This paper provides a new solution approach to a multi-player perimeter defense game, in which the intruders' team tries to enter the territory, and a team of defenders protects the territory by capturing intruders on the perimeter of the territory. The objective of the defenders is to detect and capture the intruders before the intruders enter the territory. Each defender independently senses the intruder and computes his trajectory to capture the assigned intruders in a cooperative fashion. The intruder is estimated to reach a specific location on the perimeter at a specific time. Each intruder is viewed as a spatio-temporal task, and the defenders are assigned to execute these spatio-temporal tasks. At any given time, the perimeter defense problem is converted into a Decentralized Multi-UAV Spatio-Temporal Multi-Task Allocation (DMUST-MTA) problem. The cost of executing a task for a trajectory is defined by a composite cost function of both the spatial and temporal components. In this paper, a decentralized consensus-based bundle algorithm has been modified to solve the spatio-temporal multi-task allocation problem, and the performance evaluation of the proposed approach is carried out based on Monte-Carlo simulations. The simulation results show the effectiveness of the proposed approach to solve the perimeter defense game under different scenarios. Performance comparison with a state-of-the-art centralized approach with full observability, clearly indicates that DMUST-MTA achieves similar performance in a decentralized way with partial observability conditions with a lesser computational time and easy scaling up.
LLM-Enabled Low-Altitude UAV Natural Language Navigation via Signal Temporal Logic Specification Translation and Repair
Guangdong Provincial Key Laboratory of Space-Aerial Networking and Intelligent Sensing, Harbin Institute of Technology, Shenzhen（广东省空天网络与智能感知重点实验室，哈尔滨工业大学（深圳））；Peng Cheng Laboratory (PCL), Shenzhen（鹏城实验室）；Information Systems Technology and Design, Singapore University of Technology and Design（新加坡科技设计大学信息系统技术与设计系）；School of Computer Science and Engineering, Central South University, Changsha（中南大学计算机科学与工程学院）
College of Automation, Harbin Engineering University（哈尔滨工程大学自动化学院）；Department of Mechanical Engineering, University of Hong Kong（香港大学机械工程系）；College of Mechatronics and Control Engineering, Shenzhen University（深圳大学机电与控制工程学院）
C$^2$-Explorer: Contiguity-Driven Task Allocation with Connectivity-Aware Task Representation for Decentralized Multi-UAV Exploration
School of Intelligence Science and Engineering, Harbin Institute of Technology, Shenzhen, Guangdong, China（哈尔滨工业大学深圳研究院）；Robotics and Autonomous Systems Thrust, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）机器人与自主系统方向）；Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China（南方科技大学机械与能源工程系）
Spatio-Temporal Attention Enhanced Multi-Agent DRL for UAV-Assisted Wireless Networks with Limited Communications
School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University（中山大学智能系统工程学院深圳校区）；Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)（广东海洋工程科学实验室（珠海））；Department of Equipment Management and Unmanned Aerial Vehicle Engineering, Air Force Engineering University（空军工程大学装备管理与无人机工程系）；School of Computing and Artificial Intelligence, Jiangxi University of Finance and Economics（江西财经大学计算机与人工智能学院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算机与数据科学学院）
National Chung Cheng University（中正大学）；Advanced Institute of Manufacturing with High-tech Innovations (AIM-HI), National Chung Cheng University（中正大学先进制造与高科技创新研究院）
AeroResQ: Edge-Accelerated UAV Framework for Scalable, Resilient and Collaborative Escape Route Planning in Wildfire Scenarios
Indian Institute of Science（印度科学研究院）；Information Sciences Institute, University of Southern California（信息科学研究所，南加州大学）；Renaissance Computing Institute, University of North Carolina（复兴计算研究所，北卡罗来纳大学）；University of Massachusetts（马萨诸塞大学）
Ultra-Fusion: A Resilient Tightly-Coupled Multi-Sensor Fusion SLAM Framework under Sensor Degradation and Spatiotemporal Perturbation for Intelligent Transportation Systems
Beijing Institute of Technology（北京理工大学）；Chongqing University（重庆大学）；Sichuan University（四川大学）；Northwestern Polytechnical University（西北工业大学）；Shanghai Jiao Tong University（上海交通大学）
