A Task-Oriented Hybrid Cloud Architecture with Deep Cognition Mechanism for Intelligent Space

Intelligent Space(IS)is widely regarded as a promising paradigm for improving quality of life through using service task processing.As the field matures,various state-of-the-art IS architectures have been proposed.Most of the IS architectures designed for service robots face the problems of fixedfunction modules and low scalability when performing service tasks.To this end,we propose a hybrid cloud service robot architecture based on a Service-Oriented Architecture(SOA).Specifically,we first use the distributed deployment of functional modules to solve the problem of high computing resource occupancy.Then,the Socket communication interface layer is designed to improve the calling efficiency of the function module.Next,the private cloud service knowledge base and the dataset for the home environment are used to improve the robustness and success rate of the robot when performing tasks.Finally,we design and deploy an interactive system based on Browser/Server(B/S)architecture,which aims to display the status of the robot in real-time as well as to expand and call the robot service.This system is integrated into the private cloud framework,which provides a feasible solution for improving the quality of life.Besides,it also fully reveals how to actively discover and provide the robot service mechanism of service tasks in the right way.The results of extensive experiments show that our cloud system provides sufficient prior knowledge that can assist the robot in completing service tasks.It is an efficient way to transmit data and reduce the computational burden on the robot.By using our cloud detection module,the robot system can save approximately 25% of the averageCPUusage and reduce the average detection time by 0.1 s compared to the locally deployed system,demonstrating the reliability and practicality of our proposed architecture.

Adaptive Proportional-Derivative Sliding Mode Control Law With Improved Transient Performance for Underactuated Overhead Crane Systems

In this paper, an adaptive proportional-derivative sliding mode control(APD-SMC) law, is proposed for 2D underactuated overhead crane systems. The proposed controller has the advantages of simple structure, easy to implement of PD control, strong robustness of SMC with respect to external disturbances and uncertain system parameters, and adaptation for unknown system dynamics associated with the feedforward parts. In the proposed APD-SMC law, the PD control part is used to stabilize the controlled system, the SMC part is used to compensate the external disturbances and system uncertainties,and the adaptive control part is utilized to estimate the unknown system parameters. The coupling behavior between the trolley movement and the payload swing is enhanced and, therefore, the transient performance of the proposed controller is improved.The Lyapunov techniques and the La Salle's invariance theorem are employed in to support the theoretical derivations. Experimental results are provided to validate the superior performance of the proposed control law.

Building an Intelligent Home Space for Service Robot Based on Multi-Pattern Information Model and Wireless Sensor Networks

This paper is concerned with constructing a prototype intelligent home environment for home service robot. In this environment, multi-pattern information can be represented by some intelligent artificial marks. Light-packs service robots can provide reliable and intelligent service by interacting with the environment through the wireless sensor networks. The intelligent space consists the following main components: smart devices with intelligent artificial mark;home server that connects the smart device and maintains the information through wireless sensor network;and the service robot that perform tasks in collaboration with the environment. In this paper, the multi-pattern information model is built, the construction of wireless sensor networks is presented, the smart and agilely home service is introduced. Fi- nally, the future direction of intelligent space system is discussed.

C_(3)N_(4)/TiO_(2)二维异质结:高效的表面反应与电荷分离效率

高效的电荷分离和快速的界面反应动力学是决定光催化分解水制氢效率的关键因素.通过采用原位剥离和转化策略,本文设计合成了一种具有优异光催化析氢性能的二维异质结光催化剂-超薄多孔C_(3)N_(4)纳米片负载TiO_(2)纳米晶(HCN/TiO_(2)).结果表明,HCN/TiO_(2)光催化材料具有超薄二维结构、丰富且接触紧密的界面、高度多孔结构和TiO_(2)纳米晶分布均匀的特点.以上结构优势不仅能为HCN/TiO_(2)光催化分解水制氢提供更多的活性中心、促进反应物和生成物的传输,而且极大地提升其光生载流子的分离效率.因此,由于表面反应和光生载流子分离二者协同增强,HCN/TiO_(2)光催化剂(10 mg)在模拟太阳光(AM 1.5)照射条件下的析氢速率可达到282.3μmol h^(-1),表观量子效率在420nm波长处为13.4%.该工作为开发高性能二维异质结材料应用于清洁能源领域提供了一个极具前景的合成策略.

Distributed environment representation and object localization system in intelligent space

一种新环境表示和对象本地化计划在试图在聪明的空格更高效地完成对象操作的任务的纸被建议。首先，一个分布式的环境表示方法被提出减少存储负担并且改进系统稳定性。分层的拓扑的地图独立在纳入聪明的空间的关键位置的不同里程碑被存储以便机器人能寻找地图信息能从 QR 代码，然后环境地图在上被读的里程碑能独立地被造。地图造是为目标搜索的一个重要前提。一个目标搜索计划基于 RFID 和视觉技术被建议。RFID 标签被纳入目标对象，引用在室内的环境反对。一个固定 RFID 系统被造监视目标的不平的位置(空间和本地区域) ，一个活动 RFID 系统被构造检测不在固定系统的盖住的范围的目标。等到引用标签和目标标签的顺序，和精确位置被机载的视觉系统在短距离获得，目标的存在区域被决定。实验证明在纸建议的分布式的环境表示能充分满足目标本地化的要求，并且放的计划有高搜索效率，高本地化精确性和精确，和在复杂室内的环境的一个强壮的反干扰能力。

A deep Q-learning network based active object detection model with a novel training algorithm for service robots

This paper focuses on the problem of active object detection(AOD).AOD is important for service robots to complete tasks in the family environment,and leads robots to approach the target ob ject by taking appropriate moving actions.Most of the current AOD methods are based on reinforcement learning with low training efficiency and testing accuracy.Therefore,an AOD model based on a deep Q-learning network(DQN)with a novel training algorithm is proposed in this paper.The DQN model is designed to fit the Q-values of various actions,and includes state space,feature extraction,and a multilayer perceptron.In contrast to existing research,a novel training algorithm based on memory is designed for the proposed DQN model to improve training efficiency and testing accuracy.In addition,a method of generating the end state is presented to judge when to stop the AOD task during the training process.Sufficient comparison experiments and ablation studies are performed based on an AOD dataset,proving that the presented method has better performance than the comparable methods and that the proposed training algorithm is more effective than the raw training algorithm.