Autonomous Service Robots Research Articles

Understanding Reactions in Human‐Robot Encounters with Autonomous Quadruped Robots

ABSTRACTIncidental human‐robot encounters are becoming more common as robotic technologies proliferate, but there is little scientific understanding of human experience and reactions during these encounters. To contribute towards addressing this gap, this study applies Grounded Theory methodologies to study human reactions in Human‐Robot Encounters with an autonomous quadruped robot. Based upon observation and interviews, we find that participants' reactions to the robot can be explained by their attitudes of familiarity, certainty, and confidence during their encounter and by their understanding of the robot's capabilities and role. Participants differed in how and whether they utilized opportunities to resolve their unfamiliarity, uncertainty, or lack of confidence, shedding light on the dynamics and experiential characteristics of Human‐Robot Encounters. We provide an emerging theory that can be used to unravel the complexity of the field as well as assist hypothesis generation in future research in designing and deploying mobile autonomous service robots.

Consumers’ Ethical Perceptions of Autonomous Service Robots in Hotels

This study empirically and comprehensively explores consumers’ ethical perceptions of autonomous service robots (ASRs) in hotels. Under the triangulation approach, this study has identified eight themes of consumer perceived ethical issues (privacy, security, safety, transparency, fairness, socialization, autonomy, and responsibility). Each theme can be explained from two dimensions: ethical issues arise during the interaction (i.e., ubiquitous surveillance, excessive data, unidentified risks, service disclosure, inaccessibility, dehumanization, selection of services, and service recovery), and ethical issues can be raised by the characteristics of ASRs (i.e., privacy infringement, malicious use, malfunctions, untrustworthiness, biased features, job replacement, inflexibility, and self-identified solutions). This study is the first to propose ethical issues of ASRs from two dimensions with different intelligence levels, and to highlight ethical issues during hotel service interactions. The findings contribute to ethics studies of service robots from consumers’ perspectives and offer managerial insights to reduce ethical concerns and enhance ASRs usage in hotels.

Anthropomorphic Robot Factors and Shoppers’ Acceptance of Autonomous Service Robots in Shopping Mall Complexes in Lagos State, Nigeria

This study examines the influence of anthropomorphic robot factors on shoppers’ acceptance of autonomous service robots in shopping mall complexes. It also contributes to the Service Robot Acceptance Model from the context of a developing country by validating three robot factors such as appearance, social capability and usefulness from shoppers’ perspectives.

Long-Term Localization Using Semantic Cues in Floor Plan Maps

Lifelong localization in a given map is an essential capability for autonomous service robots. In this letter, we consider the task of long-term localization in a changing indoor environment given sparse CAD floor plans. The commonly used pre-built maps from the robot sensors may increase the cost and time of deployment. Furthermore, their detailed nature requires that they are updated when significant changes occur. We address the difficulty of localization when the correspondence between the map and the observations is low due to the sparsity of the CAD map and the changing environment. To overcome both challenges, we propose to exploit semantic cues that are commonly present in human-oriented spaces. These semantic cues can be detected using RGB cameras by utilizing object detection, and are matched against an easy-to-update, abstract semantic map. The semantic information is integrated into a Monte Carlo localization framework using a particle filter that operates on 2D LiDAR scans and camera data. We provide a long-term localization solution and a semantic map format, for environments that undergo changes to their interior structure and detailed geometric maps are not available. We evaluate our localization framework on multiple challenging indoor scenarios in an office environment, taken weeks apart. The experiments suggest that our approach is robust to structural changes and can run on an onboard computer. We released the open source implementation of our approach written in C++ together with a ROS wrapper.

Study protocol for the implementation and evaluation of a digital-robotic-based intervention for nurses and patients in a hospital: a quantitative and qualitative triangulation based on the Medical Research Council (MRC) framework for developing and evaluating complex interventions.

Nurses spend part of their working time on non-nursing tasks. Unnecessary walking distances and the assumption of service activities and other non-care-related tasks take up a lot of space, which reduces the time for direct patient care and demonstrably increases the dissatisfaction of the persons involved. The REsPonSe project aims to relieve nursing staff by using a smartphone app for communication in combination with an autonomous service robot to reduce walking distances and service activities. The technical systems are tested on a nuclear medicine ward and are intended to reduce the radiation exposure of the staff. The aim of this study is to test and evaluate the use and intervention of the technical systems, the acceptance of the users and the change in the utilisation of the nursing service. In addition to findings on usability and manageability, effects on nursing practice, as well as facilitating and inhibiting contextual factors for implementation, will be identified. The Medical Research Council (MRC) Framework for Developing and Evaluating for Complex Interventions was chosen as the theoretical basis. The data collection in the Feasibility and Evaluation phase is a triangulation of quantitative and qualitative methods. Standardised observations are planned to collect data on non-care activities and walking distances, and a survey of utilisation by use of a questionnaire based on the NASA TLX. Qualitative individual interviews with patients and group discussions with nursing staff will be conducted. Statements on the subjective experiences, as well as the evaluation of the use of the digital-robotic system in the clinical setting, will be collected. The descriptive evaluation of the usage and retrieval data will provide information on duration, time, requests, and reduced contact times, as well as error and fault messages. The evaluation study will make it possible to represent a variety of perspectives from different interest groups. The results should contribute to the definition of implementation and evaluation criteria and facilitate the integration of digital-robotic assistance systems in nursing acute inpatient settings. The trial was registered with the German Clinical Trials Register (DRKS) on 16.02.2022: DRKS00028127.

A Modular and Portable Black Box Recorder for Increased Transparency of Autonomous Service Robots

Autonomous service robots have great potential to support humans in tasks they cannot perform due to, amongst others, time constraints, work overload, or staff shortages. An important step for such service robots to be trusted or accepted by society is the provision of transparency. Its purpose is not only to communicate what a robot is doing according to the human interaction partners' needs, it should also regard social and legal requirements. A black box recorder (inspired by flight recorders) increases the system's transparency by facilitating the investigation of the cause of an incident, clarifying responsibilities, or improving the user's understanding about the robot. In this work we propose the needed requirements of such a black box recorder for increased transparency of autonomous service robots, based on the related work. Further, we present a new modular and portable black box recorder design meeting these requirements. The applicability of the system is evaluated based on real-world robot data, using the realized open-source reference implementation.

Autonomous Service Robots for Urban Waste Management - Multiagent Route Planning and Cooperative Operation

A sustainable approach towards smart waste management means reduced impact on the health of the workers, lower greenhouse gas emissions and low operational costs. Incorporating fleets of the robot MARBLE (Mobile Autonomous RoBot for Litter Emptying) effectuates these requirements for autonomously emptying the dustbins on the streets of Berlin. The dedicated waypoints for the route formulation are the global positions of the dustbins, and the garbage emptying position from the robots after reaching the maximum compressed-garbage storage capacity. In this paper, we provide a cost function efficient solution for providing global routes to the robots. The cost function includes operational energy expenditure. After conducting numerous experiments with varying algorithm parameter values, the specific weightage of route permutation operators and the initial temperature for a simulated annealing method with nearest neighbour approach was narrowed down as a well-performing set. This set was used in three different operation scenarios. The results of the cost function on the basis of the chosen parameters show an improvement of average 28% for two different exemplary datasets, and 30% improvement for an implementation in an actual existing park in Berlin for operation scenarios as compared to results from a nearest neighbour heuristic.

Detection of Household Furniture Storage Space in Depth Images.

Autonomous service robots assisting in homes and institutions should be able to store and retrieve items in household furniture. This paper presents a neural network-based computer vision method for detection of storage space within storage furniture. The method consists of automatic storage volume detection and annotation within 3D models of furniture, and automatic generation of a large number of depth images of storage furniture with assigned bounding boxes representing the storage space above the furniture shelves. These scenes are used for the training of a neural network. The proposed method enables storage space detection in depth images acquired by a real 3D camera. Depth images with annotations of storage space bounding boxes are also a contribution of this paper and are available for further research. The proposed approach represents a novel research topic, and the results show that it is possible to facilitate a network originally developed for object detection to detect empty or cluttered storage volumes.

Autonomous service robot for human-aware restock, straightening and disposal tasks in retail automation

The workforce shortage in the service industry, recently highlighted by the pandemic, has increased the need for automation. We propose an autonomous robot to fulfill this purpose. Our mobile manipulator includes an extendable and compliant end effector design, as well as a custom-made automated shelf, and it is capable of manipulating food products such as lunch boxes, while traversing narrow spaces and reacting to human interventions. We benchmarked the solution in the international robotics competition Future Convenience Store Challenge (FCSC) where we obtained the first place in the 2020 edition, as well as in a laboratory setting, both situated in a convenience store scenario. We reported the results evaluated in terms of the score of the FCSC 2020 and further discussed the real-world applicability of the current system and open challenges.

Findings From A Qualitative Field Study with An Autonomous Robot in Public: Exploration of User Reactions and Conflicts

Soon service robots will be employed in public spaces with frequent human-robot interaction (HRI). To achieve a safe, trustworthy and acceptable HRI, service robots need to be equipped with interaction strategies suitable for the robot, user, and context. To gain realistic insights into the initial user reactions and challenges that arise when a mechanoid, autonomous service robot in public is applied, a field study with three data sources was conducted. In a first step, lay users’ intuitive reactions to a cleaning robot at a train station were observed (N = 344). Second, passersby’s preferences for HRI interaction strategies were explored in interviews (n = 54). As a third step, trust and acceptance of the robot were assessed with questionnaires (n = 32). Identified challenges were social robot navigation in crowded places also applicable to vulnerable passersby, inclusive communication modalities, information of staff and public about the service robot application and the need for conflict resolution strategies to avoid an inefficient robot (e.g., testing behavior, path is blocked). This study provides insights into naive HRI in public and illustrates challenges, provides recommendations supported by literature and highlights aspects for future research to inspire a research agenda in the field of public HRI.

Design and implementation of an autonomous service robot based on cyber physical modeling systems

An autonomous robot is designed and implemented to support the service works. Many technologies were adapted, namely human-robot interaction order system, path planning and mapping in indoor, Robot Operating System (ROS), and Cyber Physical System (CPS). Many subsystems are developed and constructed based on ROS to implement a human-interaction action in completing service robot tasks. A human-robot interaction order system is designed for the convenient human servicing purpose. Therefore, the customer quickly finds the commodity information through the internet. This system recommends the commodity according to the content of the database. In addition, the customer makes an order by touch screen interface, and then it sends a command to control the robot to deliver the commodity. In the second subsystem, it includes path planning and mapping generation. The well-known simultaneous localization and mapping (SLAM) concept with a Rao-Blackwellized particle filter (RBPF) addresses the appropriated maps after completing the exploration of the indoor environment. In order to reach better robustness and agility, the hybrid path planning algorithm includes a global A* algorithm and local dynamic window approach (DWA) to navigation. In the third subsystem, the Gazebo makes the perfect construction of CPS design. Finally, the simulation and implementation of dual-arm robot navigation with the interactive order situation to efficiently support servicing actions of the robot and finish the required tasks.

Detecting Groups and Estimating F-Formations for Social Human–Robot Interactions

The ability of a robot to detect and join groups of people is of increasing importance in social contexts, and for the collaboration between teams of humans and robots. In this paper, we propose a framework, autonomous group interactions for robots (AGIR), that endows a robot with the ability to detect such groups while following the principles of F-formations. Using on-board sensors, this method accounts for a wide spectrum of different robot systems, ranging from autonomous service robots to telepresence robots. The presented framework detects individuals, estimates their position and orientation, detects groups, determines their F-formations, and is able to suggest a position for the robot to enter the social group. For evaluation, two simulation scenes were developed based on the standard real-world datasets. The 1st scene is built with 20 virtual agents (VAs) interacting in 7 different groups of varying sizes and 3 different formations. The 2nd scene is built with 36 VAs, positioned in 13 different groups of varying sizes and 6 different formations. A model of a Pepper robot is used in both simulated scenes in randomly generated different positions. The ability for the robot to estimate orientation, detect groups, and estimate F-formations at various locations is used to determine the validation of the approaches. The obtained results show a high accuracy within each of the simulated scenarios and demonstrates that the framework is able to work from an egocentric view with a robot in real time.

Creating Semi-supervised learning-based Adaptable Object Detection Models for Autonomous Service Robot

Creating Semi-supervised learning-based Adaptable Object Detection Models for Autonomous Service Robot

Towards Contactless Learning Activities during Pandemics Using Autonomous Service Robots

The COVID-19 pandemic has had a significant impact worldwide, impacting schools, undergraduate, and graduate university education. More than half a million lives have been lost due to COVID-19. Moving towards contactless learning activities has become a research area due to the rapid advancement of technology, particularly in artificial intelligence and robotics. This paper proposes an autonomous service robot for handling multiple teaching assistant duties in the educational field to move towards contactless learning activities during pandemics. We use SLAM to map and navigate the environment to proctor an exam. We also propose a human–robot voice interaction and an academic content personalization algorithm. Our results show that our robot can navigate the environment to proctor students avoiding any static or dynamic obstacles. Our cheating detection system obtained a testing accuracy of 86.85%. Our image-based exam paper scanning system can scan, extract, and process exams with high accuracy.

Development of a tour guide and co-experience robot system using the quasi-zenith satellite system and the 5th-generation mobile communication system at a Theme Park

Autonomous robots are expected to replace dangerous, dirty, and demanding (3D) jobs. At a theme park, surveillance, cleaning, and guiding tasks can be regarded as 3D jobs. The present paper attempts to develop an autonomous tour guide robot system and co-experience system at a large theme park. For realizing such autonomous service robots used in our daily environment, localization is one of the most important and fundamental functions. A number of localization techniques, including simultaneous localization and mapping, have been proposed. Although a global navigation satellite system (GNSS) is most commonly used in outdoor environments, its accuracy is approximately 10 m, which is inadequate for navigation of an autonomous service robot. Therefore, a GNSS is usually used together with other localization techniques, such as map matching or camera-based localization. In the present study, we adopt the quasi-zenith satellite system (QZSS), which became available in and around Japan in November 2018, for the localization of an autonomous service robot. The QZSS provides high-accuracy position information using quasi-zenith satellites (QZSs) and has a localization error of less than 10 centimeters. In the present paper, we compare the positioning performance of the QZSS and the real-time kinematic GPS and verify the stability and the accuracy of the QZSS in an outdoor environment. In addition, we introduce a tour guide robot system using the QZSS and present the results of a guided tour experiment in a theme park. On the other hand, based on the tour guide system, we also develop a co-experience robot system in a theme park, which realizes the sharing of experiences using an immersive VR display and the 5th-generation mobile communication system (5G). The robot is equipped with a 360-degree video camera and real-time 4K video is transmitted to a remote operator using the large communication capacity of the 5G network. The experimental results at a theme park showed that the guided tour experiment was successful and that the co-experience system allowed sharing of the experience with high immersion by a remote operator.

ILLUSTRATING THE PERCEPTION OF STUDENTS TOWARDS AUTONOMOUS SERVICE ROBOTS IN THE TOURISM INDUSTRY: AN EXPLORATORY STUDY

Research purpose – The purpose of the study was to explore the students’ perceptions towards autonomous service robots, using interviews and, in a novel manner, also their drawings, to generate data. The paper contributes to current knowledge claims around the Techno-Economic Paradigm by providing a greater understanding of, and student response to, autonomous service robots. Additional insights were gained on transformational skills set for tourism practitioners. Design/Methodology/Approach – A qualitative approach followed a participatory research design which was set in a public higher education institution. Undergraduate tourism and hospitality students who had previous exposure and engagement with robots were the purposive sample. The paper draws on the Techno-Economic Paradigm to illuminate how, according to students’ experiences and perceptions, autonomous robots interact in, and disrupt, the tourism industry. Findings – Four main drawings essentially expressed tourism and hospitality students’ perceptions of autonomous service robots in the tourism industry. Centrally there are opportunities for the absorption of robots in certain sectors of the tourism industry. Despite greater use of robots in service-driven industries like tourism, it remains challenging to establish the right balance between humans and robots, and up- and re-skilling transformation would be required of those working in the tourism industry and those studying towards a tourism qualification. This study advances that additional research is still required, including longitudinal studies on the effects of autonomous services in the tourism industry, as well as students’ perception on the use of robots, re-skilling as well as ethical risks to customers, the greater value to the economy and those working in the tourism industry. Originality of the research – The article contributes to the use of visual methodology as part of data generation, specifically how students’ perceptions regarding autonomous robots in the tourism industry were graphically distilled using this methodology.

Autonomous Floor and Staircase Cleaning Framework by Reconfigurable sTetro Robot with Perception Sensors

Cleaning multi-storey buildings need to be considered while developing autonomous service robots. In this paper, we introduce a novel reconfigurable platform called sTetro with the abilitiesto navigate on the floor as well as to detect then climb the staircase autonomously. To this end, an operational framework for this cleaning robot that leverages on customized deep convolution neural network (DCNN) and the RGBD camera to locate staircases in the 3D prebuilt map and then to plan trajectories by maximizing area coverage for both floor and staircase in the multi-storey environments is proposed. While building a 3D map, the staircase location is identified at the 3D point close to the center of the staircase first step using a contour detection algorithm from the boundary of the detected staircase by DCNN. The robot follows the planned trajectory to clear the floor then approaching the staircase location accurately to execute the climbing mode while cleaning the staircase to reach the next floor. The proposed methods archive the high accuracy in identifying the presence of the different staircase types, and the first step locations. Moreover, the multi-storey building evaluations have demonstrated the efficiency of the sTetro in terms of the area coverage both staircase and floor free space.

Improving human–robot interactions in hospitality settings

PurposeService robotics, a branch of robotics that entails the development of robots able to assist humans in their environment, is of growing interest in the hospitality industry. Designing effective autonomous service robots, however, requires an understanding of Human–Robot Interaction (HRI), a relatively young discipline dedicated to understanding, designing, and evaluating robotic systems for use by or with humans. HRI has not yet received sufficient attention in hospitality robotic design, much like Human–Computer Interaction (HCI) in property management system design in the 1980s. This article proposes a set of introductory HRI guidelines with implementation standards for autonomous hospitality service robots.Design/methodology/approachA set of key user-centered HRI guidelines for hospitality service robots were extracted from 52 research articles. These are organized into service performance categories to provide more context for their application in hospitality settings.FindingsBased on an extensive literature review, this article presents some HRI guidelines that may drive higher levels of acceptance of service robots in customer-facing situations. Deriving meaningful HRI guidelines requires an understanding of how customers evaluate service interactions with humans in hospitality settings and to what degree those will differ with service robots.Originality/valueRobots are challenging assumptions on how hospitality businesses operate. They are being increasingly deployed by hotels and restaurants to boost productivity and maintain service levels. Effective HRI guidelines incorporate user requirements and expectations in the design specifications. Compilation of such information for designers of hospitality service robots will offer a clearer roadmap for them to follow.

Cleaning Tasks Knowledge Transfer Between Heterogeneous Robots: a Deep Learning Approach

Nowadays, autonomous service robots are becoming an important topic in robotic research. Differently from typical industrial scenarios, with highly controlled environments, service robots must show an additional robustness to task perturbations and changes in the characteristics of their sensory feedback. In this paper, a robot is taught to perform two different cleaning tasks over a table, using a learning from demonstration paradigm. However, differently from other approaches, a convolutional neural network is used to generalize the demonstrations to different, not yet seen dirt or stain patterns on the same table using only visual feedback, and to perform cleaning movements accordingly. Robustness to robot posture and illumination changes is achieved using data augmentation techniques and camera images transformation. This robustness allows the transfer of knowledge regarding execution of cleaning tasks between heterogeneous robots operating in different environmental settings. To demonstrate the viability of the proposed approach, a network trained in Lisbon to perform cleaning tasks, using the iCub robot, is successfully employed by the DoRo robot in Peccioli, Italy.

Continuous perception for deformable objects understanding

We present a robot vision approach to deformable object classification, with direct application to autonomous service robots. Our approach is based on the assumption that continuous perception provides robots with greater visual competence for deformable objects interpretation and classification. Our approach classifies the category of clothing items by continuously perceiving the dynamic interactions of the garment’s material and shape as it is being picked up. For this, we extract continuously visual features of a RGB-D video sequence and we fuse features by means of the Locality Constrained Group Sparse Representation (LGSR) algorithm. To evaluate the performance of our approach, we created a fully annotated database featuring 150 garment videos in random configurations. Experiments demonstrate that by continuously observing an object deform, our approach achieves a classification score of 66.7%, outperforming state-of-the-art approaches by a ∼27.3% increase.