Distributed multi-robot potential-field-based exploration with submap-based mapping and noise-augmented strategy
Singapore University of Technology and Design（新加坡科技设计大学）；Inria Grenoble, Grenoble Alpes University（法国国家信息与自动化研究所格勒诺布尔分部，格勒诺布尔阿尔卑斯大学）；School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）
Khattiya Pongsirijinda, Zhiqiang Cao, Kaushik Bhowmik, Muhammad Shalihan, Billy Pik Lik Lau, Ran Liu, Chau Yuen, U-Xuan Tan
备注：
This paper has been accepted by Robotics and Autonomous Systems
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.
Efficient Submap-based Autonomous MAV Exploration using Visual-Inertial SLAM Configurable for LiDARs or Depth Cameras
Technical University of Munich（慕尼黑技术大学）；School of Computation, Information and Technology（计算、信息与技术学院）；Imperial College London（伦敦帝国学院）；Munich Institute of Robotics and Machine Intelligence（慕尼黑机器人与机器智能研究所）；Munich Center for Machine Learning（慕尼黑机器学习中心）
MAGS-SLAM: Monocular Multi-Agent Gaussian Splatting SLAM for Geometrically and Photometrically Consistent Reconstruction
ETH Zurich（苏黎世联邦理工学院）；Harbin Engineering University（哈尔滨工程大学）；University of Liverpool（利物浦大学）；University of Macau（澳门大学）；University of Ottawa（多伦多大学）；Wuhan University（武汉大学）；Imperial College London（伦敦帝国学院）
FindAnything: Open-Vocabulary and Object-Centric Mapping for Robot Exploration in Any Environment
Technical University of Munich（技术大学慕尼黑）；School of Computation, Information and Technology（计算、信息与技术学院）；ETH Zurich（苏黎世联邦理工学院）；Department of Computing（计算学院）；Imperial College London（伦敦帝国理工学院）；University of Patras（帕特拉大学）；Munich Institute of Robotics and Machine Intelligence (MIRMI)（慕尼黑机器人与机器智能研究所）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）
Visuo-Tactile Exploration of Unknown Rigid 3D Curvatures by Vision-Augmented Unified Force-Impedance Control
Chair of Robotics and Systems Intelligence, MIRMI-Munich Institute of Robotics and Machine Intelligence, Technical University of Munich（慕尼黑工业大学机器人与系统智能教席，慕尼黑机器人与机器智能研究所）；Centre for Tactile Internet with Human-in-the-Loop (CeTI)（以人为中心的触觉互联网中心）
G-DRAGON: Geospatial Reasoning and Dynamic Planning for Retrieval-Augmented Outdoor Navigation
Spatial AI & Robotics Lab（空间人工智能与机器人实验室）；University at Buffalo（布法罗大学）；School of Future Technology（未来技术学院）；Shanghai University（上海大学）；University of Chinese Academy of Sciences（中国科学院大学）
Harnessing VLM in Embodied RL: From Reward Regressor to Success Detector via Exploration-Augmented Key-Observation Gating
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所 多模态人工智能系统国家重点实验室）；School of Artificial Intelligence, University of the Chinese Academy of Sciences（中国科学院大学 人工智能学院）；AGIBOT
Motion Planner Augmented Reinforcement Learning for Robot Manipulation in Obstructed Environments
Cognitive Learning for Vision and Robotics Lab（认知视觉与机器人学实验室）；Robotic Embedded Systems Laboratory（机器人嵌入式系统实验室）；University of Southern California（南加利福尼亚大学）
