Perception-Aware Communication-Free Multi-UAV Coordination in the Wild

• URL: http://arxiv.org/abs/2603.08379v1
• Date: Mon, 09 Mar 2026 13:41:12 GMT
• Title: Perception-Aware Communication-Free Multi-UAV Coordination in the Wild
• Abstract summary: We present a communication-free method for safe multi-robot coordination in complex environments such as forests with dense canopy cover.<n>Our approach relies on an onboard anisotropic 3D LiDAR sensor used for SLAM as well as for detecting obstacles and neighboring robots.<n>We develop a novel perception-aware 3D navigation framework that enables robots to safely and effectively progress toward a goal region despite limited sensor field-of-view.
• Score: 4.317027632626673
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a communication-free method for safe multi-robot coordination in complex environments such as forests with dense canopy cover, where GNSS is unavailable. Our approach relies on an onboard anisotropic 3D LiDAR sensor used for SLAM as well as for detecting obstacles and neighboring robots. We develop a novel perception-aware 3D navigation framework that enables robots to safely and effectively progress toward a goal region despite limited sensor field-of-view. The approach is evaluated through extensive simulations across diverse scenarios and validated in real-world field experiments, demonstrating its scalability, robustness, and reliability.

Related papers

• The Unified Autonomy Stack: Toward a Blueprint for Generalizable Robot Autonomy [13.105188128582567]
  We introduce and open-source the Unified Autonomy Stack, a system-level solution that enables resilient autonomy across aerial and ground robot morphologies.<n>The architecture centers on three modules -- multi-modal perception, multi-behavior planning, and multi-layered safe navigation.<n>The stack has been field-tested and validated on both aerial (rotorcraft) and ground (legged) robots.
  arXiv  Detail & Related papers  (2026-05-12T20:39:35Z)
• IMAGINE: Intelligent Multi-Agent Godot-based Indoor Networked Exploration [0.0]
  This paper implements Multi-Agent Reinforcement Learning (MARL) to address challenges in a 2D indoor environment.<n>Policy training aims to achieve emergent collaborative behaviours and decision-making under uncertainty.
  arXiv  Detail & Related papers  (2026-02-02T22:08:41Z)
• SLEI3D: Simultaneous Exploration and Inspection via Heterogeneous Fleets under Limited Communication [10.496161804810473]
  This work proposes a novel planning and coordination framework (SLEI3D) that integrates the online strategies for collaborative 3D exploration, adaptive inspection and timely communication.<n>The proposed framework is validated extensively via high-fidelity simulations of numerous large-scale missions with up to 48 robots and 384 thousand cubic meters.
  arXiv  Detail & Related papers  (2026-01-01T02:06:35Z)
• Near-Field Perception for Safety Enhancement of Autonomous Mobile Robots in Manufacturing Environments [1.4627307351436138]
  This paper presents a three-tier near-field perception framework for autonomous mobile robots.<n>The first approach employs light-discontinuity detection, which projects a laser stripe across the near-field zone.<n>The second approach utilizes light-displacement measurement to estimate object height.<n>The third approach employs a computer vision-based object detection model on embedded AI hardware to classify objects.
  arXiv  Detail & Related papers  (2025-12-15T17:18:04Z)
• AerialMind: Towards Referring Multi-Object Tracking in UAV Scenarios [64.51320327698231]
  We introduce AerialMind, the first large-scale RMOT benchmark in UAV scenarios.<n>We develop an innovative semi-automated collaborative agent-based labeling assistant framework.<n>We also propose HawkEyeTrack, a novel method that collaboratively enhances vision-language representation learning.
  arXiv  Detail & Related papers  (2025-11-26T04:44:27Z)
• Decentralized Vision-Based Autonomous Aerial Wildlife Monitoring [55.159556673975544]
  We propose a decentralized vision-based multi-quadrotor system for wildlife monitoring.<n>Our approach enables robust identification and tracking of large species in their natural habitat.
  arXiv  Detail & Related papers  (2025-08-20T20:05:05Z)
• Design and Experimental Validation of an Autonomous USV for Sensor Fusion-Based Navigation in GNSS-Denied Environments [2.915868985330569]
  MARVEL is an unmanned surface vehicle built for real-world testing of sensor fusion-based navigation algorithms in-denied environments.<n>It integrates electromagnetic logs, Doppler velocity logs, inertial sensors, and real-time kinematic positioning.<n>MARVEL enables real-time, in-situ validation of advanced navigation and AI-driven algorithms using redundant, synchronized sensors.
  arXiv  Detail & Related papers  (2025-03-30T13:50:46Z)
• Enhancing Autonomous Navigation by Imaging Hidden Objects using Single-Photon LiDAR [12.183773707869069]
  We present a novel approach that leverages Non-Line-of-Sight (NLOS) sensing using single-photon LiDAR to improve visibility and enhance autonomous navigation.<n>Our method enables mobile robots to "see around corners" by utilizing multi-bounce light information.
  arXiv  Detail & Related papers  (2024-10-04T16:03:13Z)
• HUM3DIL: Semi-supervised Multi-modal 3D Human Pose Estimation for Autonomous Driving [95.42203932627102]
  3D human pose estimation is an emerging technology, which can enable the autonomous vehicle to perceive and understand the subtle and complex behaviors of pedestrians. Our method efficiently makes use of these complementary signals, in a semi-supervised fashion and outperforms existing methods with a large margin. Specifically, we embed LiDAR points into pixel-aligned multi-modal features, which we pass through a sequence of Transformer refinement stages.
  arXiv  Detail & Related papers  (2022-12-15T11:15:14Z)
• Incremental 3D Scene Completion for Safe and Efficient Exploration Mapping and Planning [60.599223456298915]
  We propose a novel way to integrate deep learning into exploration by leveraging 3D scene completion for informed, safe, and interpretable mapping and planning. We show that our method can speed up coverage of an environment by 73% compared to the baselines with only minimal reduction in map accuracy. Even if scene completions are not included in the final map, we show that they can be used to guide the robot to choose more informative paths, speeding up the measurement of the scene with the robot's sensors by 35%.
  arXiv  Detail & Related papers  (2022-08-17T14:19:33Z)
• Autonomous Aerial Robot for High-Speed Search and Intercept Applications [86.72321289033562]
  A fully-autonomous aerial robot for high-speed object grasping has been proposed. As an additional sub-task, our system is able to autonomously pierce balloons located in poles close to the surface. Our approach has been validated in a challenging international competition and has shown outstanding results.
  arXiv  Detail & Related papers  (2021-12-10T11:49:51Z)
• 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)

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.