This paper addresses the problem of restricted airspace protection from invaders using the cooperative multi-UAV system. The objective is to detect and capture the invaders cooperatively by a team of homogeneous UAVs (called evaders)before invaders enter the restricted airspace. The problem of restricted airspace protection problem is formulated as a Multi-UAV Spatio-Temporal Multi-Task Allocation problem and is referred as MUST-MTA. The MUST-MTA problem is solved using a modified consensus-based bundled auction method. Here, the spatial and time constraints are handled by combining both spatial and temporal loss component. The solution identifies the sequence of spatial locations to be reached by the evader at specific time instants to neutralize the invaders. The performance of MUST-MTA with the consensus approach is evaluated in a simulated environment. The Monte-Carlo simulation results clearly indicate the efficacy of the proposed approach in restricted airspace protection against intruders
School of Information Science and Technology, Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳）信息科学与技术学院）；Guangdong Provincial Key Laboratory of Space-Aerial Networking and Intelligent Sensing（广东省空间-航空网络与智能感知重点实验室）；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）；School of System Design and Intelligent Manufacturing, Southern University of Science and Technology（南方科技大学系统设计与智能制造学院）
Indian Institute of Science（印度科学学院）；Nanyang Technological University（南洋理工大学）
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 intru…
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）
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）
This paper presents a novel information-based mission planner for a drone tasked to monitor a spatially distributed dynamical phenomenon. For the sake of simplicity, the area to be monitored is discretized. The insight behind the proposed approach is that, thanks to the spatio-temporal dependencies of the observed phenomenon, one does not need to collect data on the entire area. In fact, unmeasured states can be estimated using an estimator, such as a Kalman filter. In this context the planning problem becomes the one of generating a flight path that maximizes the quality of the state estimati…
