arXiv:2607. 19213v1 Announce Type: cross Abstract: The report envisions a decade in which drones move goods, medical supplies, and information at a scale comparable to national infrastructure investments like highways and the electric grid.
By Kevin Butler, Christopher Stewart, Nils Aschenbruck, Alina Gerall, Weisong Shi, Deborah Silver, Ufuk Topcu
arXiv:2608.21444v1 Announce Type: new
Abstract: Multi-drone systems are increasingly positioned for safety-critical missions such as search and rescue (SAR) and critical infrastructure monitoring. Ye...
By Timothy Merritt, Alejandro Jarabo-Pe\~nas, Juan Bravo-Arrabal, Maria-Theresa Bahodi, Anders Lyhne Christensen
arXiv:2606. 11687v1 Announce Type: cross Abstract: Unmanned Aerial Vehicle (UAV) threats have emerged as a defining security challenge of the 21st century.
By Marius Bayizere
arXiv:2608. 05792v1 Announce Type: new Abstract: Agentic artificial intelligence (AI) is transforming Integrated Sensing and Communication (ISAC) from a function-oriented physical-layer technology into a goal-driven, closed-loop intelligent system, a paradigm we term AISAC.
By Kai Li, Conggai Li, Sarah Ali Siddiqui, Syed Sohail Ahmed, Xin Yuan, Shenghong Li, Wei Ni
The paper proposes Neuro‑Symbolic Agentic AI (NSAAI) as a framework that blends neural grounding, symbolic reasoning, and closed‑loop interaction to enhance decision‑making for networked low‑altitude UAVs. It outlines NSAAI’s strengths in data efficiency, compositional generalization, continual learning, and zero‑shot transfer, and presents a reference architecture covering task management, planning, verification, skill execution, and network interaction. An urban fire‑inspection simulation demonstrates how a UAV can coordinate sensing, cloud access, and verified image‑delivery skills under intermittent connectivity, illustrating NSAAI’s potential for reusable skills, evidence‑grounded decisions, and adaptive mission execution.
By Yuqi Ping, Tianhao Liang, Nanchi Su, Guangyu Lei, Junwei Wu, Qinyu Zhang, Tingting Zhang
The article explores neuro-symbolic agentic AI (NSAAI) as a framework for enhancing the reliability and adaptability of networked low‑altitude UAVs. It outlines NSAAI’s strengths in data efficiency, compositional generalization, continual learning, and zero‑shot transfer, and presents a reference architecture that integrates task management, neuro‑symbolic planning, verification, metacognition, skill execution, and network interaction. A case study of an urban fire‑inspection mission demonstrates how a UAV can coordinate sensing, cloud access, and verified image‑delivery skills under intermittent connectivity, illustrating NSAAI’s potential for reusable skills, evidence‑grounded decision‑making, and adaptive mission execution.