 meters.
  • The absolute localization error was within 6% for targets at distances ranging from 100 to 500 meters.
  • The experimentation was conducted using a range of target distances from 100 to 500 meters.
  • The proposed LTLO algorithm outperformed traditional localization methods (TMVL, MGG, and LRBVTG) for long-distance targets.

Sources:

  • Long-distance target localization optimization algorithm based on single robot moving path planning. Scientific Reports, 2025;15(1):25157.
  • Nature Publishing Group - www.nature.com/
  • Scientific Reports - www.nature.com/srep/
  • Huzhou University, School of Information Engineering - Hu Zhou, 313000, People's Republic of China.