云机器人就是云计算与机器人学的结合。就像其它网络终端一样，机器人本身不需要存储所有资料信息，或具备超强的计算能力。只需要对于云端提出需求，云端进行相应响应并满足。云机器人就是云计算与机器人学的结合。就像其它网络终端一样，机器人本身不需要存储所有资料信息，或具备超强的计算能力。只需要对于云端提出需求，云端进行相应响应并满足。
自动驾驶汽车，又称无人驾驶汽车、电脑驾驶汽车或轮式移动机器人，为一种运输动力的无人地面载具。作为自动化载具，自动驾驶汽车不需要人类操作即能感测其环境及导航。完全的自动驾驶汽车仍未全面商用化，大多数均为原型机及展示系统，部分可靠技术才下放至量产车型，逐渐成为现实。
一个多智能体系统（multi-agent system，縮寫M.A.S.），是由一个在一个环境中交互的多个智能体组成的计算系统。 多智能体系统也能被用在解决分离的智能体以及单层系统难以解决的问题。一个多智能体系统（multi-agent system，縮寫M.A.S.），是由一个在一个环境中交互的多个智能体组成的计算系统。 多智能体系统也能被用在解决分离的智能体以及单层系统难以解决的问题。
传统机器人在执行任务如即时定位与地图构建、物品抓取等任务时，大量环境感知数据的存储与计算对机器人硬件配置的要求越来越高， 实时执行尤其困难。云机器人不仅能够卸载复杂的计算任务到云端，而且可以共享云感知信息和技能。然而，由于缺乏建立智能的信息检索和资源配置决策的基础支撑平台，目前云机器人的研发还处于萌芽阶段。针对上述问题，本项目围绕动态异构的云机器人实时感知开展资源分配算法的研究工作, 主要目标包括：1） 针对云机器人的动态、异构的特性，基于图论和隐马尔可夫过程，提出实时感知场景下的动态资源供需模型；2） 研究面向云机器人实时感知系统的动态资源供需架构，基于竞价博弈的最优化方法，提出能够适应具有动态异构特性的资源配置算法.
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云机器将大量异构的感知信息系统和执行系统连接在一起，以资源服务商和客户的模式，通过互联网为用户提供各种机器人服务以及共享感知信息存储和处理的计算资源。本项目以相互协调的多机器人混合架构系统为对象，通过泛在感知网络，高效获取有用信息，构建适用于基于环境感知、情感交互、协作控制等信息的知识，存储海量的目标识别、导航、任务、智能服务等机器人所需信息。使用强化学习方法提高云机器人的服务任务认知能力；使用迁移学习方法将原有信息应用到新场景。主要研究以下几个方面的内容：1）服务机器人云平台泛在感知及其信息高获取方法研究；2）云机器人类脑层次化记忆的信息知识表征的体系结构设计；3）适用于基于情感交互的语义感知与计算的多源大数据的构建问题；4）云机器人的服务任务认知知识迁移方法研究； 5）混合架构云平台搭建及其关键技术研究。
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本项目通过基于MEMS激光雷达技术，建立统一离散且稠密表达的传感器数据模型解决多传感器信息融合问题，为复杂场景识别提供标准化数据；基于深度强化学习框架解决不可控复杂交通环境的场景理解问题，为无人车的定位和导航提供先决条件；基于自适应定位算法和类脑智能的实时导航技术，解决室内（室内地下停车场）外混合环境定位与导航问题。并基于这三大核心技术点建立无人驾驶系统创新平台。
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Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.
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2016级在读博士研究生，研究方向为多智能体强化学习、智能仓储物流系统等
科研助理，中国科学院大学硕士学位。研究方向为云机器人技术、联邦学习等
2018级在读硕士研究生，研究方向为SLAM、无人驾驶系统等
2019级客座学生，主要研究方向为SLAM算法、无人驾驶系统、Carla仿真等
2018级在读硕士研究生，主要研究方向为联邦学习，SLAM和基于语义信息优化的SLAM系统等
2019级在读硕士研究生，主要研究方向为强化学习、多智能体系统、交通仿真系统等
2020级在读硕士研究生，主要研究方向为云机器人、机器学习等
2020级在读硕士研究生，主要研究方向为云机器人、机器学习等
刘博艺同学的关于云机器人技术的论文《Federated Imitation Learning: A Novel Framework for Cloud Robotic Systems with Heterogeneous Sensor Data》被ICRA-2020大会接收，同时也被期刊IEEE RAL录用。
李朝冉和韩东同学的关于SLAM的论文被机器人领域优秀会议IEEE International Conference on Robotics and Biomimetics（Robio-2019）接收，祝贺！
刘延东博士的论文《A Novel Labour Division Strategy based on fixed response Threshold Model for Warehouse Systems, International Journal of Robotics and Automation》被期刊International Journal of Robotics and Automation接收，祝贺！
Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems
Journal: IEEE Robotics and Automation Letters (JCR Q1)
Author: Boyi Liu, Lujia Wang, Ming Liu, Cheng-Zhong Xu
Federated Imitation Learning: A Novel Framework for Cloud Robotic Systems with Heterogeneous Sensor Data
Journal: IEEE Robotics and Automation Letters (JCR Q1)
Author: Boyi Liu, Lujia Wang, Ming Liu, Cheng-Zhong Xu
FC-SLAM: Federated Learning Enhanced Distributed Visual-LiDAR SLAM In Cloud Robotic System
Conference: IEEE International Conference on Robotics and Biomimetics
Author: Zhaoran Li, Lujia Wang, Cheng-Zhong Xu
A Robust Stereo Camera Localization Method with Prior LiDAR Map Constrains
Conference: IEEE International Conference on Robotics and Biomimetics
Author: Dong Han, Zuhao Zhou, Cheng-Zhong Xu
A Novel Labour Division Strategy based on fixed response Threshold Model for Warehouse Systems, International Journal of Robotics and Automation
Journal: International Journal of Robotics and Automation
Author: Yandong Liu, Lujia Wang, Ming Liu, Cheng-Zhong Xu
