Shared Workspace Research Articles

Employing Digital Tools for Collaborative and Flexible Mathematical Proving

AbstractThere is a clear disconnect between the linear and deductive presentation of written proofs and the non-linear and messy process of constructing one. This disconnect poses challenges for students who do not often have opportunities, or the necessary supports, to navigate the transition between the two, quite often contributing to feelings of exclusion. It is therefore important to design learning environments with increased epistemic transparency, so that students can flexibly work on mathematical problems while learning and failing together. In this article, I present an instructional design for online collaborative proving activities for secondary school students. I discuss how the use of digital post-it notes and digital shared workspaces can create opportunities for multiple ways of participating for youth, support the navigation of different reasoning patterns, and provide space for extended across-group knowledge sharing. I share a classroom example of how this could look like in practice and discuss main implications of some of the affordances and constraints of the tools used for equitable and respectful student participation in mathematical epistemic practices.

A novel mixed reality remote collaboration system with adaptive generation of instructions

Mixed Reality (MR) remote collaboration supports a shared workspace and various non-verbal communication cues that enable remote experts to express ideas in an immersive Virtual Reality (VR) space to guide local workers in industrial physical tasks by creating Augmented Reality (AR) instructions, such as remote assembly/disassembly guidance, emergency maintenance, and training. However, due to the information non-symmetrical caused by geographical distribution, it can be challenging and high-workload for remote experts to create clear and detailed AR instructions for local workers to match spatial relationships with the local workspace, while comprehensively considering the situation information (e.g., task-related objects and partner’s environment) in complex industrial physical tasks. Advances in ambient intelligence bring ideas for building a new remote collaboration framework that supports the adaptive generation of instructions. In the study, we developed a novel MR remote collaboration prototype system, which can automatically and adaptively generate clear, detailed, and standardized AR instructions based on remote experts’ simple and intuitive interactions and local contextual information to assist remote experts in guiding local workers to complete industrial physical tasks, especially industrial assembly tasks. The effect of two interfaces on industrial assembly tasks involving relatively complex process information and environments was explored in a user study: one was a baseline solution that uses a typical MR remote collaboration interface to support remote experts to create instructions (RECI) using popular non-verbal communication cues in the shared local 3D stereoscopic scene, and the other was our novel interface that additionally supported the adaptive generation of instructions via context awareness (AGICA) on top of the typical interface. The result of the user study showed that AGICA significantly improves collaboration performance, reduces errors and workload, and enhances usability and user experience compared to RECI. This demonstrates that supporting the adaptive generation of instructions is a feasible way to enhance MR remote collaboration in industrial assembly tasks with relatively complex process information and environments. Our research findings have a certain guiding significance for the design of MR remote collaboration systems in such tasks.

Book Review

Understanding Dialogue: Language Use and Social Interaction is written by two prominent psychologists of language. It develops an elegant model to account for the cognitive mechanisms of dialogue. The Shared Workspace Framework for Dialogue is capable of explaining how individuals contribute and control their utterances and how they can manage each other’s contributions and cooperate as one system. I believe this book is worth the attention of all linguists interested in language psychology. First, dialogue is the most fundamental form of language use; second, the theory of dialogue proposed in the book represents a major advancement in the psychology of conversation; third, the theory has a wide range of applications. I believe the current review of the book is the most comprehensive summary and assessment of the book to date.

OcAPO: Fine-grained occupancy-aware, empirically-driven PDC control in open-plan, shared workspaces

Passive Displacement Cooling (PDC) is a relatively recent technology gaining attention as a means of significantly reducing building energy consumption overheads, especially in tropical climates. PDC eliminates the use of mechanical fans, instead using chilled-water heat exchangers to perform convective cooling. In this paper, we identify and characterize the impact of several key parameters affecting occupant comfort in a 1000m2 open-floor area (consisting of multiple zones) of a ZEB (Zero Energy Building) deployed with PDC units and tackle the problem of setting the temperature setpoint of the PDC units to assure occupant thermal comfort and yet conserve energy. We tackle two key practical challenges: (a) the zone-level (i.e., occupant-experienced) temperature differs significantly, depending on occupancy levels, from that measured by the ceiling-mounted thermal sensors that drive the PDC control loop, (b) sparsely deployed sensors are unable to capture the often-significant differences in ambient temperature across neighboring zones. Using extensive real-world coarser-grained measurement data (collected over 60 days under varying occupancy conditions), (a) we first uncover the various parameters that affect the occupant-level ambient temperature, and then (b) devise a trace-based model that helps identify the optimum combination of PDC setpoints, collectively across multiple zones, while accommodating variations in the occupancy levels and weather conditions. Using this trace-based model, our OcAPO system can assure ambient temperature experienced by occupants within a tolerance of 0.3°C. In contrast, the existing approach of occupancy-agnostic, rule-based setpoint control violates this tolerance interval more than 80% of the time. However, this initial model requires unnecessary and continual database lookups and is unable to derive finer-grained setpoints, thereby potentially missing opportunities for additional energy savings. We thus collected data for another 15 days, with finer-grained setpoint control in increments of 0.2∘ under varying occupancy conditions in the second phase. To determine PDC setpoints efficiently, we subsequently used the empirical data to train a KNN-based regression model. Additional studies on our real-world testbed demonstrate the regressor-based OcAPO approach is able to assure occupant-level ambient temperature within a narrow 0.2°C tolerance. We also demonstrate that the regression version of OcAPO can reduce the opening percentage of PDC valves (an indirect proxy for energy consumption) by 58.9% under low occupancy compared to the trace-based model.

Interaction between a Human and an AGV System in a Shared Workspace—A Literature Review Identifying Research Areas

Background: This article presents the results of a literature review from 2018 to 2023, which focused on research related to human and AGV system cooperation in a shared workspace. This study defines AGV systems as systems using Automated Guided Vehicles or Autonomous Guided Vehicles. An Automated Guided Vehicle is a cart that follows a guided path, while an Autonomous Guided Vehicle is an Automated Guided Vehicle that is autonomously controlled. The analyses conducted answered two research questions: (RQ1) In what aspects are the human factor examined in publications on the implementation and operation of AGV systems? (RQ2) Has the human-AGV collaboration aspect been analyzed in the context of a sustainable work environment? Methods: The literature review was conducted following the systematic literature review method, using the PRISMA approach. Results: Based on the search of two journal databases, according to the indicated keywords, 1219 documents pertaining to the analyzed issues were identified. The selection and elimination of documents that did not meet the defined criteria made it possible to limit the number of publications to 117 articles and proceedings papers. On this basis, the authors defined a classification framework comprising five basic research categories and nine subcategories. The analyzed documents were classified, and each distinguished group was characterized by describing the results. Conclusions: The development of a two-level classification framework for research from the analyzed area according to the assumptions of the concept map and the identification of research gaps in the area of human-AGV interaction.

Shared workspace design: elements of analysis for a healthy work experience

Purpose This paper aims to propose and provide an overview of a model analysis that considers the main spatial design attributes that influence and produce the most relevant salutogenic outcomes. These results are essential for a healthy work experience, especially in shared workspaces. Design/methodology/approach This study departs from the theoretical contributions of the salutogenic approach, principles from supportive design theory, psychosocial supportive design and the environmental demands and resources model. After a scoping literature review covering different fields of workspace design, environmental psychology and evidence-based design of health-care facilities, a conceptual analysis is done on a proposed understanding of work, health and environmental relations to overview spatial attributes that enhance specific salutogenic and well-being-promoting outcomes needed for a healthy work experience. Findings The model of analysis, as a theoretical element that helps create methodological tools, combined with the application of a post occupancy evaluation, is thought to assist architects, designers, workspace owners and stakeholders in their new designs or to evaluate existing ones. Originality/value Studies on defining spatial attributes and their intended salutogenic outcomes have been formally done in health-care facilities. However, applying this idea to shared workspaces is something new and is expected to contribute to their design and evaluation, especially if the notion of environmental demands and resources is complemented.

Online and Scalable Motion Coordination for Multiple Robot Manipulators in Shared Workspaces

Multi-robot motion coordination is essential to make robots work safely and efficiently in a shared workspace, ensuring task completion while avoiding interference between each other’s motions. Achieving online replanning and scaling to large numbers of robots are particularly challenging. In this paper, we present a motion coordination method for multiple robot manipulators with given targets. The approach separates the problem into a global coordination stage and a local trajectory replanning stage. Online reactivity and scalability are achieved by means of coordination in a reduced space and efficient trajectory replanning. We first introduce the method for systems with two robots, then extend it to systems with an arbitrary number of robots. The method determines whether kinematically-feasible and non-interfering trajectories leading all the robots to their targets exist. The approach generates time-efficient trajectories if solutions exist, or provides information for switching targets if solutions cannot be found. We show formally and empirically that the method has low computational overhead and scales quadratically with the number of robots. Experiments are conducted with up to three real 7-DOF robots and up to ten simulated robots. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In underground mining, a key process is that of tunneling, i.e., drilling and blasting rock to excavate tunnels that lead to sources of ore. Drilling is carried out by rigs equipped with multiple robotic arms. A key factor affecting the efficiency of tunneling is the time to completion of drilling operations. In current industrial practice, these operations are carried out manually by an operator steering the arms on the drill rig. Time to completion can be drastically reduced if the arms could operate concurrently, intelligently optimizing and coordinating their motions. However, existing methods for multi-arm motion coordination are inadequate, as they fail to cater to one or more of the following real-world constraints: several robot arms work in close proximity and their workspaces are overlapping; task completion times are uncertain due to rock density and drill bit breakage; and contingencies such as unexpected delays in motion or stops sometimes happen. These constraints exist also in other industrial applications, like manufacturing and assembly. This paper proposes a framework which enables multiple high-DOF robot manipulators to safely and efficiently work in a shared workspace. The method allows to adjust robot trajectories while robots move, and is shown to scale well with the number of robots. Motions are generated and adjusted by time-optimal trajectory planning, whose computational overhead is small enough for online operation and benefits task completion efficiency, safety, and productivity. Experiments using three 7-DOF robots suggest that this approach is practically feasible, and simulations with up to 10 robots attest to its scalability.

Semi-Supervised Multimodal Representation Learning Through a Global Workspace.

Recent deep learning models can efficiently combine inputs from different modalities (e.g., images and text) and learn to align their latent representations or to translate signals from one domain to another (as in image captioning or text-to-image generation). However, current approaches mainly rely on brute-force supervised training over large multimodal datasets. In contrast, humans (and other animals) can learn useful multimodal representations from only sparse experience with matched cross-modal data. Here, we evaluate the capabilities of a neural network architecture inspired by the cognitive notion of a "global workspace" (GW): a shared representation for two (or more) input modalities. Each modality is processed by a specialized system (pretrained on unimodal data and subsequently frozen). The corresponding latent representations are then encoded to and decoded from a single shared workspace. Importantly, this architecture is amenable to self-supervised training via cycle-consistency: encoding-decoding sequences should approximate the identity function. For various pairings of vision-language modalities and across two datasets of varying complexity, we show that such an architecture can be trained to align and translate between two modalities with very little need for matched data (from four to seven times less than a fully supervised approach). The GW representation can be used advantageously for downstream classification and cross-modal retrieval tasks and for robust transfer learning. Ablation studies reveal that both the shared workspace and the self-supervised cycle-consistency training are critical to the system's performance.

Behavioral patterns in robotic collaborative assembly: comparing neurotypical and Autism Spectrum Disorder participants

IntroductionIn Industry 4.0, collaborative tasks often involve operators working with collaborative robots (cobots) in shared workspaces. Many aspects of the operator's well-being within this environment still need in-depth research. Moreover, these aspects are expected to differ between neurotypical (NT) and Autism Spectrum Disorder (ASD) operators.MethodsThis study examines behavioral patterns in 16 participants (eight neurotypical, eight with high-functioning ASD) during an assembly task in an industry-like lab-based robotic collaborative cell, enabling the detection of potential risks to their well-being during industrial human-robot collaboration. Each participant worked on the task for five consecutive days, 3.5 h per day. During these sessions, six video clips of 10 min each were recorded for each participant. The videos were used to extract quantitative behavioral data using the NOVA annotation tool and analyzed qualitatively using an ad-hoc observational grid. Also, during the work sessions, the researchers took unstructured notes of the observed behaviors that were analyzed qualitatively.ResultsThe two groups differ mainly regarding behavior (e.g., prioritizing the robot partner, gaze patterns, facial expressions, multi-tasking, and personal space), adaptation to the task over time, and the resulting overall performance.DiscussionThis result confirms that NT and ASD participants in a collaborative shared workspace have different needs and that the working experience should be tailored depending on the end-user's characteristics. The findings of this study represent a starting point for further efforts to promote well-being in the workplace. To the best of our knowledge, this is the first work comparing NT and ASD participants in a collaborative industrial scenario.

Public-led shared workspaces and the intangible factors of urban regeneration in UK coastal towns

ABSTRACT Recent studies suggest that policies designed to create new shared workspaces have the power to deliver skills, innovation and new employment opportunities to peripheral areas and lagging economies. We examine this idea using case studies from two British coastal locations and industrial ports of different sizes and governance structures: Portsmouth (Hampshire) and Newhaven (East Sussex). Our findings indicate the need for integrated place-based solutions that are targeted and anchored in the local needs and identities of the place. Governmental frameworks should not only address the physical aspects of regeneration but also the social and institutional ones. Positive externalities are registered when shared workspaces act as social infrastructure. For this to happen, regeneration policy needs to work on community building, regaining local trust and nurturing positive local identities and imaginaries. Finally, we note that fragmented institutional patterns hamper the scaling up of positive externalities at the wider regional scale.

Designing a video consultation area for hybrid care delivery: the Garden Room with a view.

Accelerated by the coronavirus pandemic, the healthcare landscape is rapidly evolving, with a shift towards hybrid care models combining in-person and online care. To support this shift, the Radboudumc, an academic hospital in the Netherlands, decided to redesign an existing space facilitating the conduction of video consultations. The design process involved participation of end-users to ensure that the physical space met their needs. The look and feel of the area was based on evidence-based design guidelines. Two prototype setups were built and tested, and the feedback informed the final design of the Garden Room. Identified end-user needs were divided into 3 major categories entailing consultation room setup, optimal use of technology and practical issues involving room availability. Combined with the look and feel of the hospital, final design requirements were developed. The Garden Room consists of 18 video consultation rooms, 4 shared workspaces, relaxation area with kitchen, and meeting rooms. Specific attention is given to the ergonomics, technology and privacy in the rooms to facilitate optimal video conversations between patients and healthcare providers. In the Garden Room, natural elements and an open design supports working in a healing environment. Next challenge will be optimizing the use of the Garden Room, which may be hindered by various barriers like resistance to change, existing work processes, and lack of skills training. To address these barriers and support use of the Garden Room, the hospital should focus on the implementation of education, changes in work processes, and the presence of advocates for telehealth.

Social Interventions to Encourage Co-Located Collaborative Learning in a Tangible User Interface

Abstract Technologies that support co-located group activity should not only provide a shared workspace, but they should also encourage collaboration. For this, it is necessary to know the structure of the group and how the members of the group work in order to achieve a common goal. In this paper, an observational study of groups of students interacting in a tangible user interface was performed. This study allowed us to identify social interactions and phenomena that can affect the collaboration and performance of the group. These phenomena can be for instance that some members of the group may contribute to the activity, while others may not. In order to minimize these phenomena in a co-located group activity, we propose interactions, which we call social interventions, that might be used by a coaching system to encourage group collaboration in co-located settings.

Making sustainability transitions in collaborative spaces of making: Exploring opportunities and limitations in Turin

In recent years, makerspaces and FabLabs have proliferated in urban settings. Such venues provide shared workspace, (digital fabrication) tools, and knowledge, lowering the barriers for citizens to engage in DIY (Do It Yourself), and the design and building of artefacts. In recent years, scholars have hailed the return of small-scale manufacturing, independent craft production and making practices to cities. This shift is by some seen as pre-figuring a more progressive and inclusive discourse on urban development, one that is attentive to local communities’ productive needs and opportunities, and that moves us away from the hitherto dominant consumption-centred paradigm of the creative city (Carr & Gibson, 2016; Grodach, 2017; Grodach et al., 2017). Drawing on empirical data collected in Turin in 2021, the paper investigates how and under what conditions community-led spaces centred on making may hold potential as sites of urban vitalism, reconfiguring production and consumption in more democratic, inclusive and sustainable ways. The findings consider a number of emerging tensions, relating to questions of regulating open access, managing community participation, and negotiating project-based versus changing habitual practices.

Autonomous pre-conditioning and improved personalization in shared workspaces through data-driven predictive control

This paper studies the problem of indoor zone temperature control in shared workspaces equipped with heterogeneous heating and cooling sources with the goal of increased energy savings and environment personalization. Shared workspaces typically witness distinct, pre-scheduled intervals when they are occupied or are unoccupied. In this work, we develop indoor climate control strategies for each of these intervals. For the interval when the workspace is unoccupied, we propose multiple time-bound control strategies for pre-conditioning the workspace in preparation for a scheduled activity (Phase I). For the interval when the workspace is occupied, we propose a separate control strategy which enhances the thermal comfort of the occupants by harnessing the spatial differentiation of the thermal environment to satisfy the different temperature preferences of the individuals (Phase II). Utilizing a physical test-bed and data-driven model learning, we show that our proposed pre-conditioning strategies in Phase I are less computationally expensive than conventional model predictive control (MPC). For Phase II, we use a low complexity quadratic program to minimize the thermal discomfort experienced by individuals based on their temperature preferences. The experimental results show that for Phase I, the proposed control policies can save a significant amount of energy and achieve the desired mean temperature in the space fairly accurately. We further note that for Phase II, the control scheme can achieve a significant spatial differentiation in temperature towards satisfying the occupants’ thermal preferences.

A conceptual human safety system in an industrial shared workspace with a collaborative robot

A conceptual human safety system in an industrial shared workspace with a collaborative robot

Camera Arrangement Optimization for Workspace Monitoring in Human-Robot Collaboration.

Human-robot interaction is becoming an integral part of practice. There is a greater emphasis on safety in workplaces where a robot may bump into a worker. In practice, there are solutions that control the robot based on the potential energy in a collision or a robot re-planning the straight-line trajectory. However, a sensor system must be designed to detect obstacles across the human-robot shared workspace. So far, there is no procedure that engineers can follow in practice to deploy sensors ideally. We come up with the idea of classifying the space as an importance index, which determines what part of the workspace sensors should sense to ensure ideal obstacle sensing. Then, the ideal camera positions can be automatically found according to this classified map. Based on the experiment, the coverage of the important volume by the calculated camera position in the workspace was found to be on average 37% greater compared to a camera placed intuitively by test subjects. Using two cameras at the workplace, the calculated positions were 27% more effective than the subjects' camera positions. Furthermore, for three cameras, the calculated positions were 13% better than the subjects' camera positions, with a total coverage of more than 99% of the classified map.

Re-locations: Augmenting Personal and Shared Workspaces to Support Remote Collaboration in Incongruent Spaces

Augmented reality (AR) can create the illusion of being virtually co-located during remote collaboration, e.g., by visualizing remote co-workers as avatars. However, spatial awareness of each other's activities is limited as physical spaces, including the position of physical devices, are often incongruent. Therefore, alignment methods are needed to support activities on physical devices. In this paper, we present the concept of Re-locations, a method for enabling remote collaboration with augmented reality in incongruent spaces. The idea of the concept is to enrich remote collaboration activities on multiple physical devices with attributes of co-located collaboration such as spatial awareness and spatial referencing by locally relocating remote user representations to user-defined workspaces. We evaluated the Re-locations concept in an explorative user study with dyads using an authentic, collaborative task. Our findings indicate that Re-locations introduce attributes of co-located collaboration like spatial awareness and social presence. Based on our findings, we provide implications for future research and design of remote collaboration systems using AR.

Multiple Mobile Robots Coordination in Shared Workspace for Task Makespan Minimization

In this paper we consider a system of multiple mobile robots (MMRS) and the process of their concurrent motion in a shared two-dimensional workspace. The goal is to plan the robot movement along given fixed paths so as to minimize the completion time of all the robots while ensuring that they never collide. Thus, the considered problem combines the problems of robot schedule optimization with collision and deadlock avoidance. The problem formulation is presented and its equivalent reformulation that does not depend explicitly on the geometry of the robot paths is proposed. An event-based solution representation is proposed, allowing for a discrete optimization approach. Two types of possible deadlocks are identified and deadlock avoidance procedures are discussed. We proposed two types of solving methods. First, we implemented two metaheuristics: the local-search-based taboo search as well as the population-based artificial bee colony. Next, we implemented 14 simple constructive algorithms, employing dispatch rules such as first-in first-out, shortest distance remaining first, and longest distance remaining first, among others. A set of problem instances for different numbers of robots is created and provided as a benchmark. The effectiveness of the solving methods is then evaluated by simulation using the generated instances. Both deterministic and lognormal-distributed uncertain robot travel times are considered. The results prove that the taboo search metaheuristic obtained the best results for both deterministic and uncertain cases, with only artificial bee colony and a few constructive algorithms managing to remain competitive. Detailed results as well as ideas to further improve proposed methods are discussed.

Human Arm Motion Prediction for Collision Avoidance in a Shared Workspace

Industry 4.0 transforms classical industrial systems into more human-centric and digitized systems. Close human–robot collaboration is becoming more frequent, which means security and efficiency issues need to be carefully considered. In this paper, we propose to equip robots with exteroceptive sensors and online motion generation so that the robot is able to perceive and predict human trajectories and react to the motion of the human in order to reduce the occurrence of the collisions. The dataset for training is generated in a real environment in which a human and a robot are sharing their workspace. An Encoder–Decoder based network is proposed to predict the human hand trajectories. A Model Predictive Control (MPC) framework is also proposed, which is able to plan a collision-free trajectory in the shared workspace based on this human motion prediction. The proposed framework is validated in a real environment that ensures collision free collaboration between humans and robots in a shared workspace.

Planning Collision-Free Robot Motions in a Human–Robot Shared Workspace via Mixed Reality and Sensor-Fusion Skeleton Tracking

The paper describes a method for planning collision-free motions of an industrial manipulator that shares the workspace with human operators during a human–robot collaborative application with strict safety requirements. The proposed workflow exploits the advantages of mixed reality to insert real entities into a virtual scene, wherein the robot control command is computed and validated by simulating robot motions without risks for the human. The proposed motion planner relies on a sensor-fusion algorithm that improves the 3D perception of the humans inside the robot workspace. Such an algorithm merges the estimations of the pose of the human bones reconstructed by means of a pointcloud-based skeleton tracking algorithm with the orientation data acquired from wearable inertial measurement units (IMUs) supposed to be fixed to the human bones. The algorithm provides a final reconstruction of the position and of the orientation of the human bones that can be used to include the human in the virtual simulation of the robotic workcell. A dynamic motion-planning algorithm can be processed within such a mixed-reality environment, allowing the computation of a collision-free joint velocity command for the real robot.