Mapping and navigation have gone hand-in-hand since long before robots existed. Maps are a key form of communication, allowing someone who has never been somewhere to nonetheless navigate that area successfully. In the context of multi-robot systems, the maps and information that flow between robots are necessary for effective collaboration, whether those robots are operating concurrently, sequentially, or completely asynchronously. In this paper, we argue that maps must go beyond encoding purely geometric or visual information to enable increasingly complex autonomy, particularly between robots. We propose a framework for multi-robot autonomy, focusing in particular on air and ground robots operating in outdoor 2.5D environments. We show that semantic maps can enable the specification, planning, and execution of complex collaborative missions, including localization in GPS-denied settings. A distinguishing characteristic of this work is that we strongly emphasize field experiments and testing, and by doing so demonstrate that these ideas can work at scale in the real world. We also perform extensive simulation experiments to validate our ideas at even larger scales. We believe these experiments and the experimental results constitute a significant step forward toward advancing the state-of-the-art of large-scale, collaborative multi-robot systems operating with real communication, navigation, and perception constraints.
PanoDP: Learning Collision-Free Navigation with Panoramic Depth and Differentiable Physics
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore（南洋理工大学电子与电气工程学院）；Beihang University, Beijing, China（北京航空航天大学）；The University of Queensland, Australia（昆士兰大学）
Large-Scale Tunnel Air-Ground Collaboration With FLISP: Fast LiDAR-IMU Synchronized Path Planner
Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University（同济大学上海自主智能无人系统科学中心）；State Key Laboratory of Autonomous Intelligent Unmanned Systems, Tongji University（同济大学自主智能无人系统全国重点实验室）；College of Electronic and Information Engineering, Tongji University（同济大学电子与信息工程学院）
AirSimAG: A High-Fidelity Simulation Platform for Air-Ground Collaborative Robotics
School of Aeronautic Science and Engineering, Beihang University（北京航空航天大学航空科学与工程学院）；Hangzhou International Innovation Institute, Beihang University（北京航空航天大学杭州国际创新研究院）；Institution of Unmanned System, Beihang University（北京航空航天大学无人系统研究院）
Visual navigation for airborne control of ground robots from tethered platform: creation of the first prototype
我们提出了地面机器人的协调控制系统。我们开发了机器人有效的协调使用位于塔上的设备或系留的空中设备跟踪控制区域的机器人和监督他们的环境，包括自然和人工标记。这种系统的简单原型是在Ariel大学应用数学实验室（Domoshnitsky Alexander教授的监督下）与公司TRANSIST VIDEO LLC（Skolkovo，莫斯科）合作创建的。我们计划创建更复杂的原型使用卡明格兰特（以色列）
OmniVLN: Omnidirectional 3D Perception and Token-Efficient LLM Reasoning for Visual-Language Navigation across Air and Ground Platforms
CertaintyX；School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电子与电气工程学院）；Robotics Institute, Carnegie Mellon University（卡内基梅隆大学机器人研究所）
