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 D istributed M ulti-Robot P otential- F ield-Based Exploration (DMPF-Explore). In particular, we first present a D istributed S ubmap-Based M ulti-Robot C ollaborative 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 M odified W ave- F ront Distance and C olored N oises (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. • Our exploration method, DMPF-Explore, comprises the DSMC-Map and the MWF-CN. • The DSMC-Map provides reliable and consistent maps of unknown environments. • The MWF-CN boosts frontier detection and robots' adaptability. • By experiments, robots with DMPF-Explore explore more efficiently than the baselines. [ABSTRACT FROM AUTHOR]