This paper studies a defense approach against a swarm of adversarial agents. We employ a closed formation (`StringNet') of defending agents around the adversarial agents to restrict their motion and guide them to a safe area while navigating in an obstacle-populated environment. Control laws for forming the StringNet and guiding it to a safe area are developed, and the stability of the closed-loop system is analyzed formally. The adversarial swarm is assumed to move as a flock in the presence of rectangular obstacles. Simulation results are provided to demonstrate the efficacy of the approach.
Multi-sensory Anti-collision Design for Autonomous Nano-swarm Exploration
University of Bologna（博洛尼亚大学）；KU Leuven（鲁汶大学）；ETH Zürich（苏黎世联邦理工学院）；Dalle Molle Institute for Artificial Intelligence (IDSIA), USI-SUPSI（USI-SUPSI 达尔·莫勒人工智能研究所（IDSIA））
MC-Swarm: Minimal-Communication Multi-Agent Trajectory Planning and Deadlock Resolution for Quadrotor Swarm
AI Institute of Seoul National University（首尔国立大学人工智能研究所）；Carnegie Mellon University（卡内基梅隆大学）；Department of Mechanical System Design Engineering, Seoul National University of Science and Technology (SEOULTECH)（首尔科学技术大学机械系统设计工程系）
AttentionSwarm: Reinforcement Learning with Attention Control Barier Function for Crazyflie Drones in Dynamic Environments
Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Intelligent Space Robotics Laboratory（智能空间机器人实验室）；Skolkovo Institute of Science and Technology（斯克尔科维科学与技术学院）；AI Center（人工智能中心）；Moscow Institute of Physics and Technology（莫斯科物理技术学院）
