Automatic generation of context-aware workflow documents for ubiquitous robot services

Intelligent robots in ubiquitous computing environment should be able to receive a variety of surrounding informa tion and provide users with appropriate services. A developer can describe the robot services that are proper to users＇ envir onments by using his or her various environments, and process them through the execution engine. However, it is difficult for a developer to describe and develop robot services, who knows all surrounding information which is called context infor mation. If there is a method for describing and documenting robot services in intuitive expressions, that is to use graphical user interfaces（GUls）, it would be very helpful. This paper suggests that robot service developers describe robot services us ing intuitive GUls with contextawareness. And the services can be automatically generated into workflow documents. Robot services that robot service developers have made with intuitive GUIs can be automatically generated into workflow docu ments by using the object modeling technique（OMT）. Developers can describe robot services based on contextaware work flow language（CAWL ）. For testing, scenariobased robot services are described using CAWLbased development tool, and their workflow documents are automatically generated.

Robotic Etiquette:Socially Acceptable Navigation of Service Robots with Human Motion Pattern Learning and Prediction

Nonverbal and noncontact behaviors play a significant role in allowing service robots to structure their interactions withhumans.In this paper, a novel human-mimic mechanism of robot’s navigational skills was proposed for developing sociallyacceptable robotic etiquette.Based on the sociological and physiological concerns of interpersonal interactions in movement,several criteria in navigation were represented by constraints and incorporated into a unified probabilistic cost grid for safemotion planning and control, followed by an emphasis on the prediction of the human’s movement for adjusting the robot’spre-collision navigational strategy.The human motion prediction utilizes a clustering-based algorithm for modeling humans’indoor motion patterns as well as the combination of the long-term and short-term tendency prediction that takes into accountthe uncertainties of both velocity and heading direction.Both simulation and real-world experiments verified the effectivenessand reliability of the method to ensure human’s safety and comfort in navigation.A statistical user trials study was also given tovalidate the users’favorable views of the human-friendly navigational behavior.

Architecture and Software Design for a Service Robot in an Elderly-Care Scenario

Systems for ambient assisted living(AAL) that integrate service robots with sensor networks and user monitoring can help elderly people with their daily activities, allowing them to stay in their homes and live active lives for as long as possible. In this paper, we outline the AAL system currently developed in the European project Robot-Era, and describe the engineering aspects and the serviceoriented software architecture of the domestic robot, a service robot with advanced manipulation capabilities. Based on the robot operating system(ROS) middleware, our software integrates a large set of advanced algorithms for navigation, perception, and manipulation. In tests with real end users, the performance and acceptability of the platform are evaluated.

Two types of coaxial self-balancing robots

Two types of coaxial self-balancing robots(CSBR)were proposed,one can be used as a mobile robot platform for parts transporting in unmanned factory or as an inspector in dangerous areas,and the other can be used as a personal transporter ridden in cities.Mechanical designing and control structures as well as control strategies were described and compared in order to get a general way to develop such robots.A state feedback controller and a fuzzy controller were designed for the robot using DC servo motors and the robot using torque motors,respectively.The experiments indicate that the robots can realize various desired operations smoothly and agilely at the velocity of 0.6 m/s with an operator of 65 kg.Furthermore,the robustness of the controllers is revealed since these controllers can stabilize the robot even with unknown external disturbances.

Information perception and feedback mechanism and key techniques of multi-modality human-robot interaction for service robots

With the increasing of the elderly population and the growing hearth care cost, the role of service robots in aiding the disabled and the elderly is becoming important. Many researchers in the world have paid much attention to heaRthcare robots and rehabilitation robots. To get natural and harmonious communication between the user and a service robot, the information perception/feedback ability, and interaction ability for service robots become more important in many key issues.

Semantic Region Estimation of Assistant Robot for the Elderly Long-Term Operation in Indoor Environment

In this work, in order to improve spatial recognition abilities for the long-term operation tasks of the assistant robot for the elderly, a novel approach of semantic region estimation is proposed. We define a novel graphbased semantic region descriptions, which are estimated in a dynamically fashion. We propose a two-level update algorithm, namely, Symbols update level and Regions update level. The algorithm firstly adopts particle filter to update weights of the symbols, and then use the Viterbi algorithm to estimate the region the robot stays in based on those weights, optimally. Experimental results demonstrate that our proposed approach can solve problems of the long-term operation and kidnapped robot problem.

Research on Hand-eye Calibration Technology of Visual Service Robot Grasping Based on ROS

For the hand-eye calibration of the vision service robot,the traditional hand-eye calibration tech-nology can’t be realized which because the service robot is independently developed and there is no teaching device to feed back the pose information of the service robot in real time.In this paper,a hand-eye calibration method based on ROS(Robot Operating System)is proposed.In this method,ROS system is used to accurately control the arm of the service robot to rotate in different positions for many times.Meanwhile,the head camera of the service robot takes images of a fixed point in the scene.Then,the nonlinear equations were established ac-cording to the homography matrix of the two images and the position and pose information of the ROS system,and the accurate hand-eye relationship was optimized by the least square method.Finally,an experimental platform is built and the proposed hand-eye calibration method is verified.The experiment results show that the method is easy to operate,simple algorithm and correct result,which verifies the effectiveness of the algorithm and provides conditions for the realization of humanoid grasping of visual service robot.

Service Robot Localization Based on Global Vision and Stereo Vision

To satisfy different positioning accuracy demand in the movement of the service robot, a hierarchical localization method based on vision was proposed considering the positioning cost and computational efficiency. The hierarchical positioning method could be divided into two parts: the localization method based on global vision and the localization method based on binocular vision. The global vision-based localization method was applied to calculate the initial coordinates of the robot and realize real-time rough positioning with the dead-reckoning method in the first stage. In the last stage, the binocular vision-based localization method was adopted to obtain higher positioning accuracy to make sure that the service robot can successfully grab the object. Experiments indicate that the proposed algorithm can accurately evaluate the positioning performance, obtain up to ±2 cm positioning accuracy, and absolutely meet the positioning requirements of the indoor service robot.

Main Stages of Evolution from Mechanical Automatic Systems to Humanoid Robot

Since ancient times, man has been concerned to create an artificial structure similar to itself. This concern has remained constant attention and creative thinkers most profound since antiquity until today, it is of greatest interest. This article aims to highlight the most significant moments of the evolution from mechanical creatures mythical or real, from antiquity to the present humanoid robots. There are thus shown the most significant variants humanoid imagined or realized since Talos, described in Greek mythology, continuing with mechanical machines during late Roman Empire, the Byzantine Empire and then the Renaissance of the post-renaissance, then they are shown the main achievements of the industrialization period from the nineteenth to the early part of the twentieth century, then to the most important made in the last decades of the twentieth century and early twenty-first century. The work is important to stimulate concerns academic theorists and practice but also in the industrial practice specializing in robotics and more generally in mechatronics. It also opens the prospect of defining the concept of an artificial humanoid useful both for human prosthesis with certain disabilities, and a humanoid robot performance.

Enhanced Map-Based Indoor Navigation System of a Humanoid Robot Using Ultrasound Measurements

During recent years, walking humanoid robots have gained popularity from wheeled vehicle robots in various assistive roles in human’s environment. Self-localization is a necessary requirement for the humanoid robots used in most of the assistive tasks. This is because the robots have to be able to locate themselves in their environment in order to accomplish their tasks. In addition, autonomous navigation of walking robots to the pre-defined destination is equally important mission, and therefore it is required that the robot knows its initiate location precisely. The indoor navigation is based on the map of the environment used by the robot. Assuming that the walking robot is capable of locating itself based on its initiate location and the distance walked from it, there are still factors that impair the map-based navigation. One of them is the robot’s limited ability to keep its direction when it is walking, which means that the robot is not able to walk directly from one point to another due to a stochastic error in walking direction. In this paper we present an algorithm for straightening the walking path using distance measurements by built-in sonar sensors of a NAO humanoid robot. The proposed algorithm enables the robot to walk directly from one point to another, which enables precise map-based indoor navigation.

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.

Building of cognizing semantic map in large-scale semi-unknown environment

The quick response code based artificial labels are applied to provide semantic concepts and relations of surroundings that permit the understanding of complexity and limitations of semantic recognition and scene only with robot's vision.By imitating spatial cognizing mechanism of human,the robot constantly received the information of artificial labels at cognitive-guide points in a wide range of structured environment to achieve the perception of the environment and robot navigation.The immune network algorithm was used to form the environmental awareness mechanism with "distributed representation".The color recognition and SIFT feature matching algorithm were fused to achieve the memory and cognition of scenario tag.Then the cognition-guide-action based cognizing semantic map was built.Along with the continuously abundant map,the robot did no longer need to rely on the artificial label,and it could plan path and navigate freely.Experimental results show that the artificial label designed in this work can improve the cognitive ability of the robot,navigate the robot in the case of semi-unknown environment,and build the cognizing semantic map favorably.

Towards delivery of situational information in distributed environments

In the distributed environment,robots should be able to provide users with adaptive services automatically according to the situational information changing dynamically which is obtained from both users and their environments.The workflow depends on situational information obtained from physical environments and provides context-aware services automatically based on the information retrieved.And the workflow in the business processes and the distributed computing environments have supported the automation of services by connecting tasks.The workflow needs to specify ubiquitous situational information as state-transition constraints.However,the delivery and use of sensor data in the workflow is a difficult problem for the robot system.In order to bridge the gap between applications and low-level constructs and to acquire raw situational information for the execution of the context-aware workflow in the robot systems,this paper presents an approach which can achieve the sensor data transmission between a sensing server and a robot system easily.

A navigation method based on POMDP for smart wheelchair in uncertain environments

A navigation method based on the partially observable markov decision process （POMDP） for smart wheelchairs in uncertain environments is presented in this paper. The design key factors for the navigation system of a smart wheelchair are discussed. A kinematics model of the smart wheelchair is given, and the model and principle of POMDP are introduced. In order to respond in uncertain local environments, a novel navigation methodology based on POMDP using the sensors perception and the user＇s joystick input is presented. The state space, the action set, the observations and the sensor fusion of the navigation method are given in detail, and the optimal policy of the POMDP model is proposed. Experimental results demonstrate the feasibility of this navigation method. Analysis is also conducted to investigate performance evaluation, advantages of the approach and potential generalization of this paper.

Emotion Modelling for Social Robotics Applications： A Review

Robots of today are eager to leave constrained industrial environments and embrace unexplored and unstructured areas, for extensive applications in the real world as service and social robots. Hence, in addition to these new physical frontiers, they must face human ones, too. This implies the need to consider a human-robot interaction from the beginning oft_he design; the possibility for a robot to recognize users＇ emotions and, in a certain way, to properly react and ＂behave＂. This could play a fundamental role in their integration in society. However, this capability is still far from being achieved. Over the past decade, several attempts to implement automata for different applications, outside of the industry, have been pursued. But very few applications have tried to consider the emotional state of users in the behavioural model of the robot, since it raises questions such as： how should human emotions be modelled for a correct representation of their state of mind？ Which sensing modalities and which classification methods could be the most feasible to obtain this desired knowl- edge？ Furthermore, which applications are the most suitable for the robot to have such sensitivity？ In this context, this paper aims to provide a general overview of recent attempts to enable robots to recognize human emotions and interact properly.

Item Ownership Relationship Semantic Learning Strategy for Personalized Service Robot

In order to satisfy the robotic personalized service requirements that can select exclusive items to perform inference and planning according to different service individuals,the service robots need to have the ability to independently obtain the ownership relationship between humans and their carrying items.In this work,we present a novel semantic learning strategy for item ownership.Firstly,a human-carrying-items detection network based on human posture estimation and object detection model is used.Then,the transferred convolutional neural network is used to extract the characteristics of the objects and the back-end classifier to recognize the object instance.At the same time,the face detection and recognition model are used to identify the service individual.Finally,on the basis of the former two,the active learning of ownership items is completed.The experimental results show that the proposed ownership semantic learning strategy can determine the ownership relationship of private goods accurately and efficiently.The solution of this problem can improve the intelligence level of robot life service.

Intelligence evolution for service robot： An ADRC perspective

The strategy of active disturbance rejection control （ADRC） and its applications in intelligence evolution for service robot are summarized. It is also shown that the philosophy of ADRC is consistent with the essential characteristics of intelligence evolution. Most importantly, we concentrate on five core issues which will be encountered when applying ADRC to deal with intelligence evolution for service robot, that is, how to eliminate the impact of unknown composite disturbances, how to handle the nonholonomic constraints in uncalibrated visual servoing, how to realize eye-hand-torque coordination, how to deal with the disturbance in simultaneous localization and mapping （SLAM）, and how to reject the imperfections induced by network in human-robot interaction. The main purpose of this paper is to clarify the challenges encountered on intelligence evolution for service robot when one applies ADRC to, hoping that more and more researchers can give some suggestions or work together to deal with these problems, and flourishing results of ADRC from both theory and applications.

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.

Multimodal Integration Processes in Plan-Based Service Robot Control

Cross-modal integration processes are essential for service robots to reliably perceive relevant parts of the partially known unstructured environment, We demonstrate how multimodal integration on different abstraction levels leads to reasonable behavior that would be difficult to achieve with unimodal approaches. Sensing and acting modalities are composed to multimodal robot skills via a fuzzy multisensor fusion approach. Single modalities constitute basic robot skills that can dynamically be composed to appropriate behavior by symbolic planning. Furthermore, multimodal integration is exploited to answer relevant queries about the partially known environment. All these approaches are successfully implemented and tested on our mobile service robot platform TASER,

Improved Rao-Blackwellized particle filter for simultaneous robot localization and person-tracking with single mobile sensor

A probabilistic algorithm is proposed for the problem of simultaneous robot localization and peopletracking （SLAP） using single onboard sensor in situations with sensor noise and global uncertainties over the observer＇s pose. By the decomposition of the joint distribution according to the Rao-Blackwell theorem, posteriors of the robot pose are sequentially estimated over time by a smoothed laser perception model and an improved resampling scheme with evolution strategies; the conditional distribution of the person＇s position is estimated using unscented Kalman filter （UKF） to deal with the nonlinear dynamic of human motion. Experiments conducted in a real indoor service robot scenario validate the favorable performance of the positional accuracy as well as the improved computational efficiency.