Frontier-Based Exploration

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Frontier-based exploration studies how robots can choose where to move next by identifying boundaries between known and unknown space. Common methods rank frontier viewpoints using information gain, travel cost, terrain and energy constraints, semantic cues, or task goals, then combine these scores with mapping and path planning. Work spans indoor and outdoor robots, drones, quadrupeds, and multi-robot teams, with evaluations in simulated environments and real-world exploration and navigation tasks. A central challenge is balancing coverage and task success against time, energy, safety, and computation while handling uncertain predictions, difficult terrain, and changing communication or mission constraints.

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27 papers0 papers in the last four weeks, down 100% on the four weeks before. 0.0% of all new papers.

WOLF: World Model Guided LiDAR Exploration with Predictive Frontiers
Sep 20, 2026Yuyang Tian, Penghui Yang, Pengyuan Wu +7World Model Learning UAV Navigation
University of Science and Technology of China, Hefei, China. · Shanghai Artificial Intelligence Laboratory, Shanghai, China. · East China University of Science and Technology, Shanghai, China. +3
TASG-Explore: Traversability-Aware Sector-Guided Exploration for Ground Robot on Uneven Terrain
Sep 8, 2026Shaocong Wang, Shiliang Shao, Ting Wang +2Off-Road Robot Navigation Mobile Robotics
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China · University of Chinese Academy of Sciences, Beijing 101408, China · Key Laboratory of Maritime Intelligent Network Information Technology, Ministry of Education, Hohai University, Nanjing 210098, China
Connectivity-Aware Graph Extension for Decentralized Multi-Robot Exploration
Sep 1, 2026Béatrice Garcia Cegarra, Elena Vanneaux, Quentin Picard +1Multi-Robot Exploration Multi-Robot Coordination
⋆Pˆole recherche, AMIAD, Palaiseau, France · †UI2S, ENSTA, IP Paris, Palaiseau, France
An Agentic Retrobiosynthesis Framework with Learned Frontier Selection
Aug 31, 2026Philippe Meyer, Guillaume Gricourt, Thomas Duigou +2Frontier-Based Exploration Large Language Model-Guided Scientific Discovery
1Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France
Situation Aware Frontier Prioritization for Quadruped Search and Rescue
Aug 3, 2026Kevin Farias, Santiago Martin, Barbara Flores +5Exploration-Exploitation Tradeoff Robotics
Robotics and AI Lab, Technological University of Uruguay, Rivera, Uruguay
SLIDER: Sparse History-Guided Aerial Robot Target Search using Sliding Local Maps
Jul 12, 2026Xiaolei Hou, Zheng Pan, Hua Lan +6Aerial Robotics Robot Navigation
TopoExplore: Topological Discrimination for Archive-Based Exploration
Jul 10, 2026Jason CarlsonRL Exploration Frontier-Based Exploration
TAPE: Tether-Aware Path Planning for Autonomous Exploration of Unknown 3D Cavities Using a Tangle-Compatible Tethered Aerial Robot
Jun 29, 2026Louis Petit, Alexis Lussier DesbiensAerial Robotics Mobile Robotics
Createk Design Lab, University of Sherbrooke, Sherbrooke, J1K 2R1, Canada
SPACE: Swarm Pheromone Fields for Adaptive Collision-Aware Exploration
Jun 28, 2026Haohua Que, Haojia Gao, Mingkai Liu +3Robot Collision Avoidance Swarm Robotics
Department of Electronic Engineering, Tsinghua University, Beijing, China. · Tsinghua Shenzhen International Graduate School (SIGS), Tsinghua University, Shenzhen, China. · School of Software and Microelectronics, Peking University, Beijing, China.
AnyGoal: Vision-Language Guided Multi-Agent Exploration for Training-Free Lifelong Navigation
Jun 11, 2026MoniJesu James, Marcelino Julio Fernando, Miguel Altamirano Cabrera +1Multi-Robot Systems Robot Navigation
Rethinking Embodied Navigation via Relational Inductive Bias
Jun 9, 2026Weitao An, Chenghao Xu, Xu Yang +1Embodied Navigation Relational Reasoning
School of Electronic Engineering, Xidian University, Xi’an 710071, China · School of Information Science and Engineering, Hohai University, Nanjing 210098, China
IntentNav: Learning Spatial-Visual Object Navigation from Human Demonstrations
Jun 6, 2026Yuxin Cai, Zongtai Li, Maonan Wang +9VLMs for Robotics Robot Imitation Learning
Nanyang Technological University · Carnegie Mellon University · The Chinese University of Hong Kong +1
G-DRAGON: Geospatial Reasoning and Dynamic Planning for Retrieval-Augmented Outdoor Navigation
May 25, 2026Dongzhihan Wang, Yi Du, Jianan Sun +5Off-Road Robot Navigation Large Language Model-Based Robot Planning
School of Future Technology, Shanghai University, Shanghai 200444, China
OPAL: Omnidirectional Path-efficient Aerial 3D expLoration
May 25, 2026Yoga Satwik Chappidi, Avideh ZakhorAerial Robotics Mobile Robotics
Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, USA.
Autonomous Frontier-Based Exploration with VLM Guidance
May 22, 2026Aarush Aitha, Avideh ZakhorMobile Robotics Frontier-Based Exploration
EECS Department, University of California
Semantic-Aware Guided Drone Exploration for Language-Conditioned 3D Indoor Mapping
May 22, 2026Nitin Vegesna, Avideh ZakhorAerial Robotics UAV Navigation
Department of Electrical Engineering and Computer Sciences University of California, Berkeley Berkeley, CA, USA
TravExplorer: Cross-Floor Embodied Exploration via Traversability-Aware 3-D Planning
May 19, 2026Han Zheng, Zhe Chen, Yudong Huang +4Embodied Navigation Robot Navigation
Shanghai Jiao Tong University, Shanghai, China.
ScreenSearch: Uncertainty-Aware OS Exploration
May 15, 2026Michael Solodko, Justin WagleComputer-Use Agents Monte Carlo Tree Search
Microsoft
Palm-sized Omnidirectional Vision-Based UAV Exploration with Sparse Topological Map Guidance
May 8, 2026Zirui Wang, Xinjia Luo, Haotian Sun +3Robot Navigation UAV Navigation
Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
PAC-CF: Calibrating Irreversible Frontier Pruning in LLM-Guided Search
Apr 15, 2026Tianhao Qian, Jiayu Chen, Zhenyu Sun +1LLM Inference Efficiency Inference-Time Search
The Chinese University of Hong Kong, Shenzhen · The University of Hong Kong · TikTok
Hypothesis Graph Refinement: Hypothesis-Driven Exploration with Cascade Error Correction for Embodied Navigation
Apr 5, 2026Peixin Chen, Guoxi Zhang, Jianwei Ma +1Embodied Navigation Graph-Based Agent Memory
Harbin Institute of Technology · Beijing Institute for General Artificial Intelligence · Peking University
Enhancing Multi-Robot Exploration Using Probabilistic Frontier Prioritization with Dirichlet Process Gaussian Mixtures
Apr 3, 2026John Lewis Devassy, Meysam Basiri, Mário A. T. Figueiredo +1Dirichlet Process Mixture Models Multi-Robot Exploration
Institute for Systems and Robotics / LARSyS and Instituto Superior Técnico, Universidade de Lisboa, Lisbon 1049-001, Portugal · Instituto de Telecomunicações and Instituto Superior Técnico, Universidade de Lisboa, Lisbon 1049-001, Portugal
Asymptotically-Bounded 3D Frontier Exploration enhanced with Bayesian Information Gain
Apr 3, 2026John Lewis, Meysam Basiri, Pedro U. LimaInformation Gain Frontier-Based Exploration
Institute for Systems and Robotics, Instituto Superior Técnico, Universidade de Lisboa, Lisbon 1049-001, Portugal
Escaping Offline Pessimism: Vector-Field Reward Shaping for Safe Frontier Exploration
Mar 18, 2026Amirhossein Roknilamouki, Arnob Ghosh, Eylem Ekici +1RL Exploration Reward Design for RL
Department of Electrical and Computer Engineering, The Ohio State University · Department of Electrical and Computer Engineering, New Jersey Institute of Technology · Department of Computer Science and Engineering, The Ohio State University.
EAAE: Energy-Aware Autonomous Exploration for UAVs in Unknown 3D Environments
Mar 16, 2026Jacob Elskamp, Moji Shi, Leonard Bauersfeld +2Mobile Robotics UAV Navigation
Faculty of Aerospace Engineering, Delft University of Technology, Delft, The Netherlands · Institute for Informatics, University of Zurich, Zurich, Switzerland
R2F: Repurposing Ray Frontiers for LLM-free Object Navigation
Mar 9, 2026Francesco Argenziano, John Mark Alexis Marcelo, Michele Brienza +5Open-Vocabulary Object Navigation Vision-Language Navigation
Department of Computer, Automation and Management Engineering, Sapienza University of Rome, Rome, Italy · Department of Electronics and Automation, Mohamed Khider University of Biskra, Biskra, Algeria · International University of Rome UNINT, Rome, Italy
Temporal-Logic-Aware Frontier-Based Exploration
Feb 21, 2026Azizollah Taheri, Derya AksaraySTL Motion Planning Motion Planning
Department of Electrical and Computer Engineering at Northeastern University.

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WOLF: World Model Guided LiDAR Exploration with Predictive Frontiers

Yuyang Tian, Penghui Yang, Pengyuan Wu, Haoran Yang+6

University of Science and Technology of China, Hefei, China. · Shanghai Artificial Intelligence Laboratory, Shanghai, China. · East China University of Science and Technology, Shanghai, China.+3

World Model Learning UAV Navigation Frontier-Based Exploration

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LiDAR-based unmanned aerial vehicle (UAV) exploration builds maps by continually selecting where to observe next. However, decisions based on the measured map provide limited foresight into spatial continuations behind occlusions, leaving potentially informative directions unrecognized. We present WOLF, a world-model-guided framework that predicts future observations to enhance autonomous exploration. In the training stage, a recurrent world model learns observation dynamics from exploration trajectories, with recurrent memory retaining the spatial context needed to interpret partial observations across successive views. Building on this context, the model combines observation history with candidate motions during exploration to predict local occupancy and visibility. To guide further sensing, a predictive frontier generation mechanism then aligns and fuses these predictions using confidence, branch agreement, and observation quality to identify promising regions. The resulting predictive frontiers join measured ones to guide geometric viewpoint selection and trajectory generation, while new scans update subsequent predictions. In simulations, our method reduces mean terminal time by 10.9% relative to EPIC in Garage at comparable coverage and increases mean coverage from 42.12% to 98.35% in Tunnel. Real-world experiments further demonstrate onboard deployment of the learned model for online inference during physical flight.