Distributed Coordination for Resilient Multi-UAV Remote Sensing: A Photovoltaic Inspection Case Study
- URL: http://arxiv.org/abs/2607.24482v1
- Date: Mon, 27 Jul 2026 14:17:48 GMT
- Title: Distributed Coordination for Resilient Multi-UAV Remote Sensing: A Photovoltaic Inspection Case Study
- Abstract summary: deploying multiple UAVs for remote sensing enables proportional reductions in mission time.<n>In inspection campaigns, where mission value depends on complete coverage and the usability of every capture, a centralized ground-station coordinator is a single point of failure.<n>We propose the textbfSLink, an inter-agent communication infrastructure that non-invasively extends any existing aerial framework with peer-to-peer coordination capability.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Deploying multiple UAVs for remote sensing enables proportional reductions in mission time, but realizing these benefits requires the fleet to coordinate at runtime: distributing sensing targets, responding to platform failures, and recovering from degraded data quality. In inspection campaigns, where mission value depends on complete coverage and the usability of every capture, a centralized ground-station coordinator is a single point of failure: a lost link or station fault leaves sensing gaps that cannot be filled without operator intervention. We propose the \textbf{SwarmLink}, an inter-agent communication infrastructure that non-invasively extends any existing aerial framework with peer-to-peer coordination capability, without modifying the host system. We apply it to photovoltaic plant inspection as a representative large-scale sensing campaign, extending Aerostack2 with a distributed auction that unifies initial sensing-target allocation, platform-failure recovery, and data-quality-triggered reassignment into a single runtime mechanism. All three disruption scenarios reduce to the same re-auction over remaining targets and active platforms, requiring zero modifications to the Aerostack2 core and no ground-station involvement during the mission.
Related papers
- EECTracker: Swarm Motion Prior-Guided Feature Compensation for Airborne Optical UAV Swarm Tracking [2.6963400582429036]
EECTracker is a swarm-motion-prior-guided joint detection-and-tracking framework for airborne optical UAV swarm tracking.<n>We show that EECTracker achieves superior overall tracking performance compared with state-of-the-art methods.
arXiv Detail & Related papers (2026-09-14T07:57:47Z) - MA-HEAD-Net: Adaptive Rule-Guided Multi-Agent DRL for AoI Minimization in UAV-Assisted Emergency Networks [71.01350912967922]
In post-disaster scenarios, unmanned aerial vehicles (UAVs) are critical for establishing emergency communication networks.<n>This paper investigates age of information (AoI) minimization for UAV-assisted emergency communications with heterogeneous emergency services.<n>We develop MA-HEAD-Net, an adaptive rule-guided multi-agent deep reinforcement learning framework.
arXiv Detail & Related papers (2026-08-02T10:05:50Z) - Semi-Decentralized Multi-Spacecraft Collision Avoidance under Communication Constraints [22.71668728031368]
Current spacecraft collision-avoidance operations rely on intermittent ground-station contacts, requiring operators to plan with delayed and asynchronously updated information.<n>We formulate a semi-decentralized POMDP (SDec-POMDP) where we govern information propagation directly by realistic ground-station visibility windows.<n>Results show that semi-decentralized planning recovers near-centralized maneuver quality while requiring 28.5% fewer synchronization events than continuous coordination.
arXiv Detail & Related papers (2026-07-29T07:48:18Z) - Towards Reliable Aerial Ground Vehicle Collaboration: An Integrated Planning and Autonomy Framework for Field Deployment [0.0]
We present an integrated planning and autonomy framework for reliable field deployment.<n>We formulate the problem as an energy constrained cooperative routing task and solve it using a Deep Reinforcement Learning (DRL) based planner.<n>To bridge the gap between planning and execution, we introduce a standardized two layer YAML based mission API.<n>This framework is supported by a complete autonomy stack using PX4/MAVSDK for UAV control and ROS 2/Nav2 for UGV navigation.
arXiv Detail & Related papers (2026-07-07T15:59:49Z) - CoRE-VLA: Towards Scalable and Robust Vision-Language-Action Modeling via Conditional Routing of Experts [79.70951193831506]
Vision-language-action (VLA) models have advanced generalist robotic manipulation, yet real-world deployment reveals a fundamental challenge.<n>We propose CoRE-VLA, a scalable computation and robust VLA framework that formulates action generation as context-conditioned sparse computation.<n>Experiments on LIBERO, RoboCasa GR1 Tabletop, and real-world dual-arm manipulation show that CoRE-VLA achieves strong results on long-horizon and multi-task benchmarks.
arXiv Detail & Related papers (2026-07-04T03:58:09Z) - LAEI: Layered Autonomous Edge Intelligence Framework for Robust UAV Swarm Operations [3.8233569758620054]
Layered Edge Intelligence (LAEI) is a UAV-swarm framework that combines onboard learned policies with lightweight mission-level supervision.<n>LAEI reduces mission completion time and improves operational efficiency while maintaining collision-aware distributed UAV-level decision-making.
arXiv Detail & Related papers (2026-06-08T06:50:15Z) - Intent-First Aerial V2V for Tactical Coordination and Separation: Protocol and Performance Under Density and Disturbance [1.0152838128195467]
This paper presents the first controller-coupled characterization of an all-airborne, sidelink-class, intent-first vehicle-to-vehicle (V2V) tactical neighborhood exchange stack.<n>We implement and evaluate this model on an all-airborne V2V stack using sidelink-class C-V2X modules with authenticated freshness checks.
arXiv Detail & Related papers (2026-05-20T01:04:17Z) - PRISM: : Planning and Reasoning with Intent in Simulated Embodied Environments [59.07829883257003]
Built on five multi-room apartments, PRISM structures 300 human-verified tasks into three capability tiers.<n>PRISM exposes an agent-agnostic executable action API that allows arbitrary agents to be evaluated end-to-end.
arXiv Detail & Related papers (2026-05-12T04:59:47Z) - Attention in Motion: Secure Platooning via Transformer-based Misbehavior Detection [0.6999740786886536]
Vehicular platooning promises transformative improvements in transportation efficiency and safety through the coordination of multi-vehicle formations.<n>Traditional misbehaviour detection approaches, which rely on plausibility checks and statistical methods, suffer from high False Positive (FP) rates.<n>We present Attention In Motion (AIMformer), a transformer-based framework specifically tailored for real-time misbehaviour detection in vehicular platoons.
arXiv Detail & Related papers (2025-12-17T14:45:33Z) - Alignment Tipping Process: How Self-Evolution Pushes LLM Agents Off the Rails [103.05296856071931]
We identify the Alignment Tipping Process (ATP), a critical post-deployment risk unique to self-evolving Large Language Model (LLM) agents.<n>ATP arises when continual interaction drives agents to abandon alignment constraints established during training in favor of reinforced, self-interested strategies.<n>Our experiments show that alignment benefits erode rapidly under self-evolution, with initially aligned models converging toward unaligned states.
arXiv Detail & Related papers (2025-10-06T14:48:39Z) - ASTREA: Introducing Agentic Intelligence for Orbital Thermal Autonomy [51.56484100374058]
ASTREA is the first agentic system executed on flight-heritage hardware for autonomous spacecraft operations.<n>We integrate a resource-constrained Large Language Model (LLM) agent with a reinforcement learning controller in an asynchronous architecture tailored for space-qualified platforms.
arXiv Detail & Related papers (2025-09-16T08:52:13Z) - Global Distance-distributions Separation for Unsupervised Person
Re-identification [93.39253443415392]
Existing unsupervised ReID approaches often fail in correctly identifying the positive samples and negative samples through the distance-based matching/ranking.
We introduce a global distance-distributions separation constraint over the two distributions to encourage the clear separation of positive and negative samples from a global view.
We show that our method leads to significant improvement over the baselines and achieves the state-of-the-art performance.
arXiv Detail & Related papers (2020-06-01T07:05:39Z)
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.