Distributed multi-robot potential-field-based exploration with submap-based mapping and noise-augmented strategy
<|reference_start|>Distributed multi-robot potential-field-based exploration with submap-based mapping and noise-augmented strategy: Multi-robot collaboration has become a needed component in unknown environment exploration due to its ability to accomplish various challenging situations. Potential-field-based methods are widely used for autonomous exploration because of their high efficiency and low travel cost. However, exploration speed and collaboration ability are still challenging topics. Therefore, we propose a Distributed Multi-Robot Potential-Field-Based Exploration (DMPF-Explore). In particular, we first present a Distributed Submap-Based Multi-Robot Collaborative Mapping Method (DSMC-Map), which can efficiently estimate the robot trajectories and construct the global map by merging the local maps from each robot. Second, we introduce a Potential-Field-Based Exploration Strategy Augmented with Modified Wave-Front Distance and Colored Noises (MWF-CN), in which the accessible frontier neighborhood is extended, and the colored noise provokes the enhancement of exploration performance. The proposed exploration method is deployed for simulation and real-world scenarios. The results show that our approach outperforms the existing ones regarding exploration speed and collaboration ability.<|reference_end|>
@article{pongsirijinda2024distributed,
    title={Distributed multi-robot potential-field-based exploration with
  submap-based mapping and noise-augmented strategy},
    author={Khattiya Pongsirijinda, Zhiqiang Cao, Kaushik Bhowmik, Muhammad
  Shalihan, Billy Pik Lik Lau, Ran Liu, Chau Yuen, U-Xuan Tan},
    journal={arXiv preprint arXiv:2407.07409},
    year={2024},
    doi={10.1016/j.robot.2024.104752},
    archivePrefix={arXiv},
    eprint={2407.07409},
    primaryClass={cs.RO}
}