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Current robotic studies are focused on the performance of specific tasks. However, such tasks cannot be generalized, and some special tasks, such as compliant and precise manipulation, fast and flexible response, and deep collaboration between humans and robots, cannot be realized. Brain-inspired intelligent robots imitate humans and animals, from inner mechanisms to external structures, through an integration of visual cognition, decision making, motion control, and musculoskeletal systems. This kind of robot is more likely to realize the functions that current robots cannot realize and become human friends. With the focus on the development of brain-inspired intelligent robots, this article reviews cutting-edge research in the areas of brain-inspired visual cognition, decision making, musculoskeletal robots, motion control, and their integration. It aims to provide greater insight into brain-inspired intelligent robots and attracts more attention to this field from the global research community.

This literature review of the most prominent academic and non-academic publications in the last 10 years on the question of whether intelligent robots should be entitled to human rights is the first review of its kind in the academic context. We review three challenging academic contributions and six non-academic but important popular texts in blogs, magazines, and newspapers which are also frequently cited in academia. One of the main findings is that several authors base their critical views (i.e. views against recognizing human rights for robots) on misleading ethical and philosophical assumptions and hence offer flawed arguments regarding the moral and legal status of artificial intelligent robots and their possible claims to human rights. Our analysis sheds light on some of the complex and challenging issues related to the crucial question of whether intelligent robots with human-like capabilities are eventually entitled to human rights (or not).

K. Miyashita, Y. Masuda, M. Gunji, A. Fukuhara, K. Tadakuma and M. Ishikawa, "Emergence of Swing-to-Stance Transition from Interlocking Mechanism in Horse Hindlimb," 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA, 2020, pp. 7860-7865, doi: 10.1109/IROS45743.2020.9341026., The bodies of quadrupeds have very complex muscle-tendon structure. In particular, it is known that in the horse hindlimb, multiple joints in the leg are remarkably interlocked due to the muscle-tendon structure. Although the function of these interlocking mechanisms during standing has been investigated in the field of anatomy, the function related to the emergence of limb trajectory during dynamic walking has not been revealed. To investigate the role of the interlocking mechanism, we developed a robot model imitating the muscle-tendon arrangement and the dynamics of a horse hindlimb. In the walking experiment, the robot autonomously generated a limb trajectory with a smooth transition between the swing phase and the stance phase by simply swinging the hip joint with sinusoidal input. Moreover, we compared the joint angles between successful and failed walking. The compared results indicate that the extension of the fetlock joint after hoof touchdown plays the crucial role in emergence of a function of supporting body.

Recent developments in the field of service robots have led to a renewed interest in human-robot coexistence environments such as at home and office. In this regard, this study focuses on one of such service robots, a human-following mobile robot. In particular, we consider predicting the future trajectory of pedestrians using a machine learning algorithm to improve the accuracy of tracking people. Massive trajectory data is required in existing methods to train the prediction model; however, collecting a sufficient amount of data in general public places before providing services is challenging. Therefore, in this study, we propose a trajectory prediction method based on extracting similar datasets from a large-size dataset and generating a pre-trained prediction model using the extracting datasets. We express the data features in the source and target environments as probability distributions and evaluate the divergence between them. Specifically, the dataset features are expressed as a multidimensional Gaussian distribution and discrete distribution of samples. Then, similarities using the Kullback-Leibler divergence are compared. To verify the effectiveness of the proposed method, we compare the prediction results of the LSTM-based algorithm with those obtained by extracting multiple source datasets from a large dataset and training prediction models using these datasets. The result shows that the proposed method makes it possible to construct an appropriate prediction model with high accuracy in trajectory prediction.

In recent years, reinforcement learning (RL) has been widely used to solve multi-agent navigation tasks, and a high-fidelity level for the simulator is critical to narrow the gap between simulation and real-world tasks. However, high-fidelity simulators have high sampling costs and bottleneck the training model-free RL algorithms. Hence, we propose a Multi-Fidelity Simulator framework to train Multi-Agent Reinforcement Learning (MFS-MARL), reducing the total data cost with samples generated by a low-fidelity simulator. We apply the depth-first search to obtain local feasible policies on the low-fidelity simulator as expert policies to help the original reinforcement learning algorithm explore. We built a multi-vehicle simulator with variable fidelity levels to test the proposed method and compared it with the vanilla Soft Actor-Critic (SAC) and expert actor methods. The results show that our method can effectively obtain local feasible policies and can achieve a 23% cost reduction in multi-agent navigation tasks.

In this paper, fast path planning of on-water automatic rescue intelligent robot is studied based on the constant thrust artificial fluid method. First, a three-dimensional environment model is established, and then the kinematics equation of the robot is given. The constant thrust artificial fluid method based on the isochronous interpolation method is proposed, and a novel algorithm of constant thrust fitting is researched through the impulse compensation. The effect of obstacles on original fluid field is quantified by the perturbation matrix, and the streamlines can be regarded as the planned path. Simulation results demonstrate the effectiveness of this method by comparing with A-star algorithm and ant colony algorithm. It is proved that the planned path can avoid all obstacles smoothly and swiftly and reach the destination eventually.

Smart Electromechanical Systems (SEMS) are used in cyber-physical systems and, in particular, in intelligent robots. These systems have the ability to integrate the functions of computing, controlling, communicating, storing information, monitoring, measuring, and controlling their own parameters and the environmental parameters. It is important to keep in mind that the behavior of the system is based on information received from the sense sensors of the robot’s central nervous system (CNS) about the state of the environment and the state of the system. The aim of the paper is to describe an algebraic approach to form images based on the sensor data of robots built based on the SEMS modules. An algebraic approach for the formation of images based on the sensory data of robots built based on the SEMS modules is proposed. The algebraic approach to the formation of images based on the sensory data proposed in the article can be used to form the strategy and tactics for controlling intelligent robots.

Automation is not a new phenomenon, and questions about its effects have long followed its advances. More than a half-century ago, US President Lyndon B. Johnson established a national commission to examine the impact of technology on the economy, declaring that automation "can be the ally of our prosperity if we will just look ahead". In this paper, our premise is that we are at a technological inflection point in which robots are developing the capacity to greatly increase their cognitive and physical capabilities, and thus raising questions on labor dynamics. With increasing levels of autonomy and human-robot interaction, intelligent robots could soon accomplish new human-like capabilities such as engaging into social activities. Therefore, an increase in automation and autonomy brings the question of robots directly participating in some economic activities as autonomous agents. In this paper, a technological framework describing a robot economy is outlined and the challenges it might represent in the current socio-economic scenario are pondered., 10 pages, 5 figures

As intelligent robots enter our daily routine, it is important to be equipped with proper adaptable social perception and explainable behaviours. To do so, machine learning (ML) is often employed. This paper intends to find a trend in the way ML methods are used and applied to model human social perception and produce explainable robot behaviours. The literature has shown a substantial advancement in ML methods with application to social perception and explainable behaviours. There are papers which report models for robots to imitate humans and also for humans to imitate robots. Others use classical methods and propose new and/or improved ones which led to better human-robot interaction performances. This paper reports a review on social perception and explainable behaviours based on ML methods. First, we present literature background on these three research areas and finish with a discussion on limitations and future research venues.

In order to help students be more interested in learning about programming both at school and at home, we have designed educational toys in the form of humanoid robot. In this paper, intelligent robot movement control systems are developed, thus the students can interact directly and learn programming on human robots. For building a low-cost humanoid robot, we modified low-cost robot toy, that is Robosapien from Wowwee by embbeding a control system. The control system is developed based on Raspberry Pi Zero W, which enables us to control Robosapien's movements wirelessly. This learning media is expected to enhance the student’s spirit of learning, especially in the field of robotics and programming. The main contribution of the research is the description and analysis of various design decisions during the development of this humanoid robot. In addition, it provides information about the main components for development and example for interacting with robots as learning media.

Robotic automation has proliferated various industrial deployments including manufacturing, retail warehousing and logistics supply chains. In order for robots to advance to the next stage of cognitive autonomy, a robust framework for planning, execution and adaptation is needed. While there have been advances in abstract automated planning systems, they are still ill-suited to be applied within runtime robotic executions, which take place in uncertain environments. In this study, the authors provide a deliberative robotic planning and simulated execution framework called RoboPlanner that provides a pragmatic integration of automated planning, orchestration and adaptive deployments. This is coupled with an execution monitor and plan repair module, that allows reconfiguration to various template actions with runtime changes. Structured rules for re-planning in the case of state changes, unforeseen obstacles or execution failures are provided. They demonstrate their simulation framework on a realistic example of mobile pick & delivery robots in Industry 4.0 warehouses, that plan, execute, adapt and re-plan in sync with a knowledge base.

We present a novel implementation of a Rock-Paper-Scissors (RPS) game interaction with a social robot. The framework is tailored to be computationally lightweight, as well as entertaining and visually appealing through collaboration with designers and animators. The fundamental gesture recognition pipeline employs a Leap motion device and two separate machine learning architectures to evaluate kinematic hand data on-the-fly. The first architecture is used to recognize and segment human motion activity in order to initialize the RPS play, and the second architecture is used to classify hand gestures into rock, paper or scissors. The employed tabletop robot interacts in the RPS play through unique animated gestural movements and vocalizations designed by animators which communicate the robot's choices as well as cognitive reflection on winning, losing and draw states. Performance of both learning architectures is carefully evaluated with respect to accuracy, reliability and run time performance under different feature and classifier types. Moreover, we assess our system during an interactive RPS play between robot and human. Experimental results show that the proposed system is robust to user variations and play style in real environment conditions. As such, it offers a powerful application for the subsequent exploration of social human-machine interaction.

This paper proposes and evaluates an embedded architecture aimed to promote the utilization of cognitive agents in cooperation with the Robotic Operating System (ROS), serving as an alternative for programming intelligent robots. It promotes the programming abstraction level in two directions. The first direction regards using cognitive agents facilities for programming the robots intelligence, consisting of its perceptions and related actions. The second direction exploits the facilities of using ROS layers for programming the robot interaction with its sensors and actuators. The paper reports experiments of using agents to command simulated UAVs while measuring performance metrics that allowed us to evaluate the benefits of the proposed architecture.

The Internet of Things and artificial intelligence have developed rapidly in recent decades, and intelligent robots have been used in various fields of production and life. There are many requirements for the functions, performance and intelligence of smar...

This paper aims at the inspection problems faced by photovoltaic power plants in the long-term operation of photovoltaic power plants in harsh environments such as Qinghai and Tibet plateaus under low air pressure, high cold and hypoxia, large temperature differences, centered on robot technology, fusion sensor application technology, image recognition and processing Technology and artificial intelligence and other advanced technologies, research and development and design of intelligent robot inspection system for photovoltaic power plants. The system is used to monitor the operating status of photovoltaic panels and meteorological parameters in real time to establish a prediction model, estimate the instantaneous power generation of photovoltaic power plants, and establish an image recognition model based on convolutional neural networks, which can accurately detect the failure of photovoltaic power plant equipment. Analysis and discrimination provide innovative technical testing methods for unattended photovoltaic power stations to achieve safe and stable operation of high-altitude photovoltaic power stations and good operational benefits.

Clustering on target positions is a class of centralized algorithms used to calculate the surveillance robots' displacements in the Cooperative Target Observation (CTO) problem. This work proposes and evaluates Fuzzy C-means (FCM) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) with K-means (DBSk) based self-tuning clustering centralized algorithms for the CTO problem and compares its performances with that of K-means. Two random motion patterns are adopted for the targets: in free space or on a grid. As a contribution, the work allows identifying ranges of problem configuration parameters in which each algorithm shows the highest average performance. As a first conclusion, in the challenging situation in which the relative speed of the targets is high, and the relative sensor range of the surveillance is low, for which the existing algorithms present a substantial drop in performance, the FCM algorithm proposed outperforms the others. Finally, the DBSk algorithm adapts very well in low execution frequency, showing promising results in this challenging situation.

From caretaking activities for elderly people to being assistive in healthcare setup, mobile and non-mobile robots have the potential to be highly applicable and serviceable. The ongoing pandemic has shown that human-to-human contact in healthcare institutions and senior homes must be limited. In this scenario, elderlies and immunocompromised individuals must be exclusively protected. Robots are a promising way to overcome this problem in assisted living environments. In addition, the advent of AI and machine learning will pave a way for intelligent robots with cognitive abilities, while enabling them to be more aware of their surroundings. In this paper, we discuss the general perspectives, potential research opportunities, and challenges arising in the area of robots in assisted living environments and present our research work pertaining to certain application scenarios, i.e., robots in rehabilitation and robots in hospital environments and pandemics, which, in turn, exhibits the growing prospects and interdisciplinary nature of the field of robots in assisted living environment.

It is anticipated that intelligent robots will gain the ability to learn from humans how to perform tasks, and will assist them in many contexts such as with household chores in the near future; therefore, people should have the confidence to trust these robots after teaching them how to do a task. Like most machines, robots may sometimes behave in an erroneous manner and such errors can easily undermine trust in the robots, depending on their severity. Nevertheless, when a robot has been taught a task by humans, we hypothesize that the teachers may ignore small mistakes made by the robot, if it shows significant improvements while practising the task. We first conducted a study with 173 participants in which the perceived severity of different robot errors in a household chore (preparing food) was investigated. We then used the results to create scenarios of different levels of severity and conducted a second study with 138 participants to investigate the impact of error severity on trust. Participants remotely taught their preferences in food preparation tasks to robots. Over several practice rounds, robots’ behaviour improved, but the robots made either (a) no errors, (b) a small, or (c) a big error at the end, depending on the experimental condition. Small errors significantly affected trust and big errors had an even more adverse impact. Trust in the robot was found to be correlated with personality traits of the participants as well as with their disposition to trust other people.

In order to clean the garbage effectively in small areas, such as communities, gardens and squares, and save the cost of garbage cleaning, this paper developed an intelligent robot, which can clean garbage independently outdoors. The vacuum cleaner on the chassis of the robot can inhale small garbage, and the flexible manipulator can grab big garbage. The robot identifies garbage based on OpenCV. To improve the accuracy of garbage recognition, the method of edge detection and contour detection is used in the process of image recognition. The test of cleaning efficiency based on the Yolo model shows that the recognition accuracy of the robot is 95%. It can clean garbage well.

Forty years ago the notion of configuration space (C-space) revolutionised robot motion planning for rigid and articulated objects. Despite great progress, handling deformable materials has remained elusive because of their infinite-dimensional shape-state space. Finding low-complexity representations has become a pressing research goal. This work tries to make a tiny step in this direction by proposing a state representation for textiles relying on the C-space of some distinctive points. A stratification of the configuration space for n points in the cloth is derived from that of the flag manifold, and topological techniques to determine adjacencies in manipulation-centred state graphs are developed. Their algorithmic implementation permits obtaining cloth state–space representations of different granularities and tailored to particular purposes. An example of their usage to distinguish between cloth states having different manipulation affordances is provided. Suggestions on how the proposed state graphs can serve as a common ground to link the perception, planning and manipulation of textiles are also made., This work is supported by the European Research Council (ERC) within the European Union Horizon 2020 Programme under grant agreement ERC-2016-ADG-741930 (CLOTHILDE: CLOTH manIpulation Learning from DEmonstrations) and by the Spanish State Research Agency through the María de Maeztu Seal of Excellence to IRI (MDM-2016-0656). We thank Dr Jordi Sanchez Riera for providing us with cloth simulations.

Intuitive and safe interfaces for robots are challenging issues in robotics. Robo-HUD is a gadget-less interaction concept for contactless operation of industrial systems. We use virtual collision detection based on time-of-flight sensor data, combined with augmented reality and audio feedback, allowing the operators to navigate a virtual menu by “hover and hold” gestures. When incorporated with virtual safety barriers, the collision detection also functions as a safety feature, slowing or stopping the robot if a barrier is breached. Additionally, a user focus recognition module monitors the awareness, enabling the interaction only when intended. Early case studies show that these features present good use-cases for inspection tasks and operation in difficult environments, where contactless operation is needed.

Due to the rapidly increasing need of human-robot interaction (HRI), more intelligent robots are in demand. However, the vast majority of robots can only follow strict instructions, which seriously restricts their flexibility and versatility. A critical fact that strongly negates the experience of HRI is that robots cannot understand human intentions. This study aims at improving the robotic intelligence by training it to understand human intentions. Different from previous studies that recognizing human intentions from distinctive actions, this paper introduces a method to predict human intentions before a single action is completed. The experiment of throwing a ball towards designated targets are conducted to verify the effectiveness of the method. The proposed deep learning based method proves the feasibility of applying convolutional neural networks (CNN) under a novel circumstance. Experiment results show that the proposed CNN-vote method out competes three traditional machine learning techniques. In current context, the CNN-vote predictor achieves the highest testing accuracy with relatively less data needed.

Applying the learning mechanism of natural living beings to endow intelligent robots with humanoid perception and decision-making wisdom becomes an important force to promote the revolution of science and technology in robot domains. Advances in reinforcement learning (RL) over the past decades have led robotics to be highly automated and intelligent, which ensures safety operation instead of manual work and implementation of more intelligence for many challenging tasks. As an important branch of machine learning, RL can realize sequential decision-making under uncertainties through end-to-end learning and has made a series of significant breakthroughs in robot applications. In this review article, we cover RL algorithms from theoretical background to advanced learning policies in different domains, which accelerate to solving practical problems in robotics. The challenges, open issues, and our thoughts on future research directions of RL are also presented to discover new research areas with the objective to motivate new interest.

Intelligent robots are redefining autonomous tasks but are still far from being fully capable of assisting humans in day to day tasks. An important requirement of collaboration is to have a clear understanding of each other's expectations and capabilities. Lack of which may lead to serious issues such as loose coordination between teammates, ineffective team performance, and ultimately mission failures. Hence, it is important for the robot to behave explicably to make themselves understandable to the human. One of the challenges here is that the expectations of the human are often hidden and dynamically changing as the human interacts with the robot. Existing approaches in plan explicability often assume the human's expectations are known and static. In this paper, we propose the idea of active explicable planning to address this issue. We apply a Bayesian approach to model and predict dynamic human beliefs to be more anticipatory, and hence can generate more efficient plans without impacting explicability. We hypothesize that active explicable plans can be more efficient and more explicable at the same time, compared to the plans generated by existing methods. From the preliminary results of Mturk study, we find that our approach effectively captures the dynamic belief of the human which can be used to generate efficient and explicable behavior that benefits from dynamically changing expectations.

While requirements for robot performance to perform a task are generally well understood, the requirements around the explanatory capabilities of these systems are often at best an afterthought. This results in a dangerous situation where neither users nor experts can predict situations where the robot will or will not work, nor understand what causes failures and unexpected behaviour. In this paper, we discuss and survey the field of Explainable Artificial Intelligence, as it relates to the generation of requirements for the development of safe, intelligent robots. We then present a categorisation of explanatory capabilities and requirements that aims to help users and developers alike to ensure an appropriate match between the types of explanations that a given application requires, and the capabilities of various underlying AI techniques.

The ship wall-climbing robot belongs to a branch of intelligent robots, and its application objects are large military and civilian ships. It is mainly engaged in special operations such as testing, welding, cleaning and descaling. With the rapid development of science and technology, ship wall-climbing robots have also developed from the original negative pressure adsorption wall-climbing robots to the current bionic foot-type wall-climbing robots. In practical applications, it is gradually replacing manual operations, greatly improving work efficiency. This article introduces various ship-climbing robots with adsorption, movement, and driving methods as the main categories. This article focuses on the magnetic adsorption method, combined with other adsorption methods, and analyzes the performance characteristics and existing problems of various wall-climbing robots. Finally, we summarize the current technical problems of wall-climbing robots, and then make a prospect for the future development of wall-climbing robots.

In order to solve the aging problem and the empty-nest elderly problem, to reduce the burden on children and make the elderly laugh often, enhance the promotion of robots in caring for the elderly, improve the quality of products, and increase the sense of experience in use, standing rationally Considering the ethical and moral issues of this model and the relevant policies issued by the government, it provides a certain idea for the smooth application of intelligent robot care for the elderly [1].

Assistive Robots have an inherent need of adapting to the user they are assisting. This is crucial for the correct development of the task, user safety, and comfort. However, adaptation can be performed in several manners. We believe user preferences are key to this adaptation. In this article, we evaluate the use of preferences for Physically Assistive Robotics tasks in a Human-Robot Interaction user evaluation. Three assistive tasks have been implemented consisting of assisted feeding, shoe-fitting, and jacket dressing, where the robot performs each task in a different manner based on user preferences. We assess the ability of the users to determine which execution of the task used their chosen preferences (if any). The obtained results show that most of the users were able to successfully guess the cases where their preferences were used even when they had not seen the task before. We also observe that their satisfaction with the task increases when the chosen preferences are employed. Finally, we also analyze the user's opinions regarding assistive tasks and preferences, showing promising expectations as to the benefits of adapting the robot behavior to the user through preferences., This work has been supported by the ERC project Clothilde (ERC-2016- ADG-741930), the HuMoUR project (Spanish Ministry of Science and Innovation TIN2017-90086-R) and by the Spanish State Research Agency through the María de Maeztu Seal of Excellence to IRI (MDM-2016-0656). Gerard Canal has also been supported by the Spanish Ministry of Education, Culture and Sport by the FPU15/00504 doctoral grant and the CHIST-ERA project COHERENT (EPSRC EP/V062506/1).

Robots provide important help in relieving work pressure and reducing workload. This study aimed to explore the impact of robots’ gender on individuals’ performance and their acceptance of robots. This study explored the effects of human-robot gender matching on the performance and the mediating role of acceptance of robots. A questionnaire survey was conducted and 212 valid questionnaires were collected. The results indicated that for the male respondents, their acceptance of opposite-sex robots was higher than the acceptance of same-sex robots and their performance was higher while working with opposite-sex robots. For the female respondents, their acceptance of same-sex robots was higher and their performance wasn’t influenced by robots’ gender. Moreover, the acceptance of robots mediated in the influence of gender matching on performance. Implications were discussed for future work.

Learning from Demonstration (LfD) is a paradigm that allows robots to learn complex manipulation tasks that can not be easily scripted, but can be demonstrated by a human teacher. One of the challenges of LfD is to enable robots to acquire skills that can be adapted to different scenarios. In this letter, we propose to achieve this by exploiting the variations in the demonstrations to retrieve an adaptive and robust policy, using Gaussian Process (GP) models. Adaptability is enhanced by incorporating task parameters into the model, which encode different specifications within the same task. With our formulation, these parameters can be either real, integer, or categorical. Furthermore, we propose a GP design that exploits the structure of replications, i.e., repeated demonstrations with identical conditions within data. Our method significantly reduces the computational cost of model fitting in complex tasks, where replications are essential to obtain a robust model. We illustrate our approach through several experiments on a handwritten letter demonstration dataset., This work has been partially funded by the European Union Horizon 2020 Programme under grant agreement no. 741930 (CLOTHILDE) and by the Spanish State Research Agency through the Maria de Maeztu Seal of Excellence to IRI [MDM-2016-0656].

We present a current automation trend, robot collaboration intelligence, to control and manage individual industrial robots to collaborate intelligently with advanced AI technologies. To increase the level of flexibility in manufacturing lines and warehouse/distribution centers, flexible agent–type robots such as automated guided vehicles have been adopted in many industries. As information technologies advance, these individual agent robots become smart and the fleet size of agents becomes larger. Robot collaboration intelligence is a newly emerging technology that allows intelligent robots to work in a more effective and efficient way. We introduce this emerging technology with industry cases and provide researchers with new research directions in automation and simulation with AI.

Social robots are fake users who are active in social networks. Usually, social robots imitate real users and publish a lot of tweets. Malicious social robots are destroying the security of social networks. Existing research uses machine learning technology to detect social robots from social users' attributes and tweets. The features extracted from tweets include emotional features, thematic features, etc. However, the redesigned intelligent robot can imitate a certain feature of normal users' tweets, which makes the detection of social robots more difficult. The purpose of this paper is to propose several methods of fusing the old features to obtain more complex features, which makes the design of social robots more difficult. The results show that the new feature fusion method has better accuracy than the single feature for social robot detection.

This paper presents the design and development of an autonomous intelligent robot on the Robotic Operating System (ROS) with automated navigation to monitor the grass health in turfs by spraying fertilizers or nutrients for an early detection of stress in the leaves through chlorophyll index estimation and further the disease detection is performed by processing the images snapped by the bot. The accuracy in regards to automated navigation of the bot is in the order of twenty centimeters in outdoor operations, while the accuracy of stress detection in leaves has been about 90% on field trials, which makes the system very promising for a commercial deployment.

In order to guard against the 2019-nCoV new coronavirus, this paper proposes a novel intelligent robot for epidemic identification and prevention. To examine people's health more comprehensively, an infrared thermal imaging camera is equipped on the humanoid eyes-head robot platform that has the ability to hear, speak and watch. The robot not only has common capabilities such as face recognition, cough detection, mask detection, and heart rate detection, but also epidemic prevention and detection capabilities such as respiratory frequency and body temperature detection.

With the rapid development of rail transit in China, there is a great demand for the manufacturing capacity of intelligent equipment. Facing the demand of efficient cleaning of work-piece in rail transit, this paper proposes the automatic cleaning of assembly work-piece of railway vehicle by intelligent robot.Intelligent industrial robots are used to wash and deburr the work-piece in the intelligent processing. The main cleaning parts are fasteners, rods and valve bodies. The main technology is clean and deburr the work-piece with holes and deep holes. Binocular vision is used to identify the coarse positioning and fine positioning of the work-piece with holes, and different types of work-pieces are stored in memory, which is convenient for the robot to quickly clean and deburr. According to the coordinates of image processing holes, the robot can automatically optimize the path and complete the cleaning work.

Understanding manipulation scenarios allows intelligent robots to plan for appropriate actions to complete a manipulation task successfully. It is essential for intelligent robots to semantically interpret manipulation knowledge by describing entities, relations and attributes in a structural manner. In this paper, we propose an implementing framework to generate high-level conceptual dynamic knowledge graphs from video clips. A combination of a Vision-Language model and an ontology system, in correspondence with visual perception and contextual semantics, is used to represent robot manipulation knowledge with Entity-Relation-Entity (E-R-E) and Entity-Attribute-Value (E-A-V) tuples. The proposed method is flexible and well-versed. Using the framework, we present a case study where robot performs manipulation actions in a kitchen environment, bridging visual perception with contextual semantics using the generated dynamic knowledge graphs.

nowadays development of IoT applications with robotics is an ongoing revaluation. This paper mainly focuses on the security, remote surveillance, and monitoring of our homes done by the surveillance robots. Remote surveillance has become the most important research topic over the past decade. Through this paper we put forward a surveillance robot that can be used in domestic areas and many other places. Robots are becoming important in our day to day life activities as they reduce the human labor and probability of error. We can control robots manually or they can be automatic based on the need of people. This paper focuses on design and implementation of mobile robot for obstacle detection and avoidance in a real-time basis.

Cloth manipulation has been mostly advancing in perception and modeling methods for cloth state estimation and grasping point detection. In comparison, less attention has been put on end-effector design. Indeed, most implementations use a parallel gripper that can only perform pinch grasps. Instead, a more versatile set of possible grasp types could ease many tasks by providing more support to certain parts of the object, as well as make feasible tasks that become very complex when executed with only pinch grasps. We present a versatile gripper design which, besides the common open-close thumb+finger(s) feature, it has a couple of reconfiguration degrees of freedom that offer, first, a wide base plane to provide support and, second, variable friction surface on the thumb tip. Our gripper can execute a versatile set of grasps that ease some complex tasks such as pick and place folded clothes or fold in the air. In addition, the variable friction mechanism enables a more robust pinch-and-slide manipulation to trace cloth edges. Our evaluation shows the gripper potential to execute a wide variety of cloth manipulation tasks, This work receives funding from the European Union Horizon 2020 Programme under grant agreement no. 741930 (CLOTHILDE), Spanish State Research Agency through the Mar´ıa de Maeztu Seal of Excellence to IRI (MDM-2016-0656) and the “Ramon y Cajal” Fellowship RYC-2017- 22703

Celem artykułu jest zaprezentowanie toposu człowieka w nawiązaniu do teorii mimesis na tle analizy współczesnych przykładów antropomorficznych, inteligentnych robotów. Uwzględnione są dwie warstwy analizy: pierwsza, wiążąca się z upodabnianiem takich robotów do wyidealizowanego ludzkiego ciała oraz druga, w której uwzględnia się podobieństwo powyższych robotów do człowieka w warstwie mentalnej, co wiąże się z zagadnieniem sztucznej inteligencji. Większość zaprezentowanych przykładów pochodzi ze świata sztuki, będąc efektem interdyscyplinarnej współpracy artystów z inżynierami. Artykuł zawiera analizę porównawczą, jak również część treści wynika z obserwacji uczestniczącej, co wiąże się w szeregu przypadkach z kontaktem autora tekstu z prezentowanymi inteligentnymi robotami. The objective of this paper is to discuss the commonplace of man in relation to the theory of mimesis in the context of the analysis of current examples of anthropomorphic and intelligent robots. Two aspects of the analysis have been taken into consideration. The first one is linked with the similarities of such robots to the idealized human body and the second one acknowledges mental similarities between the robots and humans, which entail the question of artificial intelligence. Most of the quoted examples derive from the world of art which has beco­me an interdisciplinary area of collaboration between artists and engineers. This contribution contains a comparative study and a part of it, in many cases, involves the contributor’s observa­tions on the presented intelligent robots.

Robotic Mobility in dynamic environments has gained popularity among many researchers over past few years. The environment where human beings perform their normal operations includes narrow spaces, sharp turns, slopes and steps. In addition, some times the field of operation may be found to be chemically hazardous, radioactive or reconnaissance in nature. It is not quite easy to develop intelligent robots with very precise mechanism suitable for specific navigational purposes. The balancing on two wheels and spin on the spot ability with smaller footprints of the bi-differential wheeled mobile robots distinguish them from other multi-wheel counterparts. In this paper some of the basic literatures regarding the motion planning of mobile robots are discussed using different artificial intelligence techniques. The robots can able to reach a respective goal positions by successfully avoiding the intermediate collisions among themselves and with the obstacles using these techniques as control scheme.

Social robots have become an important way to alleviate the impact of aging on society and families. This study aims to explore the influence of the living state of the elderly on robot acceptance. Based on Unified Theory of Acceptance and Use of Technology (UTAUT) questionnaire, this study conducted a questionnaire survey for the elderly. The results show that performance expectancy, social influence, facilitating conditions have significant positive effects on robot acceptance of the elderly. In addition, the gender of aging adults moderates the influence of performance expectancy on robot acceptance, the experience of using intelligent products for the elderly moderates the impact of social influence on robot acceptance, and the living state of the elderly moderates the significant influence of facilitating conditions on robot acceptance. Intelligent robots can improve the lives of aging adults living alone, and the higher the convenience of robots, the stronger the usage intention of aging adults living alone to use robots. The results of this study can help relevant researchers to better understand the psychological needs of the elderly, provide ideas for the design of robot products, and provide references for the establishment of social pension system.

The future of robotics, envisioned by the US military, is made up of humans teamed with autonomous, intelligent robots. A shift in human–robot interaction (HRI), from the teleoperation of robotic systems to a more teamwork-oriented interaction, concurrently changes the informational requirements of the actors in the interaction. Consequently, military research into robotics is exploring what information fulfills those changed informational requirements, how robots can convey that information to human teammates, and what information do robots need to acquire from those human teammates. In this chapter, we discuss how the conception of a military robot is anticipated to change, how that change influences the interaction between humans and robots, and some of the different lines of research being done to support future HRI.

In the backdrop of increasingly intelligent machines, important issues of law have been raised by the use of robots that operate autonomous from human supervisory control. In particular, when systems operating with autonomous robot’s damage property or injure humans, it may be difficult to determinewho’s at fault and therefore liable under current legal schemes. This paper reviews product liability and negligence tort law which may be used to allocate liability for robots that damage property or cause injury. Further, the paper concludes with a discussion of different approaches to allocating liability in an age of increasingly intelligent and autonomous robots directed by sophisticated algorithms, analytical, and computational techniques

This paper describes a novel method for guiding a robot in an environment containing moving objects. The robot predicts the motions of the objects and plans a collision-free path to the goal. It is a hybrid method that combines local and global approaches. This paper describes two new predicting algorithms, which are based on the AprioriAll algorithm, and one planning algorithm that is based on heuristic search. This method has been tested via a framework using a simulation environment. The results have shown that the robot succeeds in reaching the goal via collision-free paths at an average rate of about 94%.

The purpose of this research is to analyze and upgrade the performances of the Baxter intelligent robot, through data mining methods. The case study belongs to the robotics domain, being integrated in the context of manufacturing execution systems and product lifecycle management, aiming to overcome the lack of vertical integration inside a company. The explored data comprises the parameters registered during the activities of the Baxter intelligent robot, as, for example, the movement of the left or right arm. First, the state of the art concerning the data mining methods is presented, and then the solution is detailed by describing the data mining techniques. The final purpose was that of improving the speed and robustness of the robot in the production. Specific techniques and sometimes their combinations are experimented and assessed, in order to perform root cause analysis, then powerful classifiers and metaclassifiers, as well as deep learning methods, in optimum configuration, are analyzed for prediction. The experimental results are described and discussed in details, then the conclusions and further development possibilities are formulated. Based on the experiments, important relationships among the robot parameters were discovered, the obtained accuracy for predicting the target variables being always above 96%.

The introduction of service robots in the public domain has introduced a paradigm shift in how robots are interacting with people, where robots must learn to autonomously interact with the untrained public instead of being directed by trained personnel. As an example, a hospital service robot is told to deliver medicine to Patient Two in Ward Three. Without awareness of what “Patient Two” or “Ward Three” is, a service robot must systematically explore the environment to perform this task, which requires a long time. The implementation of a Semantic Map allows for robots to perceive the environment similar to people by associating semantic information with spatial information found in geometric maps. Currently, many semantic mapping works provide insufficient or incorrect semantic-metric information to allow a service robot to function dynamically in human-centric environments. This paper proposes a semantic map with a hierarchical semantic organization structure based on a hybrid metric-topological map leveraging convolutional neural networks and spatial room segmentation methods. Our results are validated using multiple simulated and real environments on our lab's custom developed mobile service robot and demonstrate an application of semantic maps by providing only vocal commands. We show that this proposed method provides better capabilities in terms of semantic map labeling and retain multiple levels of semantic information.

Skin-like sensors have attracted considerable attention in recent years owing to their important applications in intelligent robots, advanced medical equipment, and bionic technologies. However, most sensing skins are based on electronic systems that have many constraints and application limitations since their electronics-based structures have the disadvantages of complex structure, cannot withstand harsh environments, power supply needed, and so on. In this study, a transparent phosphorescence-based, flexible temperature-tactile multi-functional sensing skin was developed by arranging temperature-sensitive and mechanoluminescent microparticles in a polyimide. Based on the thermal-quenching of phosphorescence and mechanoluminescence, the temperature and stress signals are sensing wirelessly in the form of light. And due to the different phosphor materials, the temperature and stress signals are optically sensed and separated by wavelength. To enhance the sensitivity of tactile sensing, a unique sensor structure with protruding was constructed based on the tension effect of the polyimide solution to achieve effective tactile sensing. The temperature and tactile sensing functions of the skin were successfully demonstrated. In addition, its application to robot skin has also been verified. The current study shows the potential of the phosphorescence- based flexible sensing skin, indicating that it is a possible development direction for flexible sensing skin.