现有的机器人本身计算能力有限，仅靠自身的传感器接收的信息也有一定的局限性，还不足以胜任面对复杂场景的应对能力，也不能够满足人们对服务机器人的期待。互联网中包含着丰富的资源，互联网的发展也为机器人的发展和进步提供了资源支持。服务机器人的智能化发展，离不开对互联网资源的充分利用。在服务机器人发展的同时，智能的传感器硬件设备也在不断的进步。与服务机器人相比，智能的传感器硬件设备分布更加广泛。而服务机器人的传感器设备集中在机器人身上，分布密集，信息采集受到机器人物理位置的制约。

本文设计和实现的云机器人计算框架（cloud robot computing framework， CRCF），通过在云端平台中部署分布式系统、数据库以及图像算法库等应用，拓展了服务机器人的存储和运算能力；通过在云端平台与传感器硬件设备相连接，为服务机器人提供更多更广的数据信息；通过将云端平台与互联网结合，为机器人提供了诸如语音识别、图像处理等功能；机器人部分通过应用机器人操作系统ROS，使得机器人的管理更加灵活和方便。CRCF框架旨在利用云端的大数据处理能力提升机器人的计算和存储能力，并结合第三方互联网云端应用服务和智能硬件设备来拓展机器人的信息来源和服务功能。

最后，本文通过远程语音控制机器人的实验，验证了CRCF框架的云端平台在结合硬件设备以及第三方云端应用的功能和性能，验证了CRCF框架将服务机器人与云端平台结合在一起的有效性。同时，该实验了也验证了通过CRCF框架的云端平台来编写服务机器人应用的可能性和便捷性。

Robots themselves have limited computing capability, especially in processing the complex information received from different sensors. Thus make it not enough for a single robot to be competent to deal with the complex scenes. Internet contains a wealth of resources, the development of Internet also provides resources for the development and progress of the robot. The intelligent development of service robots, cannot be separated from the full use of Internet resources. In parallel with the development of service robots, intelligent sensor equipment is also in constant progress. Compared with the service robots, intelligent sensor devices are more widely distributed. The sensor equipment of the service robot is concentrated on the robot, so the sensor data collection is restricted by the physical position of the robot.

This paper proposes a cloud robot computing framework (CRCF). Through the deployment of distributed system and databases in the cloud platform, service robots have expanded its storage and computing power; through connected to the sensor devices in the cloud platform, service robots can collect more sensor data; through connected to the Internet, the service robots can have the function such as speech recognition and image processing. Through the deployment of ROS on robots, the management of robots become more flexible and convenient. CRCF system platform designed to take advantage of cloud’s computing large data capability to extend the computing power and storage capacity of the robot. At the same time, CRCF system combined with third-party services and hardware to extend the sources of information and service functions of robots.

Finally, through a remote control robot experiment, we verified the CRCF system platform’s capabilities and performance in combination with intelligent hardware and third-party cloud applications. The experiment also verified it is possible and convenient to write applications for service robots from the cloud platform of CRCF.