探索复杂系统与智能控制的前沿Exploring the frontiers of complex systems and intelligent control
面向无人机集群的自主协同控制、任务规划与编队飞行等关键问题, 研究通信受限与对抗环境下的集群智能涌现机理、分布式决策与安全控制方法, 推动无人系统在侦察、运输与应急救援等场景中的工程应用。
Focusing on key problems such as autonomous cooperative control, mission planning and formation flight of UAV swarms, we investigate the mechanisms of swarm intelligence emergence, distributed decision-making and safe control under communication constraints and adversarial environments, advancing engineering applications of unmanned systems in reconnaissance, transportation and emergency response.
查看详情 →View Details →
针对复杂环境下无人机集群的自主编队与避障控制,提出基于 MAPPO 的“集中训练—分布执行”框架, 融合无人机状态与激光雷达信息实现集群统一调度,三维环境避障平均成功率达 94%,显著优于传统 PPO 方法。
For autonomous formation and obstacle avoidance of UAV swarms in complex environments, we propose a MAPPO-based centralized-training decentralized-execution framework that fuses UAV states with LiDAR data for unified swarm scheduling, achieving an average obstacle-avoidance success rate of 94% in 3D environments, markedly outperforming conventional PPO.
面向导航卫星信号缺失场景,构建多向视觉融合感知建图与基于目标可视的运动规划方法, 无 GPS 条件下 8 机集群搜索成功率约 75%,动态目标追踪可视时间占比达 96%,定位精度 1.49m。
For GNSS-denied scenarios, we develop multi-directional vision-fused perception mapping and target-visibility-based motion planning; with 8 GPS-free UAVs, the swarm search success rate reaches about 75%, target visibility time ratio reaches 96%, and localization accuracy reaches 1.49 m.
研发 SKP 系列光纤制导无人机(战术四旋翼、多功能四旋翼与固定翼),摆脱无线电依赖、不受电磁干扰影响, 已在反恐特警等实战场景应用;水空两栖无人机实现水面出入与水下远距离通信。
We develop the SKP series of fiber-optic guided UAVs (tactical quadcopter, multi-function quadcopter and fixed-wing), free from radio dependence and immune to electromagnetic interference, already deployed in counter-terrorism and other field scenarios; amphibious UAVs enable water entry/exit and long-range underwater communication.
自主构建涵盖地面指控、任务调度、通信中继与空中自治执行的空地一体化集群控制系统, 支持异构平台接入与多算法并行部署;三机协同探索任务成功率超 90%,6-7 级强风下仍能稳定协同。
We build an air-ground integrated swarm control system covering ground command, task scheduling, communication relay and airborne autonomous execution, supporting heterogeneous platforms and parallel algorithm deployment; three-UAV cooperative exploration achieves over 90% success and remains stable in force 6-7 winds.
围绕大规模作业调度与智能仓储系统的优化运行, 研究多资源约束下的组合优化建模、智能调度算法与仓储机器人协同控制方法, 探索数据驱动的实时调度与库存协同决策,提升物流系统的自动化与智能化水平。
Centered on the optimal operation of large-scale scheduling and intelligent warehousing systems, we study combinatorial optimization modeling under multi-resource constraints, intelligent scheduling algorithms, and cooperative control of warehouse robots, exploring data-driven real-time scheduling and integrated inventory decisions to enhance the automation and intelligence of logistics systems.
查看详情 →View Details →
针对多任务、多场景及作业插入、修改、故障、取消等动态扰动,构建数据驱动与深度强化学习相结合的混合分层调度策略搜索模型, 精准描述异构、变尺寸机器人运动模型,突破系统死锁与不可控涌现难题,实现秒级千台调度能力。
For dynamic disturbances such as task insertion, modification, failure and cancellation, we build a hybrid hierarchical scheduling policy search model combining data-driven methods with deep reinforcement learning, accurately describing heterogeneous and variable-size robot motion models, overcoming deadlock and uncontrollable emergent behaviors, and achieving thousand-robot scheduling within seconds.
围绕“订单分配—料箱命中—机器人调度”的业务分解与联合决策建模,构建紧耦合与松耦合的柔性联合优化模型体系, 支撑机器人仓储系统大规模连续订单的实时与柔性履约。
Centered on the "order allocation - bin hitting - robot scheduling" decomposition and joint decision modeling, we build tightly- and loosely-coupled flexible joint optimization frameworks to support real-time and flexible fulfillment of massive continuous orders in robotic warehousing systems.
面向移动边缘计算网络、多数据中心与多微电网,提出能量管理、任务迁移与带宽分配等联合优化算法, 兼顾电能成本、碳排放与服务质量约束,有效降低系统运行成本。
For mobile edge computing networks, multiple data centers and microgrids, we propose joint optimization algorithms for energy management, task migration and bandwidth allocation, balancing electricity cost, carbon emission and QoS constraints to effectively reduce operational costs.
以复杂网络和多智能体系统为对象,研究网络结构、动力学与控制之间的内在关联, 发展分布式协同控制、网络牵制控制与一致性理论, 解决大规模互联系统在不确定环境下的鲁棒协同与性能优化问题。
Taking complex networks and multi-agent systems as research objects, we investigate the intrinsic relations among network structure, dynamics and control, developing distributed cooperative control, network pinning control and consensus theory to address robust coordination and performance optimization of large-scale interconnected systems under uncertainties.
查看详情 →View Details →
面向大规模复杂网络,研究小世界、无标度等典型拓扑结构特征及其对网络同步、鲁棒性与级联失效行为的影响,揭示网络“结构—动力学—控制”之间的内在关联。
Focusing on large-scale complex networks, we study typical topological features such as small-world and scale-free properties and their effects on synchronization, robustness and cascading failures, revealing the intrinsic relations among network structure, dynamics and control.
研究多智能体系统在时变通信拓扑、传输时延与噪声干扰下的分布式一致性、编队控制与包含控制问题,设计仅依赖局部信息的协同控制协议。
We investigate distributed consensus, formation control and containment control of multi-agent systems under time-varying topologies, communication delays and noises, designing cooperative protocols based only on local information.
针对大规模动态网络的受控同步问题,研究牵制控制节点的选择策略、自适应牵制控制与簇同步控制方法,以少量控制输入实现全网协同。
For controlled synchronization of large-scale dynamical networks, we study pinning node selection strategies, adaptive pinning control and cluster synchronization methods, achieving network-wide coordination with limited control inputs.
研究社交网络中的观点形成、社会学习与演化博弈行为,建立异构置信与影响力条件下的观点动力学模型,揭示群体决策的涌现规律。
We study opinion formation, social learning and evolutionary game behaviors in social networks, establishing opinion dynamics models under heterogeneous confidence and influence to reveal emergent patterns of collective decision-making.
Housheng Su, Xiaofan Wang, Pinning Control of Complex Networked Systems, Springer & SJTU Press, 2013.
Housheng Su, Xiaofan Wang, Pinning Control of Complex Networked Systems, Springer & SJTU Press, 2013.
汪小帆、李翔、陈关荣,《网络科学导论》,高等教育出版社,2012。
Xiaofan Wang, Xiang Li, Guanrong Chen, Network Science: An Introduction, Higher Education Press, 2012.
Guanrong Chen, Xiaofan Wang, Xiang Li, Introduction to Complex Networks: Models, Structure and Dynamics, Higher Education Press, 2012.
Guanrong Chen, Xiaofan Wang, Xiang Li, Introduction to Complex Networks: Models, Structure and Dynamics, Higher Education Press, 2012.
汪小帆、李翔、陈关荣,《复杂网络理论及其应用》,清华大学出版社,2006。
Xiaofan Wang, Xiang Li, Guanrong Chen, Complex Network Theory: Theory & Application, Qinghua University Press, 2006.
陈关荣、汪小帆,《动力系统的混沌化》,上海交通大学出版社,2006。
Guanrong Chen & Xiaofan Wang, Chaotification of Dynamical Systems, SJTU Press, 2006.