Line-of-Sight-Constrained Multi-Robot Mapless Navigation via Polygonal Visible Regions

• URL: http://arxiv.org/abs/2603.26314v1
• Date: Fri, 27 Mar 2026 11:33:25 GMT
• Title: Line-of-Sight-Constrained Multi-Robot Mapless Navigation via Polygonal Visible Regions
• Abstract summary: This paper studies the line-of-sight (LoS) connectivity maintenance problem in multi-robot navigation with unknown obstacles.<n>We propose an inherently distributed approach where each robot only constructs an egocentric visible region based on its real-time LiDAR scans.<n>Results show that it reliably maintains LoS-connectivity between robots in challenging environments cluttered with obstacles.
• Score: 34.42294321573021
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-robot systems rely on underlying connectivity to ensure reliable communication and timely coordination. This paper studies the line-of-sight (LoS) connectivity maintenance problem in multi-robot navigation with unknown obstacles. Prior works typically assume known environment maps to formulate LoS constraints between robots, which hinders their practical deployment. To overcome this limitation, we propose an inherently distributed approach where each robot only constructs an egocentric visible region based on its real-time LiDAR scans, instead of endeavoring to build a global map online. The individual visible regions are shared through distributed communication to establish inter-robot LoS constraints, which are then incorporated into a multi-robot navigation framework to ensure LoS-connectivity. Moreover, we enhance the robustness of connectivity maintenance by proposing a more accurate LoS-distance metric, which further enables flexible topology optimization that eliminates redundant and effort-demanding connections. The proposed framework is evaluated through extensive multi-robot navigation and exploration tasks in both simulation and real-world experiments. Results show that it reliably maintains LoS-connectivity between robots in challenging environments cluttered with obstacles, even under large visible ranges and fragile minimal topologies, where existing methods consistently fail. Ablation studies also reveal that topology optimization boosts navigation efficiency by around $20\%$, demonstrating the framework's potential for efficient navigation under connectivity constraints.

Related papers

• AutoPath: Learning Transferable Goal-Conditioned Stochastic Path Prior for Safe Navigation Without Human Demonstrations [103.61864552260316]
  Real-time navigation in dynamic environments requires collision-free and dynamically feasible motion under limited perception.<n>We formulate navigation as learning a transferable goal-aligned path prior that models a reusable distribution over goal-aligned geometry-consistent local paths conditioned on local observations.<n>We develop a structured prior learning framework that parameterizes local paths using a geometry-aware polar action manifold and incorporates risk-sensitive utility shaping with multi-goal distributional rollouts for stable and safety-aware planning.
  arXiv  Detail & Related papers  (2026-07-13T16:00:41Z)
• Simulation-Informed Diffusion for Decentralized Multi-robot Motion Planning [56.240199425429445]
  Multi-robot motion planning requires each robot to generate collision-free trajectories from local observations.<n>Most existing planners generate trajectories from a static snapshot of the local observation.<n>This paper introduces Simulation-Informed Diffusion (SID), a decentralized framework built on constraint-aware diffusion models.
  arXiv  Detail & Related papers  (2026-05-26T21:17:53Z)
• Decentralized Heterogeneous Multi-Robot Collaborative Exploration for Indoor and Outdoor 3D Environments [11.81511178687002]
  This paper proposes a decentralized collaborative framework for heterogeneous multi-robot systems.<n>The framework coordinates exploration tasks among heterogeneous robots, achieving superior exploration efficiency and communication savings.
  arXiv  Detail & Related papers  (2026-04-26T13:16:13Z)
• Towards Edge Intelligence via Autonomous Navigation: A Robot-Assisted Data Collection Approach [7.871361120036574]
  Existing robot-assisted data collection methods face significant challenges in achieving reliable and efficient performance, especially in non-line-of-sight (NLoS) environments.<n>This paper proposes a communication-driven dual-MMD (CLD) navigation scheme that incorporates region-aware propagation characteristics and a non-point collision propagation characteristics.<n>It is also shown that CLD can adapt to different scenarios by flexibly adjusting the weight factor among navigation, communication and learning objectives.
  arXiv  Detail & Related papers  (2026-04-04T07:40:26Z)
• VORL-EXPLORE: A Hybrid Learning Planning Approach to Multi-Robot Exploration in Dynamic Environments [19.811711277536187]
  Multi-robot exploration commonly decouples frontier allocation from local navigation.<n>We propose VORL-EXPLORE, a hybrid learning and planning framework that addresses this limitation through execution fidelity.<n>It also drives a risk-aware adaptive arbitration mechanism between global A* guidance and a reactive reinforcement learning policy.
  arXiv  Detail & Related papers  (2026-03-09T05:20:33Z)
• Hierarchical Trajectory Planning of Floating-Base Multi-Link Robot for Maneuvering in Confined Environments [9.325443556436436]
  Floating-base multi-link robots can change their shape during flight, making them well-suited for applications in confined environments.<n>This work introduces a hierarchical trajectory planning framework that integrates global guidance with configuration-aware local optimization.<n>To the best of our knowledge, this is the first planning framework for floating-base multi-link robots that has been demonstrated on a real robot.
  arXiv  Detail & Related papers  (2026-02-25T22:49:54Z)
• CHORAL: Traversal-Aware Planning for Safe and Efficient Heterogeneous Multi-Robot Routing [4.3992789649203194]
  Monitoring large, unknown, and complex environments with autonomous robots poses significant navigation challenges.<n>We propose an integrated semantic-aware framework for coordinating heterogeneous robots.<n> Experiments in simulation and in a real inspection mission with three robotic platforms demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2026-01-15T12:34:22Z)
• Dynamic Cross-Modal Feature Interaction Network for Hyperspectral and LiDAR Data Classification [66.59320112015556]
  Hyperspectral image (HSI) and LiDAR data joint classification is a challenging task.<n>We propose a novel Dynamic Cross-Modal Feature Interaction Network (DCMNet)<n>Our approach introduces three feature interaction blocks: Bilinear Spatial Attention Block (BSAB), Bilinear Channel Attention Block (BCAB), and Integration Convolutional Block (ICB)
  arXiv  Detail & Related papers  (2025-03-10T05:50:13Z)
• SCoTT: Strategic Chain-of-Thought Tasking for Wireless-Aware Robot Navigation in Digital Twins [78.53885607559958]
  We propose SCoTT, a wireless-aware path planning framework.<n>We show that SCoTT achieves path gains within 2% of DP-WA* while consistently generating shorter trajectories.<n>We also show the practical viability of our approach by deploying SCoTT as a ROS node within Gazebo simulations.
  arXiv  Detail & Related papers  (2024-11-27T10:45:49Z)
• From Simulations to Reality: Enhancing Multi-Robot Exploration for Urban Search and Rescue [46.377510400989536]
  We present a novel hybrid algorithm for efficient multi-robot exploration in unknown environments with limited communication and no global positioning information. We redefine the local best and global best positions to suit scenarios without continuous target information. The presented work holds promise for enhancing multi-robot exploration in scenarios with limited information and communication capabilities.
  arXiv  Detail & Related papers  (2023-11-28T17:05:25Z)
• Active Visual Localization for Multi-Agent Collaboration: A Data-Driven Approach [47.373245682678515]
  This work investigates how active visual localization can be used to overcome challenges of viewpoint changes. Specifically, we focus on the problem of selecting the optimal viewpoint at a given location. The result demonstrates the superior performance of the data-driven approach when compared to existing methods.
  arXiv  Detail & Related papers  (2023-10-04T08:18:30Z)
• Decentralized Global Connectivity Maintenance for Multi-Robot Navigation: A Reinforcement Learning Approach [12.649986200029717]
  This work investigates how to navigate a multi-robot team in unknown environments while maintaining connectivity. We propose a reinforcement learning approach to develop a decentralized policy, which is shared among multiple robots. We validate the effectiveness of the proposed approach by comparing different combinations of connectivity constraints and behavior cloning.
  arXiv  Detail & Related papers  (2021-09-17T13:20:19Z)
• SABER: Data-Driven Motion Planner for Autonomously Navigating Heterogeneous Robots [112.2491765424719]
  We present an end-to-end online motion planning framework that uses a data-driven approach to navigate a heterogeneous robot team towards a global goal. We use model predictive control (SMPC) to calculate control inputs that satisfy robot dynamics, and consider uncertainty during obstacle avoidance with chance constraints. recurrent neural networks are used to provide a quick estimate of future state uncertainty considered in the SMPC finite-time horizon solution. A Deep Q-learning agent is employed to serve as a high-level path planner, providing the SMPC with target positions that move the robots towards a desired global goal.
  arXiv  Detail & Related papers  (2021-08-03T02:56:21Z)
• Risk-Averse MPC via Visual-Inertial Input and Recurrent Networks for Online Collision Avoidance [95.86944752753564]
  We propose an online path planning architecture that extends the model predictive control (MPC) formulation to consider future location uncertainties. Our algorithm combines an object detection pipeline with a recurrent neural network (RNN) which infers the covariance of state estimates. The robustness of our methods is validated on complex quadruped robot dynamics and can be generally applied to most robotic platforms.
  arXiv  Detail & Related papers  (2020-07-28T07:34:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.