This work presents a 3D multi-robot exploration framework for a team of UGVs moving on uneven terrains. The framework was designed by casting the two-level coordination strategy presented in [1] into the context of multi-robot exploration. The resulting distributed exploration technique minimizes and explicitly manages the occurrence of conflicts and interferences in the robot team. Each robot selects where to scan next by using a receding horizon next-best-view approach [2]. A sampling-based tree is directly expanded on segmented traversable regions of the terrain 3D map to generate the candidate next viewpoints. During the exploration, users can assign locations with higher priorities on-demand to steer the robot exploration toward areas of interest. The proposed framework can be also used to perform coverage tasks in the case a map of the environment is a priori provided as input. An open-source implementation is available online.
Enhancing Multi-Robot Exploration Using Probabilistic Frontier Prioritization with Dirichlet Process Gaussian Mixtures
Institute for Systems and Robotics / LARSyS and Instituto Superior Técnico, Universidade de Lisboa（系统与机器人研究所 / LARSyS 和里斯本大学理工学院）；Instituto de Telecomunicações and Instituto Superior Técnico, Universidade de Lisboa（电信研究所和里斯本大学理工学院）
The hunter and gatherer approach copes with the problem of dynamic multi-robot task allocation, where tasks are unknowingly distributed over an environment. This approach employs two complementary teams of agents: one agile in exploring (hunters) and another dexterous in completing (gatherers) the tasks. Although this approach has been studied from the task planning point of view in our previous works, the multi-robot exploration and coordination aspects of the problem remain uninvestigated. This paper proposes a multi-robot exploration algorithm for hunters based on innovative notions of "exp…
University of Technology Sydney（悉尼科技大学）；Defence Science and Technology Group（国防科技集团）；Mission Systems Pty. Ltd.（任务系统私人有限公司）
Heterogeneous multi-robot systems are advantageous for operations in unknown environments because functionally specialised robots can gather environmental information, while others perform tasks. We define this decomposition as the scout-task robot architecture and show how it avoids the need to explicitly balance exploration and exploitation~by permitting the system to do both simultaneously. The challenge is to guide exploration in a way that improves overall performance for time-limited tasks. We derive a novel upper confidence bound for simultaneous exploration and exploitation based on mu…
University of Nevada, Reno（内华达大学里诺分校）；NTNU（挪威科技大学）；ETH Zurich（苏黎世联邦理工学院）
This paper presents a novel strategy for autonomous teamed exploration of subterranean environments using legged and aerial robots. Tailored to the fact that subterranean settings, such as cave networks and underground mines, often involve complex, large-scale and multi-branched topologies, while wireless communication within them can be particularly challenging, this work is structured around the synergy of an onboard exploration path planner that allows for resilient long-term autonomy, and a multi-robot coordination framework. The onboard path planner is unified across legged and flying rob…
MOSAIC: Modular Scalable Autonomy for Intelligent Coordination of Heterogeneous Robotic Teams
FZI Forschungszentrum für Informatik, Karlsruhe, Germany（弗赖堡信息研究中心，卡尔斯鲁厄，德国）；Robotic Systems Lab (RSL), ETH Zürich, Zürich, Switzerland（机器人系统实验室（RSL），苏黎世联邦理工学院，苏黎世，瑞士）；Machine Intelligence and Robotics Lab (MaiRo), Karlsruhe Institute for Technology (KIT), Karlsruhe, Germany（机器智能与机器人实验室（MaiRo），卡尔斯鲁厄技术学院（KIT），卡尔斯鲁厄，德国）；European Space Agency (ESA), Noordwijk, Netherlands（欧洲航天局（ESA），诺德威克，荷兰）
University of Maryland（马里兰大学）；University of Pennsylvania（宾夕法尼亚大学）；Virginia Tech（弗吉尼亚理工大学）
Submodular maximization has been widely used in many multi-robot task planning problems including information gathering, exploration, and target tracking. However, the interplay between submodular maximization and communication is rarely explored in the multi-robot setting. In many cases, maximizing the submodular objective may drive the robots in a way so as to disconnect the communication network. Driven by such observations, in this paper, we consider the problem of maximizing submodular function with connectivity constraints. Specifically, we propose a problem called Communication-aware Su…
Jet Propulsion Laboratory, California Institute of Technology（加州理工学院喷气推进实验室）；Instituto Tecnológico Vale（淡水河谷技术研究所）；University of Southern California（南加州大学）；Cornell University（康奈尔大学）；University of California, Riverside（加州大学河滨分校）；Polytechnique Montréal（蒙特利尔理工学院）；Institut de Robòtica i Informàtica Industrial, CSIC-UPC（西班牙高等科学研究委员会-加泰罗尼亚理工大学机器人与工业信息研究所）；California Institute of Technology（加州理工学院）
Communication is an important capability for multi-robot exploration because (1) inter-robot communication (comms) improves coverage efficiency and (2) robot-to-base comms improves situational awareness. Exploring comms-restricted (e.g., subterranean) environments requires a multi-robot system to tolerate and anticipate intermittent connectivity, and to carefully consider comms requirements, otherwise mission-critical data may be lost. In this paper, we describe and analyze ACHORD (Autonomous & Collaborative High-Bandwidth Operations with Radio Droppables), a multi-layer networking solution wh…
OpenSpace Lab Solution to the IROS 2026 Indoor Exploration Competition
China University of Mining and Technology-Beijing（中国矿业大学（北京））；Macau University of Science and Technology（澳门科技大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Shenzhen University（深圳大学）；Technical University of Munich（慕尼黑工业大学）
University of Calabria（卡拉布里亚大学）；Middlesex University London（伦敦密德萨斯大学）
The mine detection in an unexplored area is an optimization problem where multiple mines, randomly distributed throughout an area, need to be discovered and disarmed in a minimum amount of time. We propose a strategy to explore an unknown area, using a stigmergy approach based on ants behavior, and a novel swarm based protocol to recruit and coordinate robots for disarming the mines cooperatively. Simulation tests are presented to show the effectiveness of our proposed Ant-based Task Robot Coordination (ATRC) with only the exploration task and with both exploration and recruiting strategies. M…
University of Calabria（卡拉布里亚大学）；Middlesex University London（伦敦密德萨斯大学）
This work addresses the coordination problem of multiple robots with the goal of finding specific hazardous targets in an unknown area and dealing with them cooperatively. The desired behaviour for the robotic system entails multiple requirements, which may also be conflicting. The paper presents the problem as a constrained bi-objective optimization problem in which mobile robots must perform two specific tasks of exploration and at same time cooperation and coordination for disarming the hazardous targets. These objectives are opposed goals, in which one may be favored, but only at the expen…
Can LLM Agents Solve Collaborative Tasks? A Study on Urgency-Aware Planning and Coordination
Computer Vision and Robotics Laboratory (VeRLab), Department of Computer Science, Universidade Federal de Minas Gerais（计算机视觉与机器人实验室（VeRLab）、计算机科学系、巴西联邦大学矿务格里斯大学）
LERR-Nav: Learning to Coordinate Exploration, Recovery, and Reminiscing for Object Navigation
Hong Kong Polytechnic University（香港理工大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；DeepRobotics（深度机器人公司）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Guangzhou University（广州大学）
SLEI3D: Simultaneous Exploration and Inspection via Heterogeneous Fleets under Limited Communication
School of Advanced Manufacturing and Robotics, Peking University（北京大学先进制造与机器人学院）；Division of Natural and Applied Sciences, Duke Kunshan University（杜克昆山大学自然科学与应用科学系）
C$^2$-Explorer: Contiguity-Driven Task Allocation with Connectivity-Aware Task Representation for Decentralized Multi-UAV Exploration
School of Intelligence Science and Engineering, Harbin Institute of Technology, Shenzhen, Guangdong, China（哈尔滨工业大学深圳研究院）；Robotics and Autonomous Systems Thrust, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）机器人与自主系统方向）；Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China（南方科技大学机械与能源工程系）
Underwater Embodied Intelligence for Autonomous Robots: A Constraint-Coupled Perspective on Planning, Control, and Deployment
Department of Mechanical Engineering, The University of Hong Kong（香港大学机械工程系）；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生学院）；Department of Data and Systems Engineering, The University of Hong Kong（香港大学数据与系统工程系）；Department of Data Science, New Jersey Institute of Technology（新泽西理工学院数据科学系）
As robots become ubiquitous in the workforce, it is essential that human-robot collaboration be both intuitive and adaptive. A robot's quality improves based on its ability to explicitly reason about the time-varying (i.e. learning curves) and stochastic capabilities of its human counterparts, and adjust the joint workload to improve efficiency while factoring human preferences. We introduce a novel resource coordination algorithm that enables robots to explore the relative strengths and learning abilities of their human teammates, by constructing schedules that are robust to stochastic and ti…
Learning to Recover: Dynamic Reward Shaping with Wheel-Leg Coordination for Fallen Robots
Humanoids and Human-Centered Mechatronics (HHCM), Istituto Italiano di Tecnologia（人类oids与人本机电系统（HHCM），意大利技术研究所）；Ph.D. Program of National Interest in Robotics and Intelligent Machines (DRIM), University of Genova（机器人与智能机器国家利益博士项目（DRIM），热那亚大学）；Department of Computer Science, University College London（计算机科学系，伦敦大学学院）
Value-Aware Prediction for Robust Multi-Agent Coordination Under Communication Loss
Istanbul Technical University（伊斯坦布尔技术大学）；University of Stuttgart（斯图加特大学）；Istanbul Technical University Artificial Intelligence and Data Science Application and Research Center（伊斯坦布尔技术大学人工智能与数据科学应用与研究中心）；Stanford University（斯坦福大学）
Structural Induced Exploration for Balanced and Scalable Multi-Robot Path Planning
Department of Artificial Intelligence, School of Electronics Engineering, Kyungpook National University（人工智能系，电子工程学院，韩国庆北国立大学）；Department of IT Convergence Engineering, Kumoh National Institute of Technology（IT融合工程系，木谷国立技术学院）
