uav

The paper proposes a Decision Transformer-based approach for optimizing UAV-mounted RIS-assisted dynamic D2D communications, demonstrating cross-scenario generalization and efficient zero-shot transfer.

A benchmark comparison shows CABiNet, a 2021 efficient architecture, achieves better accuracy-to-latency trade-offs than YOLO26-sem on the UAVid dataset for real-time semantic segmentation.

Squire is a machine demonstrated in a military exercise, integrating UAV and USV capabilities for electronic warfare with communications and sensing equipment.

This article presents a GitHub repository for reproducing a paper on acoustic UAV detection in battlefield scenarios, addressing challenges like noise, domain shift, and weak labels. The repository provides code for the method and evaluation protocol, with synthetic data for testing.

SonicFly, a research project from Duke University, enables a UAV to intercept another drone by passively listening to its flight sound using aeroacoustic perception.

This paper proposes an exemplar-based machine learning approach to objectively classify gust-induced pressure-load measurements across multiple flight attitudes, identifying nine fundamental response types from 3480 measurements on a flying-wing model.

This empirical study compares conversational XAI (powered by LLMs) against a traditional dashboard for UAV intrusion detection auditing, finding the conversational interface improves perceived usefulness but risks operator over-reliance on AI advice.

This paper proposes JUROR, a reinforcement learning-based framework that jointly optimizes UAV flight paths and decentralized opportunistic routing in delay-tolerant networks under centralized training and decentralized execution.

This paper proposes a cost-aware Bayesian optimization framework with level-set estimation to guide UAVs for rapid post-disaster damage assessment, reducing uncertainty while minimizing operational costs.

This paper introduces SULAND v2, a refined RGB surface landmine detection dataset and benchmark for UAV/UGV-based surveys, addressing annotation errors and domain-shift evaluation in object detection.

This paper presents a human-in-the-loop bootstrapping method for detecting PFM-1 mines in UAV hyperspectral imagery, showing that ACE with bootstrapping can find all targets in 2 rounds of inspection, while aggregate ROC-AUC scores hide large operational differences between detectors.

This systematic literature review investigates autonomous UAV route planning for coverage maximization in environmental monitoring, analyzing 562 records and reporting preliminary findings on algorithmic families, metrics, and validation practices.

This paper proposes a unified imitation learning framework using Taylor Series Imitation Learning and distributionally robust adaptive control to address both policy-induced and uncertainty-induced distribution shifts, with a UAV case study demonstrating safety under uncertainty.

This paper proposes an edge-aware online tracking pipeline for thermal infrared UAV swarm tracking, featuring the Adaptive Kinematic Kalman Filter (AKKF) that balances efficiency and robustness under challenging conditions.

This paper introduces SIS-Bench, a benchmark for evaluating self-awareness and spatial cognition in UAV embodied intelligence using multimodal large language models, and explores motion-aware representations to improve performance.

This paper proposes a CKM-driven framework for multi-UAV trajectory planning in urban inspection, using diffusion models to reconstruct high-fidelity channel quality maps and a graph attention network with soft actor-critic algorithm for communication-aware path planning.

MIT researchers have developed a new system-on-a-chip that enables tiny robots to create detailed 3D maps of their environments in real-time using only about 6 milliwatts of power, potentially enabling long-duration autonomous navigation in complex spaces.

Proposes AE-YOLO, an attention-guided autoencoder-enhanced YOLO framework for robust insulator defect detection in UAV transmission-line imagery, achieving 95.10% [email protected] and outperforming YOLO baselines by 5 points.

The paper presents an agentic AI framework that leverages large language models and chain-of-thought reasoning to optimize UAV-assisted logistics scheduling with mobile edge computing, aiming to improve efficiency and resource allocation in manufacturing logistics.