Deployment Of Robots Research Articles

Application of an adapted FMEA framework for robot-inclusivity of built environments

Mobile robots are deployed in the built environment at increasing rates. However, lack of considerations for a robot-inclusive planning has led to physical spaces that would potentially pose hazards to robots, and contribute to an overall productivity decline for mobile service robots. This research proposes the use of an adapted Failure Mode and Effects Analysis (FMEA) as a structured tool to evaluate a building’s level of robot-inclusivity and safety for service robot deployments. This Robot-Inclusive FMEA (RIFMEA) framework, is used to identify failures in the built environment that compromise the workflow of service robots, assess their effects and causes, and provide recommended actions to alleviate these problems. The method was supported with a case study of deploying telepresence robots in a university campus. The study concluded that common failures were related to poor furniture design, a lack of clearance and hazard indicators, and sub-optimal interior planning.

Humanoid robots go to school

Humanoid robots are being used in some schools, however, research on the use of these robots is relatively new. In this three-year study, humanoid robots were deployed in 10 schools, involving 29 teachers across early childhood to Year 10. This study aimed to ascertain teachers’ perceptions of the benefits and challenges, the pedagogical practices that helped with student engagement, and where the robots fitted in the curriculum. Through multiple case studies, teachers’ views were explored and results from the study supports the deployment of humanoid robots. The study advocates a constructivist inquiry approach to using humanoid robots across the curriculum.

Human, Organisational and Societal Factors in Robotic Rail Infrastructure Maintenance

Robotics are set to play a significant role in the maintenance of rail infrastructure. However, the introduction of robotics in this environment requires new ways of working for individuals, teams and organisations and needs to reflect societal attitudes if it is to achieve sustainable goals. The following paper presents a qualitative analysis of interviews with 25 experts from rail and robotics to outline the human and organisational issues of robotics in the rail infrastructure environment. Themes were structured around user, team, organisational and societal issues. While the results point to many of the expected issues of robotics (trust, acceptance, business change), a number of issues were identified that were specific to rail. Examples include the importance of considering the whole maintenance task lifecycle, conceptualizing robotic teamworking within the structures of rail maintenance worksites, the complex upstream (robotics suppliers) and downstream (third-party maintenance contractors) supply chain implications of robotic deployment and the public acceptance of robotics in an environment that often comes into direct contact with passenger and people around the railways. Recommendations are made in the paper for successful, human-centric rail robotics deployment.

Unknown Object Detection Using a One-Class Support Vector Machine for a Cloud-Robot System.

Inter-robot communication and high computational power are challenging issues for deploying indoor mobile robot applications with sensor data processing. Thus, this paper presents an efficient cloud-based multirobot framework with inter-robot communication and high computational power to deploy autonomous mobile robots for indoor applications. Deployment of usable indoor service robots requires uninterrupted movement and enhanced robot vision with a robust classification of objects and obstacles using vision sensor data in the indoor environment. However, state-of-the-art methods face degraded indoor object and obstacle recognition for multiobject vision frames and unknown objects in complex and dynamic environments. From these points of view, this paper proposes a new object segmentation model to separate objects from a multiobject robotic view-frame. In addition, we present a support vector data description (SVDD)-based one-class support vector machine for detecting unknown objects in an outlier detection fashion for the classification model. A cloud-based convolutional neural network (CNN) model with a SoftMax classifier is used for training and identification of objects in the environment, and an incremental learning method is introduced for adding unknown objects to the robot knowledge. A cloud–robot architecture is implemented using a Node-RED environment to validate the proposed model. A benchmarked object image dataset from an open resource repository and images captured from the lab environment were used to train the models. The proposed model showed good object detection and identification results. The performance of the model was compared with three state-of-the-art models and was found to outperform them. Moreover, the usability of the proposed system was enhanced by the unknown object detection, incremental learning, and cloud-based framework.

Emotion and service quality of anthropomorphic robots

Deployment of robots has become prevalent in the service sector. The advanced digital technologies, especially artificial intelligence (AI), and the current COVID-19 pandemic have accelerated the wide application of service robots to provide quality customer experience with limited in-person service encounters. Many of today's service robots perform not only complex services, but they are anthropomorphic with physical and psychological states like humans. This study investigates how the varying degree of anthropomorphism in robots affect customers’ perceived service quality. A research model with associated hypotheses was developed based on affect-as-information theory. The study collected data from 455 randomly selected customers who were served by either a mechanical robot (automatic on-demand coffee maker) or an anthropomorphic robot (travel information assistant). The results of the study showed that anthropomorphism has a significant positive impact on intimacy between robots and humans, intimacy has significant positive impact on customers’ perceived service quality, and intimacy has a mediating role on the relationship between anthropomorphism and service quality.

Machine-learning-accelerated design of functional structural components in deep-sea soft robots

To explore the deepest regions of the ocean with high flexibility and environmental adaptability, deep-sea soft robots have been developed recently. One prominent example is the self-powered soft robot that successfully operated in the Mariana Trench at a depth of 11,000 meters. Notably, many functional electronic components such as resistive elements, capacitors, and crystal oscillators may fail under extreme hydrostatic pressure, posing significant challenges for the practical massive deployment of deep-sea soft robots. Consequently, designing miniature pressure vessels on the printed circuit board to protect these vulnerable electronic components is vital for enhancing the reliability of deep-sea soft robots. However, traditional structure design methods – which often rely on theoretical analysis, experimental testing and numerical simulations – are often costly and time-consuming, especially for design problems in high-dimensional design spaces. Herein, we demonstrate a machine-learning-accelerated design method for devising miniature pressure vessels for vulnerable electronic components in deep-sea soft robots. Machine learning algorithms including decision trees and neural network models are employed and compared. The resulting design algorithm can predict whether a specific design can survive the deep-sea hydrostatic pressure with high accuracy in ∼0.35 ms, roughly seven orders of magnitude faster than traditional design methods.

Quadruped robots venture into the wild with open eyes.

Perception-based locomotion learning accelerates the real-world deployment of quadruped robots.

Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning

The last half decade has seen a steep rise in the number of contributions on safe learning methods for real-world robotic deployments from both the control and reinforcement learning communities. This article provides a concise but holistic review of the recent advances made in using machine learning to achieve safe decision-making under uncertainties, with a focus on unifying the language and frameworks used in control theory and reinforcement learning research. It includes learning-based control approaches that safely improve performance by learning the uncertain dynamics, reinforcement learning approaches that encourage safety or robustness, and methods that can formally certify the safety of a learned control policy. As data- and learning-based robot control methods continue to gain traction, researchers must understand when and how to best leverage them in real-world scenarios where safety is imperative, such as when operating in close proximityto humans. We highlight some of the open challenges that will drive the field of robot learning in the coming years, and emphasize the need for realistic physics-based benchmarks to facilitate fair comparisons between control and reinforcement learning approaches.

Human-robot collaboration: A multilevel and integrated leadership framework

In an era of rapid advances in artificial intelligence, the deployment of robots in organizations is accelerating. Further, robotic capabilities are expanding to serve a broader range of leadership behaviors related to task accomplishment and relationship support. Despite the increasing use of robots in various roles across different industries, research on human-robot collaboration in the workplace is lagging behind. As such, the current research aims to provide a state-of-the-science review and directions for future work in this underdeveloped area. Drawing on current leadership paradigms, we review human-robot collaboration studies from four academic disciplines with a history of publishing such work (i.e., management, economics, psychology, engineering) and propose that the research trajectory of human-robot collaboration parallels the evolution of leadership research paradigms (i.e., leader centric, relational view, and follower centric). Given that leadership is an inherently multilevel phenomenon, we apply a levels-of-analysis framework to integrate and synthesize human-robot collaboration studies from cross-disciplinary research areas. Based on our findings, we offer suggestions for future research in terms of conceptualization, theory building and testing, practical implications, and ethical considerations.

Evaluation of Lidar-based 3D SLAM algorithms in SubT environment

Autonomous navigation of robots in harsh and GPS denied subterranean (SubT) environments with lack of natural or poor illumination is a challenging task that fosters the development of algorithms for pose estimation and mapping. Inspired by the need for real-life deployment of autonomous robots in such environments, this article presents an experimental comparative study of 3D SLAM algorithms. The study focuses on state-of-the-art Lidar SLAM algorithms with open-source implementation that are i) lidar-only like BLAM, LOAM, A-LOAM, ISC-LOAM and hdl graph slam, or ii) lidar-inertial like LeGO-LOAM, Cartographer, LIO-mapping and LIO-SAM. The evaluation of the methods is performed based on a dataset collected from the Boston Dynamics Spot robot equipped with 3D lidar Velodyne Puck Lite and IMU Vectornav VN-100, during a mission in an underground tunnel. In the evaluation process poses and 3D tunnel reconstructions from SLAM algorithms are compared against each other to find methods with most solid performance in terms of pose accuracy and map quality.

Designing a Virtual Reality Testbed for Direct Human-Swarm Interaction in Aquatic Species Monitoring

Swarm robotics is a natural candidate for monitoring large and complex environments. With the aid of a human who is directly involved in monitoring and search using a teleoperated robot, swarms of autonomous robots can solve problems related to navigation and changing objectives. In this context, understanding the role of nonverbal interactions have the potential to enable rapid and bidirectional communication between humans and robots. Quantifying such interactions however, requires a repeatable and engaging experimental setup that can track human action and cognition. This paper describes a desktop virtual reality testbed designed specifically to quantify human actions, perception, and cognitive load in real time during a monitoring mission. Motivated by recent deployment of underwater robots to monitor invasive species, the testbed is designed to mimic an underwater environment within the Great Lakes with five species of fish whose appearance, locomotion, and behavior are modeled based on videos from the field. Brain activity and pupillometry data are recorded synchronously in real time to aid in the measurement of cognitive load of the human operator. To quantify human perception, empirical data of visual acuity from the literature is used to model virtual object recognition. The capabilities of the testbed are evaluated in terms of frame rate achieved as a function of number of fish and robots in the environment, and demonstrated through two examples highlighting possible uses in human-swarm interaction studies.

TeMoto: A Software Framework for Adaptive and Dependable Robotic Autonomy With Dynamic Resource Management

For widespread deployment of robots in challenging environments (fire fighting, search and rescue, planetary exploration, etc), the software of the robot must allow for reliability and adaptability. For many existing systems, an unexpected change in mission specification, component failures, or energy conservation requires downtime for adaption (redesign of mission logic, switching sensor data processing pipeline, etc.). This is because software and hardware components for robotic applications are commonly chosen or designed based on task requirements and integrated either directly in the source code or via system configuration scripts, such as ROS launch files, leading to a fixed monolithic design. As the necessity and extent of adaptive behaviors is not always known prior to deployment, the structure of a robot’s software needs to support it by design. In this paper, we propose TeMoto, a novel architecture for adaptive autonomous robots, and a ROS-based framework of openly available software tools that implement the TeMoto architecture. TeMoto is a developer tool which combines dynamic (run-time) task and resource management, encourages modular and scalable system design and is task and platform agnostic - TeMoto provides the foundation for an adaptive robotic system. The feasibility of the TeMoto framework is qualitatively assessed via experiments on single and multi-robot setups spanning common scenarios (teleoperation, autonomous surveillance, cargo delivery, etc.). TeMoto-based systems exhibit increased fault tolerance and dynamic reconfigurability of software and hardware resources, as well as up to a 47% reduction in power consumption compared to the non TeMoto-enabled reference setup.

Non-Contact Service Robot Development in Fast-Food Restaurants

Given the prospect of low birth rate and aging population among developed countries, which further resulted in the shortage of workforces, service robots have been gradually applied to real-life from past academic research. This paper introduces the development of a service robot that is designed for food service in fast-food restaurants with the innovative improvement of mapping, localization, and navigation. Moreover, this research took the initiative of integrating 3D point cloud map and 2D occupancy grid map (OGM) in order to build a PC-OGM. In another word, using the sensory fusion method allows the service robot to adapt to a more complicated environment as well as enhance its positioning. In terms of its navigation function, the adaptive motion controller is refined so the service robot could navigate through narrow aisles smoothly. Finally, friendly contact-free food service robots were evaluated at fast-food restaurants in order to gain feedback from diners and waiters. Their feedback was broken down into 3 categories, availability, reliability, and satisfaction, for further analysis. As COVID-19 assails the world, we also look into future possibilities of food service robot deployment among restaurants to keep food and surface free from the virus.

Warehouse robot market boosted by Covid pandemic and technological innovations

PurposeThis paper aims to provide details of recent commercial and technological developments that are driving robotic warehouse automation.Design/methodology/approachFollowing a short introduction, this first provides a commercial background and identifies the factors driving the market growth. It then gives examples of robotics companies, products and applications that exploit innovations in artificial intelligence (AI). It then considers future prospects, and finally, brief conclusions are drawn.FindingsAmazon’s acquisition of Kiva led to a community of new robot manufacturers and the realisation by major e-commerce companies that robotic automation would be required to maintain competitiveness. The Covid pandemic caused a surge in e-commerce and a critical shortage of labour, which further highlighted the need for automation and boosted robotic deployments. Recent advances in AI have resulted in a rapidly growing community of companies producing AI-powered robots which offer advanced capabilities such as mixed product picking, sorting and kitting. These are being deployed by a growing number of e-commerce and logistics companies and are paving the way towards ever-higher levels of warehouse automation. Full automation will soon become a reality.Originality/valueThis paper identifies the factors driving the rapidly developing warehouse robot business by considering the companies, products, technology and applications.

A Dual‐Origami Design that Enables the Quasisequential Deployment and Bending Motion of Soft Robots and Grippers

Soft fluidic actuators produce continuous and life‐like motions that are intrinsically safe, but current designs are not yet mature enough to enable large deployment with high force and low‐cost fabrication methods. Herein, soft fluidic actuators with two superimposed origami architectures are reported. Driven by a fluid input, the presented dual‐origami soft actuators produce quasisequential deployment and bending motion that is guided by unsymmetric unfolding of low‐stretchable origami components. The dominance between the deployment and bending can be shifted by varying the unfolding behavior, enabling preprogramming of the motion. The proposed origami‐inspired soft actuators are directly fabricated by low‐cost fused deposition modeling 3D printing and subjected to heat treatment postprocessing to enhance the fluid sealing performance. Finally, soft gripper applications are presented and they successfully demonstrate gripping tasks where each requires strength, delicacy, precision, and dexterity. The dual‐origami approach offers a design guidance for soft robots to embody grow‐and‐retract motion with a small initial form factor, promising for applications in next‐generation soft robotic systems. An interactive preprint version of the article can be found here: https://www.authorea.com/doi/full/10.22541/au.163698906.68661340.

See Spot save lives: fear, humanitarianism, and war in the development of robot quadrupeds

Boston Dynamics’ robotic quadrupeds have achieved infamy and virality through a series of social media videos since 2008. In 2019 Boston Dynamics began commercial sale of ‘Spot’, a moving, sensing, networked robot dog. Spot has been designed to be a platform, which can be augmented with hardware payloads (e.g. sensors, robotic arm) and software to command Spot to conduct specific missions. In this paper we first trace the development of Spot and highlight the interest of the United States military in its development. This is followed by our text analysis of social media reactions to Boston Dynamics’ quadrupeds, revealing public fascination as well as ongoing suspicion and dark humour about ‘killer robots’. We then discuss how humanitarian applications, including in response to the COVID-19 pandemic, have been used as an opportunity to promote Spot and overcome public negativity. This is an example of a more general strategy advocates use to garner acceptance for autonomous robots in both civilian and military roles using humanitarian justifications: the robots ‘save lives.’ We conclude by discussing how Spot and other robot quadrupeds demonstrate the intertwining of humanitarian and military applications in the development, normalization and deployment of autonomous robots.

10 Years of Human-NAO Interaction Research: A Scoping Review.

The evolving field of human-robot interaction (HRI) necessitates that we better understand how social robots operate and interact with humans. This scoping review provides an overview of about 300 research works focusing on the use of the NAO robot from 2010 to 2020. This study presents one of the most extensive and inclusive pieces of evidence on the deployment of the humanoid NAO robot and its global reach. Unlike most reviews, we provide both qualitative and quantitative results regarding how NAO is being used and what has been achieved so far. We analyzed a wide range of theoretical, empirical, and technical contributions that provide multidimensional insights, such as general trends in terms of application, the robot capabilities, its input and output modalities of communication, and the human-robot interaction experiments that featured NAO (e.g. number and roles of participants, design, and the length of interaction). Lastly, we derive from the review some research gaps in current state-of-the-art and provide suggestions for the design of the next generation of social robots.

Explicit feature disentanglement for visual place recognition across appearance changes

In the long-term deployment of mobile robots, changing appearance brings challenges for localization. When a robot travels to the same place or restarts from an existing map, global localization is needed, where place recognition provides coarse position information. For visual sensors, changing appearances such as the transition from day to night and seasonal variation can reduce the performance of a visual place recognition system. To address this problem, we propose to learn domain-unrelated features across extreme changing appearance, where a domain denotes a specific appearance condition, such as a season or a kind of weather. We use an adversarial network with two discriminators to disentangle domain-related features and domain-unrelated features from images, and the domain-unrelated features are used as descriptors in place recognition. Provided images from different domains, our network is trained in a self-supervised manner which does not require correspondences between these domains. Besides, our feature extractors are shared among all domains, making it possible to contain more appearance without increasing model complexity. Qualitative and quantitative results on two toy cases are presented to show that our network can disentangle domain-related and domain-unrelated features from given data. Experiments on three public datasets and one proposed dataset for visual place recognition are conducted to illustrate the performance of our method compared with several typical algorithms. Besides, an ablation study is designed to validate the effectiveness of the introduced discriminators in our network. Additionally, we use a four-domain dataset to verify that the network can extend to multiple domains with one model while achieving similar performance.

A Multidimensional Analysis of Robotic Deployment in Thai Hotels.

Several hotel businesses are interested in robotic deployment to improve customer service, enhance hotel productivity, and gain a competitive advantage. In this study, I deployed a robot in a real working environment to explore stakeholders’ perceptions and investigate factors involved in robotic deployment in Thai hotels. A qualitative research method, interpretative phenomenological analysis, was utilized. As there was no robotic deployment in Thai hotels, Peanut, a navigation service robot, was temporarily assigned to work in the hotels to allow key informants—hotel executives, human resource managers, reception managers, hotel staff, and hotel guests—to understand the phenomenon and experience robotic deployment. The findings revealed that stakeholders in Thai hotels accepted the robot; however, this acceptance was contingent on various robot, human, and organizational dimensions. Greater user acceptance can promote more widespread robotic deployment. This study has important implications for human–robot interactions in the hotel industry, especially in Thailand where this technology has not yet been applied.

Immunized Token-Based Approach for Autonomous Deployment of Multiple Mobile Robots in Burnt Area

Collaborative exploration, sensing and communication in previously unknown environments with high network latency, such as outer space, battlefields and disaster hit areas are promising in multi-agent applications. When disasters such as large fires or natural disasters occur, previously established networks might be destroyed or incapacitated. In these cases, multiple autonomous mobile robots (AMR) or autonomous unmanned ground vehicles carrying wireless devices and/or thermal sensors can be deployed to create an end-to-end communication and sensing coverage to support rescue efforts or access the severity of damage. However, a fundamental problem is how to rapidly deploy these mobile agents in such complex and dynamic environments. The uncertainties introduced by the operational environment and wide range of scheduling problem have made solving them as a whole challenging. In this paper, we present an efficient decentralized approach for practical mobile agents deployment in unknown, burnt or disaster hit areas. Specifically, we propose an approach that combines methods from Artificial Immune System (AIS) with special token messages passing for a team of interconnected AMR to decide who, when and how to act during deployment process. A distributed scheme is adopted, where each AMR makes its movement decisions based on its local observation and a special token it receives from its neighbors. Empirical evidence of robustness and effectiveness of the proposed approach is demonstrated through simulation.