The rise in the cases of motor impairing therapy. Due to the current situation that the service illnesses demands the research for improvements in rehabilitation of the professional therapists cannot meet the need of ...The rise in the cases of motor impairing therapy. Due to the current situation that the service illnesses demands the research for improvements in rehabilitation of the professional therapists cannot meet the need of the motorimpaired subjects, a cloud robotic system is proposed to provide an Internet-based process for upper-limb rehabilitation with multimodal interaction. In this system, therapists and subjects are connected through the Internet using client/server architecture. At the client site, gradual virtual games are introduced so that the subjects can control and interact with virtual objects through the interaction devices such as robot arms. Computer graphics show the geometric results and interaction haptic/force is fed back during exercising. Both video/audio information and kinematical/physiological data axe transferred to the therapist for monitoring and analysis. In this way, patients can be diagnosed and directed and therapists can manage therapy sessions remotely. The rehabilitation process can be monitored through the Internet. Expert libraries on the central server can serve as a supervisor and give advice based on the training data and the physiological data. The proposed solution is a convenient application that has several features taking advantage of the extensive technological utilization in the area of physical rehabilitation and multimodal interaction.展开更多
The multi-robot systems(MRS)exploration and fire searching problem is an important application of mobile robots which require massive computation capability that exceeds the ability of traditional MRS′s.This paper pr...The multi-robot systems(MRS)exploration and fire searching problem is an important application of mobile robots which require massive computation capability that exceeds the ability of traditional MRS′s.This paper propose a cloud-based hybrid decentralized partially observable semi-Markov decision process(HDec-POSMDPs)model.The proposed model is implemented for MRS exploration and fire searching application based on the Internet of things(IoT)cloud robotics framework.In this implementation the heavy and expensive computational tasks are offloaded to the cloud servers.The proposed model achieves a significant improvement in the computation burden of the whole task relative to a traditional MRS.The proposed model is applied to explore and search for fire objects in an unknown environment;using different sets of robots sizes.The preliminary evaluation of this implementation demonstrates that as the parallelism of computational instances increase the delay of new actuation commands which will be decreased,the mean time of task completion is decreased,the number of turns in the path from the start pose cells to the target cells is minimized and the energy consumption for each robot is reduced.展开更多
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.Mos...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.展开更多
为了解决现有激光SLAM(simultaneous localization and mapping)方法忽略垂直方向漂移而导致的高度不准确和地图重影问题,提出了一种基于垂直约束的紧耦合激光惯性SLAM方法。该方法结合激光雷达传感器的安装高度以及点到激光雷达的距离...为了解决现有激光SLAM(simultaneous localization and mapping)方法忽略垂直方向漂移而导致的高度不准确和地图重影问题,提出了一种基于垂直约束的紧耦合激光惯性SLAM方法。该方法结合激光雷达传感器的安装高度以及点到激光雷达的距离提取精确的地面点,基于提取的地面点设计了一种考虑垂直方向残差的激光里程计,使用两步列文伯格-马夸尔特(Levenberg-Marquardt,L-M)方法来求解姿态变换,这些残差将有助于在垂直方向上收敛到最优解。使用简单有效的基于欧氏距离的回环检测方法避免地图重影问题。为验证算法的优越性,在KITTI数据集及真实场景下均进行了相关实验。在KITTI数据集上,与LeGO-LOAM、LIO-SAM和Point-LIO相比,轨迹均方根误差(root mean square error,RMSE)分别降低了47.62%、33.14%和73.79%。在实测校园环境中,与LeGO-LOAM、LIO-SAM和Point-LIO相比,RMSE分别降低了83.56%、13.55%和82.04%,从而验证了提出方法具有更高的定位精度。展开更多
基金This work was supported by the National Key Research and Development Program of China under Grant No. 2016YFB1001300, the National Natural Science Foundation of China under Grant No. 61403080, and the Natural Science Foundation of Jiangsu Province Technology Support Plan under Grant No. BK20140641.
文摘The rise in the cases of motor impairing therapy. Due to the current situation that the service illnesses demands the research for improvements in rehabilitation of the professional therapists cannot meet the need of the motorimpaired subjects, a cloud robotic system is proposed to provide an Internet-based process for upper-limb rehabilitation with multimodal interaction. In this system, therapists and subjects are connected through the Internet using client/server architecture. At the client site, gradual virtual games are introduced so that the subjects can control and interact with virtual objects through the interaction devices such as robot arms. Computer graphics show the geometric results and interaction haptic/force is fed back during exercising. Both video/audio information and kinematical/physiological data axe transferred to the therapist for monitoring and analysis. In this way, patients can be diagnosed and directed and therapists can manage therapy sessions remotely. The rehabilitation process can be monitored through the Internet. Expert libraries on the central server can serve as a supervisor and give advice based on the training data and the physiological data. The proposed solution is a convenient application that has several features taking advantage of the extensive technological utilization in the area of physical rehabilitation and multimodal interaction.
基金Corresponding au-thor:Ayman El Shenawy received the Ph.D.degree in systems and computer engineer-ing from Al-Azhar University,Egypt in 2013.He is currently working as a lecturer at Systems and Computers Engineering Department,Faculty of Engineering Al-Azhar University,Egypt.He already de-veloped some breakthrough research in the mentioned areas.He made significant con-tributions to the stated research fields.His research interests include artificial intelligent methods,robotics and machine learning.E-mail:eaymanelshenawy@azhar.edu.eg ORCID iD:0000-0002-1309-644。
文摘The multi-robot systems(MRS)exploration and fire searching problem is an important application of mobile robots which require massive computation capability that exceeds the ability of traditional MRS′s.This paper propose a cloud-based hybrid decentralized partially observable semi-Markov decision process(HDec-POSMDPs)model.The proposed model is implemented for MRS exploration and fire searching application based on the Internet of things(IoT)cloud robotics framework.In this implementation the heavy and expensive computational tasks are offloaded to the cloud servers.The proposed model achieves a significant improvement in the computation burden of the whole task relative to a traditional MRS.The proposed model is applied to explore and search for fire objects in an unknown environment;using different sets of robots sizes.The preliminary evaluation of this implementation demonstrates that as the parallelism of computational instances increase the delay of new actuation commands which will be decreased,the mean time of task completion is decreased,the number of turns in the path from the start pose cells to the target cells is minimized and the energy consumption for each robot is reduced.
基金supported in part by the National Natural Science Foundation of China under Grant 62273203,Grant U1813215in part by the Special Fund for the Taishan Scholars Program of Shandong Province(ts201511005).
文摘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.
文摘为了解决现有激光SLAM(simultaneous localization and mapping)方法忽略垂直方向漂移而导致的高度不准确和地图重影问题,提出了一种基于垂直约束的紧耦合激光惯性SLAM方法。该方法结合激光雷达传感器的安装高度以及点到激光雷达的距离提取精确的地面点,基于提取的地面点设计了一种考虑垂直方向残差的激光里程计,使用两步列文伯格-马夸尔特(Levenberg-Marquardt,L-M)方法来求解姿态变换,这些残差将有助于在垂直方向上收敛到最优解。使用简单有效的基于欧氏距离的回环检测方法避免地图重影问题。为验证算法的优越性,在KITTI数据集及真实场景下均进行了相关实验。在KITTI数据集上,与LeGO-LOAM、LIO-SAM和Point-LIO相比,轨迹均方根误差(root mean square error,RMSE)分别降低了47.62%、33.14%和73.79%。在实测校园环境中,与LeGO-LOAM、LIO-SAM和Point-LIO相比,RMSE分别降低了83.56%、13.55%和82.04%,从而验证了提出方法具有更高的定位精度。
