サブマップ地図とノイズ強化戦略を用いた分散マルチロボットポテンシャル場探索
Distributed multi-robot potential-field-based exploration with submap-based mapping and noise-augmented strategy
複数ロボットが協調して未知環境を探索するため、サブマップベースの地図統合とノイズを加えたポテンシャル場戦略を提案し、探索速度と協調性を向上させた。
著者: Khattiya Pongsirijinda, Zhiqiang Cao, Kaushik Bhowmik, Muhammad Shalihan, Billy Pik Lik Lau, Ran Liu, Chau Yuen, U-Xuan Tan
分類: cs.RO
原文アブストラクト
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.
