arXiv:2609.22353v1 Announce Type: new
Abstract: Mobile river monitoring robots must interpret obstacles and water boundaries that geographic waypoints alone cannot describe. On resource constrained p...
By Savio Cardoz, Santhiya Rajan
Remote-sensing and UAV applications need models that generalize across platforms and viewpoints without task-specific training. Yet training-free pipelines often falter on oriented geometry, scale/rotation variation, and crowded ports or airfields, and rarely unify detection and segmentation.
WareFly-VLA is a photorealistic vision‑language‑action framework and dataset for UAV navigation and human tracking in smart warehouses. It comprises 507 human‑teleoperated flight episodes with 8,504 high‑resolution RGB transitions, each paired with a natural‑language description of a target worker and a synchronized four‑degree‑of‑freedom control command. The benchmark evaluates four open‑source VLA architectures on tasks such as target approach and person following, revealing significant challenges for language‑conditioned aerial control under realistic warehouse conditions.
By Thinh D. Le, Son T. Nguyen, Duong Q. Nguyen, Dung D. Le, Ngo Anh Vien, H. Nguyen-Xuan
arXiv:2608. 11738v1 Announce Type: cross Abstract: Multimodal Large Language Model (MLLM)-based UAV aerial image understanding and reasoning is essential for aerial intelligence yet poses distinct challenges arising from extreme scale variation, arbitrary camera orientations, and high object density.
By Haoyu Zhang, Shuoxun Zhang, Peng Ye, Lin Zhang, Jiakang Yuan, Shenghong Yi, Yuening Wang, Tao Chen
The paper evaluates out‑of‑the‑box object detection models for automatic target detection and recognition (ATD/R) in military settings. Six YOLO variants and two DETR variants were benchmarked on a new military dataset featuring vehicles, occlusions, and small targets, with performance measured in mAP@0.5 and mAP@0.5:0.95 across air‑to‑ground and ground‑to‑ground perspectives. Findings show larger models and DETR-based approaches perform best, fine‑tuning on the VisDrone dataset improves air‑to‑ground and small‑object performance, yet all models still struggle with small targets in air‑to‑ground scenarios.
By Alma M. Liezenga, Lotte Nijskens, Henrik R. Baumann, Stefan Becker, Simon Bensberg, Niccol\`o Camarlinghi, H{\aa}vard R. Eiring, Alexander W. Johnsgaard, Tanel Liiv, Giuseppe Martino, Matteo Marturini, Matthias Rapp, Jan Erik van Woerden, Alexander Wolpert, Hugo J. Kuijf
This study explores temporal neural networks for estimating the end‑effector position of an aerial continuum manipulator (ACM) affected by aerodynamic disturbances from a UAV. An experimental dataset covering stationary and free‑hovering conditions across various robot configurations and altitudes was used to evaluate strain‑parameterized kinematic models and to benchmark a closed‑form continuous‑time (CfC) neural network against an MLP and a GRU. The CfC network achieved a 22 mm RMSE, outperforming the MLP (36 mm) and GRU (28 mm) by 39.5 % and 20.6 %, respectively, demonstrating the advantage of continuous‑time learning for this task.
By Niloufar Amiri, Houman Masnavi, Farrokh Janabi-Sharifi