多机器人通过局部通信协作完成目标围控与巡逻。
Multi-Robot Target Monitoring and Encirclement via Triggered Distributed Feedback Optimization
- 基于分布式反馈优化，仅用局部信息和邻居数据实现协同
- 在虚拟与真实环境中均验证了策略的有效性与可扩展性
- 适合异构地面与空中机器人团队应用
我们设计了一种嵌入模块化ROS 2架构的分布式反馈优化策略，使异构机器人团队能够协同监控并围控目标，同时巡检兴趣点。基于聚合反馈优化框架，该方法兼顾单个机器人的微观状态（如位置）与团队宏观分布，最小化全局性能指标。所提分布式策略仅依赖本地测量与邻近数据交换，通信采用由本地可验证触发条件控制的异步协议。理论证明该策略能使机器人收敛至优化问题的驻定配置。通过大量真实的Webots ROS 2虚拟实验的蒙特卡洛测试，验证了策略的有效性与可扩展性。最终在地面与无人机的真实实验中展示了方案的实际适用性。
原文摘要 · Abstract (English)
We design a distributed feedback optimization strategy, embedded into a modular ROS 2 control architecture, which allows a team of heterogeneous robots to cooperatively monitor and encircle a target while patrolling points of interest. Relying on the aggregative feedback optimization framework, we handle multi-robot dynamics while minimizing a global performance index depending on both microscopic (e.g., the location of single robots) and macroscopic variables (e.g., the spatial distribution of the team). The proposed distributed policy allows the robots to cooperatively address the global problem by employing only local measurements and neighboring data exchanges. These exchanges are performed through an asynchronous communication protocol ruled by locally-verifiable triggering conditions. We formally prove that our strategy steers the robots to a set of configurations representing stationary points of the considered optimization problem. The effectiveness and scalability of the overall strategy are tested via Monte Carlo campaigns of realistic Webots ROS 2 virtual experiments. Finally, the applicability of our solution is shown with real experiments on ground and aerial robots.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。