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.

翻译：多机器人协作因其能够应对各种复杂场景，已成为未知环境探索中的必要组成部分。基于势场的方法因其高效率和低移动成本而被广泛用于自主探索。然而，探索速度与协作能力仍是具有挑战性的课题。为此，我们提出了一种分布式多机器人基于势场的探索方法（DMPF-Explore）。具体而言，我们首先提出了一种基于分布式子地图的多机器人协作建图方法（DSMC-Map），该方法能够高效估计机器人轨迹，并通过融合各机器人的局部地图来构建全局地图。其次，我们引入了一种基于势场的探索策略，该策略通过改进波前距离与有色噪声进行增强（MWF-CN），其中可访问前沿邻域得到扩展，且有色噪声有效提升了探索性能。所提出的探索方法在仿真与真实场景中进行了部署验证。结果表明，本方法在探索速度与协作能力方面均优于现有方法。