arXiv:2607. 02037v1 Announce Type: cross Abstract: Autonomous surface vehicles vary widely in hydrodynamic and actuation characteristics, yet most controllers are designed for single-platform deployment.
By Ruiheng Jiang, Thomas Bi, Raffaello D'Andrea, Aswin Ramachandran
arXiv:2505. 08222v3 Announce Type: replace-cross Abstract: Autonomous vehicles (AVs) offer a cost-effective solution for scientific missions such as underwater tracking.
By Matteo Gallici, Ivan Masmitja, Mario Mart\'in
arXiv:2606. 08513v1 Announce Type: cross Abstract: Autonomous Underwater Vehicles (AUVs) traditionally rely on complex, heavily engineered pipelines for perception, path planning, and motion control.
By Elisei Shafer, Oren Gal
arXiv:2603. 29426v2 Announce Type: replace-cross Abstract: Multi-agent reinforcement learning (MARL) provides a promising solution for cooperative target tracking in networks of autonomous underwater vehicles (AUVs).
By Jiaao Ma, Chuan Lin, Guangjie Han, Shengchao Zhu, Zhenyu Wang, Chen An
arXiv:2608. 14135v1 Announce Type: cross Abstract: Autonomous pursuit-evasion is a fundamental challenge for Unmanned Aerial Vehicles (UAVs), requiring rapid decision-making under tightly coupled dynamics and continuously changing opponent behaviors.
By Wenhao Tang, Tianyang Chen, Zhejun Cui, Boyuan An, Jiayu Chen, Ruize Zhang, Huidong Liu, Tianyue Wu, Qingmin Liao, Fei Gao, Yu Wang, Chao Yu
arXiv:2604. 12645v2 Announce Type: replace-cross Abstract: Although autonomous underwater vehicles promise the capability of marine ecosystem monitoring, their deployment is fundamentally limited by the difficulty of controlling vehicles under highly uncertain and non-stationary underwater dynamics.
By Melvin Laux, Yi-Ling Liu, Rina Alo, S\"oren T\"opper, Mariela De Lucas Alvarez, Frank Kirchner, Rebecca Adam