HDec-POSMDPs MRS Exploration and Fire Searching Based on IoT Cloud Robotics

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.

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.

Architectural Design of a Cloud Robotic System for Upper-Limb Rehabilitation with Multimodal Interaction

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.

Research on robot grabbing system based on hybrid cloud

Robot grabbing has been successfully applied to a range of challenging environments but met the resource bottleneck. To answer this question, a hybrid cloud-based robot grabbing system is proposed, which supports centralized bin-picking management and deployment, large-scale storage, and communication technologies. The hybrid cloud combines the powerful computational capabilities through massive parallel computation and higher data storage facilities in the public cloud with data privacy in the private data center. The benchmark tasks against a public cloud based on robot grabbing method are evaluated, whose results indicate that the whole system reduces the data collection time and increases elastic resource scheduling and is adapted in the real industry.

Cloud-assisted cognition adaptation for service robots in changing home environments

Robots need more intelligence to complete cognitive tasks in home environments.In this paper,we present a new cloud-assisted cognition adaptation mechanism for home service robots,which learns new knowledge from other robots.In this mechanism,a change detection approach is implemented in the robot to detect changes in the user’s home environment and trigger the adaptation procedure that adapts the robot’s local customized model to the environmental changes,while the adaptation is achieved by transferring knowledge from the global cloud model to the local model through model fusion.First,three different model fusion methods are proposed to carry out the adaptation procedure,and two key factors of the fusion methods are emphasized.Second,the most suitable model fusion method and its settings for the cloud–robot knowledge transfer are determined.Third,we carry out a case study of learning in a changing home environment,and the experimental results verify the efficiency and effectiveness of our solutions.The experimental results lead us to propose an empirical guideline of model fusion for the cloud–robot knowledge transfer.