Performance evaluation of ambient services by combining robotic frameworks and a smart environment platform

Mobile robots and smart environments are two areas of research that can easily profit from each other. Smart environments, which are spaces unobtrusively equipped with sensors and actuators, providing ambient services to the people living within. Mobile robots inside those smart environments can use the existing infrastructure to increase their performance while decreasing the cost of local sensor systems. On the other side, evaluation of ambient services is often a laborious task. This work presents an approach that simplifies the evaluation by making use of two frameworks from robotics to perform tests in simulated smart environments. A method based on the language as action principle is used to extract realistic behavior of people living in real-world smart environments. Using this data, many different scenarios with varying configurations (different floor layouts, numbers and types of sensors, different number of people and pets) can easily be simulated and the performance of the ambient services evaluated.     

Peer to peer trade in HTM5 meta model for agent oriented cloud robotic systems

Cloud computing is a methodology and not a technology. Adaptation of cloud computing services for robotic applications is relatively straightforward while adaptation of underlying ideas will require a new design attitude. Cloud computing is a cost-effective and dynamic business model. Currently cloud robotics is understood as a client server methodology which enables robots utilize resources and services placed at centralized servers. These cloud servers treat robots as any other client computer offering them platform, infrastructure, process or algorithm as a service. HTM5 is an OMG MDA based multi-view meta-model for agent oriented development of cloud robotic systems. HTM5 encourages design of peer-to-peer service ecosystems based on an open registry and matchmaking mechanism. In peer-to-peer cloud robotics, a robot can trade its hardware, software and functional resources as a service to other robots in the ecosystem. The peer-to-peer trade in such systems may be driven by contracts and relationships between its member agents. This article discusses trade-view model of HTM5 methodology and its use in developing a cloud robotic ecosystem that implements peer-to-peer, contract based economy. The article also presents a case study with experiments that implement distributed artificial intelligence and peer-to-peer service oriented trade on simulated and real robot colonies.     

Robot apology as a post-accident trust-recovery control strategy in industrial human-robot interaction

Due to safety requirements for Human-Robot Interaction (HRI), industrial robots have to meet high standards of safety requirements (ISO 10218). However, even if robots are incapable of causing serious physical harm, they still may influence people's mental and emotional wellbeing, as well as their trust, behaviour and performance in close collaboration. This work uses an HTC Vive Virtual Reality headset to study the potential of using robot control strategies to positively influence human post-accident behaviour. In the designed scenario, a virtual industrial robot first makes sudden unexpected movements, after which it either does or does not attempt to apologise for them. The results show that after the robot tries to communicate with the participants, the robot is reported to be less scary, more predictable and easier to work with. Furthermore, postural analysis shows that the participants who were the most affected by the robot's sudden movement recover 74% of their postural displacement within 60 s after the event if the robot apologised, and only 34% if it did not apologise. It is concluded, that apologies, which are commonly used as a trust-recovery strategy in social robotics, can positively influence people engaged with industrial robotics as well.Relevance to industryFindings can be used as guidelines for designing robot behaviour and trust-recovery control strategies meant to speed up human recovery after a trust-violating event in industrial Human-Robot Interaction.     

Digital twin-based industrial cloud robotics: Framework,control approach and implementation

Industrial cloud robotics (ICR) integrates cloud computing with industrial robots (IRs). The capabilities of industrial robots can be encapsulated as cloud services and used for ubiquitous manufacturing. Currently, the digital models for process simulation, path simulation, etc. are encapsulated as cloud services. The digital models in the cloud may not reflect the real state of the physical robotic manufacturing systems due to inaccurate or delayed condition update and therefore result in inaccurate simulation and robotic control. Digital twin can be used to realize fine sensing control of the physical manufacturing systems by a combination of high-fidelity digital model and sensory data. In this paper, we propose a framework of digital twin-based industrial cloud robotics (DTICR) for industrial robotic control and its key methodologies. The DTICR is divided into physical IR, digital IR, robotic control services, and digital twin data. First, the robotic control capabilities are encapsulated as Robot Control as-a-Service (RCaaS) based on manufacturing features and feature-level robotic capability model. Then the available RCaaSs are ranked and parsed. After manufacturing process simulation with digital IR models, RCaaSs are mapped to physical robots for robotic control. The digital IR models are connected to the physical robots and updated by sensory data. A case is implemented to demonstrate the workflow of DTICR. The results show that DTICR is capable to synchronize and merge digital IRs and physical IRs effectively. The bidirectional interaction between digital IRs and physical IRs enables fine sensing control of IRs. The proposed DTICR is also flexible and extensible by using ontology models.     

受生物群体智能启发的多机器人系统研究

首先,介绍了生物社会中的群体智能．接着,讨论了群体机器人技术中的群体控制、群体通信和群体形态等主要研究内容及其发展趋势,并介绍了群体机器人系统的几个典型任务环境．最后,通过一个物体搜集任务的仿真实例来说明群体智能设计原则在多机器人系统中的应用．     

Mobile Robot Navigation for Moving Obstacles with Unpredictable Direction Changes,Including Humans

In many service applications, mobile robots need to share their work areas with obstacles. Avoiding moving obstacles with unpredictable direction changes, such as humans, is more challenging than avoiding moving obstacles whose motion can be predicted. Precise information on the future moving directions of humans is unobtainable for use in navigation algorithms. Furthermore, humans should be able to pursue their activities unhindered and without worrying about the robots around them. An enhanced virtual force field-based mobile robot navigation algorithm (termed EVFF) is presented for avoiding moving obstacles with unpredictable direction changes. This algorithm may be used with both holonomic and nonholonomic robots. It incorporates improved virtual force functions and an improved method for selecting the sense of the detour force to better avoid moving obstacles. For several challenging obstacle configurations, the EVFF algorithm is compared with five state-of-the-art navigation algorithms for moving obstacles. The navigation system with the new algorithm generated collision-free paths consistently. Methods for solving local minima conditions are proposed. Experimental results are also presented to further verify the avoidance performance of this algorithm.     

On Modular Design of Field Robotic Systems

Robots are needed to perform important field tasks such as hazardous material clean-up, nuclear site inspection, and space exploration. Unfortunately their use is not widespread due to their long development times and high costs. To make them practical, a modular design approach is proposed. Prefabricated modules are rapidly assembled to give a low-cost system for a specific task.This paper described the modular design problem for field robots and the application of a hierarchical selection process to solve this problem. Theoretical analysis and an example case study are presented. The theoretical analysis of the modular design problem revealed the large size of the search space. It showed the advantages of approaching the design on various levels.The hierarchical selection process applies physical rules to reduce the search space to a computationally feasible size and a genetic algorithm performs the final search in a greatly reduced space. This process is based on the observation that simple physically based rules can eliminate large sections of the design space to greatly simplify the search.The design process is applied to a duct inspection task. Five candidate robots were developed. Two of these robots are evaluated using detailed physical simulation. It is shown that the more obvious solution is not able to complete the task, while the non-obvious asymmetric design develop by the process is successful.     

一种云计算的虚拟网络管理系统

云计算技术是IT产业界的一场技术革命,已经成为IT行业未来发展的方向,这种变化使得IT基础架构的运营专业化程度不断集中和提高。在云计算的使用中,云计算使用者缺乏对于网络的配置能力,这部分目前并没有开放给用户所使用。虽然云计算的虚拟网络服务已经受到了更多云计算提供商的关注,但是目前对这方面的支持还处于不完善的阶段。本文提出了一种基于云计算的虚拟网络管理系统,本系统能够以动态的方式为用户提供基于云计算的网络服务,可以根据用户需求实现虚拟网络配置,并加以优化,以便最大限度地提高虚拟网络的性能。     

The evolution of robotics research

This article surveys traditional research topics in industrial robotics and mobile robotics and then expands on new trends in robotics research that focus more on the interaction between human and robot. The new trends in robotics research have been denominated service robotics because of their general goal of getting robots closer to human social needs, and this article surveys research on service robotics such as medical robotics, rehabilitation robotics, underwater robotics, field robotics, construction robotics and humanoid robotics. The aim of this article is to provide an overview of the evolution of research topics in robotics from classical motion control for industrial robots to modern intelligent control techniques and social learning paradigms, among other aspects     

面向智能家居/智慧生活的服务机器人技术与系统

随着机器人技术的发展以及服务机器人形态的日益丰富,智能家居/智慧生活逐渐成为一种未来的生活方式。文章探索了智能家居和智慧生活的服务机器人技术与系统,在其众多的关键技术中,重点探讨了面向服务机器人的云端融合技术以及高用户体验度的人-机器人交互技术。并以家庭服务机器人为例,实现了基于微信和语音云的人-机器人交互方式,验证了基于云架构的家庭服务机器人体系结构的可行性。文章所提出的服务机器人技术与系统是智能家居、智慧生活的一种实现方式,为未来形形色色的服务机器人技术方案提供了一些思路。     

Robots, insects and swarm intelligence

The aim of this paper is to consider the relationships between robots and insects. To this end, an overview is provided of the two main areas in which insects have been implicated in robotics research. First, robots have been used to provide working models of mechanisms underlying insect behaviour. Second, there are developments in robotics that have been inspired by our understanding of insect behaviour; in particular the approach of swarm robotics. In the final section of the paper, the possibility of achieving “strong swarm intelligence” is discussed. Two possible interpretations of strong swarm intelligence are raised: (1) the emergence of a group mind from a natural, or robot swarm, and (2) that behaviours could emerge from a swarm of artificial robots in the same way as they emerge from a biological swarm. Both interpretations are dismissed as being unachievable in principle. It is concluded that bio-robotic modelling and biological inspiration have made important contributions to both insect and robot research, but insects and robots remain separated by the divide between the living and the purely mechanical.     

Survey on frontiers of language and robotics

ABSTRACT

The understanding and acquisition of a language in a real-world environment is an important task for future robotics services. Natural language processing and cognitive robotics have both been focusing on the problem for decades using machine learning. However, many problems remain unsolved despite significant progress in machine learning (such as deep learning and probabilistic generative models) during the past decade. The remaining problems have not been systematically surveyed and organized, as most of them are highly interdisciplinary challenges for language and robotics. This study conducts a survey on the frontier of the intersection of the research fields of language and robotics, ranging from logic probabilistic programming to designing a competition to evaluate language understanding systems. We focus on cognitive developmental robots that can learn a language from interaction with their environment and unsupervised learning methods that enable robots to learn a language without hand-crafted training data.     

Social and collaborative aspects of interaction with a service robot

To an increasing extent, robots are being designed to become a part of the lives of ordinary people. This calls for new models of the interaction between humans and robots, taking advantage of human social and communicative skills. Furthermore, human–robot relationships must be understood in the context of use of robots, and based on empirical studies of humans and robots in real settings. This paper discusses social aspects of interaction with a service robot, departing from our experiences of designing a fetch-and-carry robot for motion-impaired users in an office environment. We present the motivations behind the design of the Cero robot, especially its communication paradigm. Finally, we discuss experiences from a recent usage study, and research issues emerging from this work. A conclusion is that addressing only the primary user in service robotics is unsatisfactory, and that the focus should be on the setting, activities and social interactions of the group of people where the robot is to be used.     

From bio-inspired vs. psycho-inspired to etho-inspired robots

Artificial intelligence has been roughly divided into two schools of thought since its beginnings, the symbolic (also known as GOFAI, neat, classical, etc.) and the subsymbolic one (connectionist, scruffy, etc.). These two approaches have also had strong influence on the robotics field. In this paper we want to present a less known one, based on Ethology, and its application to the generation of autonomous behavior in mobile robots. In this way, we present the foundations of JDE (Jerarquía Dinámica de Esquemas), an etho-inspired architecture where behavior is organized as a dynamic hierarchy of independent schemas, as well as some examples of its applications, and a discussion about its properties.     

Measuring learning progress in intelligent autonomous robots

AI researchers claim to understand some aspect of human intelligence when their model is able to “emulate” it. In the contexts of mobile robots, the ability to go from teleoperation to autonomy should accordingly be treated not simply as a trick for robotics hackers but as an important epistemological and methodological goal. In this article the experience derived from designing locomotion control systems for both teleoperated and autonomous multi-legged articulated robots is described.     

Scheduling of decentralized robot services in cloud manufacturing with deep reinforcement learning

Cloud manufacturing is a service-oriented manufacturing model that offers manufacturing resources as cloud services. Robots are an important type of manufacturing resources. In cloud manufacturng, large-scale distributed robots are encapsulated into cloud services and provided to consumers in an on-demand manner. How to effectively and efficiently manage and schedule decentralized robot services in cloud manufacturing to achieve on-demand provisioning is a challenging issue. During the past few years, Deep Reinforcement Learning (DRL) has become very popular and successfully been applied to many different areas such as games, robotics, and manufacturing. DRL also holds tremendous potential for solving scheduling issues in cloud manufacturing. To this end, this paper is devoted to exploring effective approaches for scheduling of decentralized robot manufacturing services in cloud manufacturing with DRL. Specifically, both Deep Q-Networks (DQN) and Dueling Deep Q-Networks (DDQN)-based scheduling algorithms are proposed. Performance of different algorithms, including DQN, DDQN, and other three benchmark algorithms, indicates that DDQN performs the best with respect to each indicator. Effects of different combinations of weight coefficients and influencing degrees of different indicators on the overall scheduling objective are analyzed. Results indicate that the DDQN-based scheduling algorithm is able to generate scheduling solutions efficiently.     

Anthropomorphism and the social robot

This paper discusses the issues pertinent to the development of a meaningful social interaction between robots and people through employing degrees of anthropomorphism in a robot’s physical design and behaviour. As robots enter our social space, we will inherently project/impose our interpretation on their actions similar to the techniques we employ in rationalising, for example, a pet’s behaviour. This propensity to anthropomorphise is not seen as a hindrance to social robot development, but rather a useful mechanism that requires judicious examination and employment in social robot research.     

深度学习在智能机器人中的应用研究综述

机器人发展的趋势是人工智能化,深度学习是智能机器人的前沿技术,也是机器学习领域的新课题。深度学习技术被广泛运用于农业、工业、军事、航空等领域,与机器人的有机结合能设计出具有高工作效率、高实时性、高精确度的智能机器人。为了增强智能机器人在各方面的能力,使其更智能化,介绍了深度学习与机器人有关的研究项目与深度学习在机器人中的各种应用,包括室内和室外的场景识别、机器人的工业服务和家庭服务以及多机器人协作等。最后,对深度学习在智能机器人中应用的未来发展、可能面临的机遇和挑战等进行了讨论。     

A middleware to integrate robots,simple devices and everyday objects into an ambient ecology

The fields of ambient intelligence, distributed robotics and wireless sensor networks are converging toward a common vision, in which ubiquitous sensing and acting devices cooperate to provide useful services in the home. These devices can range from sophisticated mobile robots to simple sensor nodes and even simpler tagged everyday objects. In this vision, a milkbox left on the table after the user has left the home could ask the service of a mobile robot to be placed back in the refrigerator. A missing ingredient to realize this vision is a mechanism that enables the communication and interoperation among such highly heterogeneous entities. In this paper, we propose such a mechanism in the form of a middleware able to integrate robots, tiny devices and augmented everyday objects into one and the same system. The key moves to cope with heterogeneity are: the definition of a tiny, compatible version of the middleware, that can run on small devices; and the concept of object proxy, used to make everyday object accessible within the middleware. We describe the concepts and implementation of our middleware, and show a number of experiments that illustrate its performance.