Mohamed Ez-Zaouia
email: mohamed.ez-zaouia@univ-lemans.fr

Iza Marfisi-Schottman

Cendrine Mercier
email: cendrine.mercier@univ-nantes.fr

Authoring Tools: The Road To Democratizing Augmented Reality For Education

Keywords: Augmented Reality, Authoring Tools, AR for Education, AR Activities, Design Study, User Study

Augmented Reality (AR) has great potential to facilitate multisensorial and experiential learning. However, creating activities in AR for everyday classroom use is far from an easy task for non-experts users, such as teachers and learners. To examine if and how an authoring approach for AR can be beneficial for educational contexts, we first designed MIXAP through a participatory design with 19 pilot teachers. MIXAP enables non-expert users to create AR activities using interactive and visual authoring workflows. To evaluate our approach with a wider audience of teachers, we conducted a study with 39 teachers examining the usability, utility, acceptability, and transfer between pilot and non-pilot teachers. We found that this approach can help teachers create quality educational AR activities. For both groups, the effect sizes were significantly large for ease of use, emotional experience, and low cognitive load. Additionally, we found that there is no significant difference between the pilot and non-pilot teachers in terms of ease of use, learnability, emotional experience, and cognitive load, highlighting the transfer of our approach to a wider audience. Ultimately, we discuss our results and propose perspectives.

INTRODUCTION

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However, authoring AR activities that support pedagogical objectives, is still far from easy for teachers. Authoring AR content still requires advanced programa

ming knowledge and skills in specialized toolkits, such as Unity1 , Vuforia2 , ARCore3 . This makes authoring AR only accessible to a small group of people with advanced programming skills. Furthermore, because existing AR toolkits are designed for general purposes, they lack support for educational AR content. Currently, it is harder for educators to harness this emerging learning medium in everyday classroom.

Authoring tools [START_REF] Lieberman | End-user development: An emerging paradigm[END_REF] offer a new approach that can democratize AR for education by lowering the barriers to creating AR content. AR authoring tools can provide users with tools that enable them to create or modify AR artifacts without programming [START_REF] Ez-Zaouia | A design space of educational authoring tools for augmented reality[END_REF]. This is promising because people who are not professional software developers might be able to create AR activities, using user-friendly and easy-to-use interactions. For instance, educators can take a photo of an object, a poster, or a book and add multimodal resources as virtual augmentations, such as texts, audios, videos, images, or 3D models. While AR authoring approaches seem very promising for teachers, they have not been studied extensively, especially not by involving endusers in the design process. Systematic reviews have raised several challenges in educational AR, such as the complexity of the technology, difficulties in usability, and a lack of ways to customize AR contents (Akc ¸ayır and Akc ¸ayır, 2017; [START_REF] Yang | XR-Ed Framework: Designing Instruction-driven andLearner-centered Extended Reality Systems for Education[END_REF][START_REF] Ibáñez | Augmented reality for STEM learning: A systematic review[END_REF]. Other reviews have found that existing AR authoring tools have limited support for educational content [START_REF] Ez-Zaouia | A design space of educational authoring tools for augmented reality[END_REF][START_REF] Dengel | A review on augmented reality authoring toolkits for education[END_REF], suggesting that more work is needed.

To examine if and how AR authoring approaches can benefit teachers, we first designed MIXAP, a prototype that enables teachers to create educational AR activities through simple authoring workflows (Figure 2). We designed MIXAP through an iterative and participatory design with 19 pilot teachers. To evaluate our approach with a wider audience of teachers, we designed and conducted a study with two groups of 39 teachers focusing on usability, utility, acceptability, and transfer. More specifically, we compared the group of pilot teachers to a second group of non-pilot teachers. The non-pilot teachers used the MIXAP for the first time. We found that MIXAP can support both groups of teachers in creating quality educational AR activities. We found that for both groups, the effect sizes of ease of use, emotional experience, and low cognitive load of [TOOL] were significantly large. In addition, we found that there is no significant difference between the pilot and non-pilot teachers in terms of ease of use, learnability, emotional experience, and cognitive load, highlighting the transfer of our approach to a wider audience.

BACKGROUND

Numerous systematic reviews have revealed trends, benefits, and challenges of educational AR applications [START_REF] Radu | Augmented reality in education: a metareview and cross-media analysis[END_REF][START_REF] Akc ¸ayır | Advantages and challenges associated with augmented reality for education: A systematic review of the literature[END_REF][START_REF] Arici | Research trends in the use of augmented reality in science education: Content and bibliometric mapping analysis[END_REF][START_REF] Dengel | A review on augmented reality authoring toolkits for education[END_REF][START_REF] Hincapie | Educational applications of augmented reality: A bibliometric study[END_REF][START_REF] Ibáñez | Augmented reality for STEM learning: A systematic review[END_REF][START_REF] Garzón | How do pedagogical approaches affect the impact of augmented reality on education? A meta-analysis and research synthesis[END_REF]. For example, [START_REF] Radu | Augmented reality in education: a metareview and cross-media analysis[END_REF] analyzed 26 studies that compared AR learning to non-AR learning and found that AR had several benefits for learners, such as fostering motivation, collaboration, retention, and learning spatial structures. Focusing on the pedagogical approaches taken in AR, [START_REF] Garzón | How do pedagogical approaches affect the impact of augmented reality on education? A meta-analysis and research synthesis[END_REF] reviewed the impact of AR factors, namely collaborative learning, project-based learning, situated learning, multimedia learning, intervention duration, the environment of use (e.g., classrooms, outdoors, field drops, museums). The authors found that collaborative AR showed the highest impact on learners. [START_REF] Ibáñez | Augmented reality for STEM learning: A systematic review[END_REF] reviewed AR literature concerning sci-ence, technology, engineering, and mathematics fields (STEM) and characterized AR applications, instructional processes, research approaches, and problems reported.

Given the availability of the above-mentioned studies, in this paper, we instead focus on the creation of AR content by the teachers themselves using authoring tools. Such tools attempt to make creating, modifying or extending software artifacts less technical, easier, and accessible to people who are not professional developers. They also provide end-users the means to adapt the content to their needs and not be limited to what pre-made artifacts offer. This is particularly important for education in three main ways. First, teachers can automate the creation of AR artifacts. Second, they can customize and personalize artifacts to suit their teaching needs. And finally, they can appropriate and take ownership of tools and artifacts in their unique ways.

While the authoring approaches are extremely important for the wider adoption of AR in education, they have not been studied extensively. Very few studies reviewed design aspects underlying AR authoring tools. [START_REF] Nebeling | The Trouble with Augmented Reality/Virtual Reality Authoring Tools[END_REF], classified existing authoring tools relevant to the rapid prototyping of AR/VR experiences in terms of four main categories: screen types, interaction (use of the camera), 3D content, and 3D games. [START_REF] Mota | POSTER] Authoring Tools in Augmented Reality: An Analysis and Classification of Content Design Tools[END_REF] discussed authoring tools under the lens of two main themes: the authoring paradigms (stand-alone, plug-in) and deployment strategies (platform-specific, platformindependent). [START_REF] Dengel | A review on augmented reality authoring toolkits for education[END_REF] reviewed 26 AR toolkits cited in scientific research. They characterized toolkits by their level of required programming skills (high, low, or medium), level of interactivity (static, i.e., without user interaction, or dynamic), affordability: (free or commercial), device compatibility (mobile, desktop, HMDs, or web) and collaboration capacity (yes or no). However, the aforementioned research focused mainly on authoring tools that require some level of programming and mostly the ones cited in scientific research including non-educational tools. [START_REF] Ez-Zaouia | A design space of educational authoring tools for augmented reality[END_REF] recently analyzed 21 educational authoring tools that do not require programming, from both industry and academia. They formulated a design space of four design dimensions of AR authoring tools, namely, (1) authoring workflow (production style, content sources, collaboration, and platform), (2) AR modality (object tracking, object augmentation, interaction, and navigation), (3) AR use (device type, usage, content collection, connectivity, and language) and (4) Content and User Management (sharing, administration, and licensing). In addition, these reviews raised several design challenges of edu-cational AR, such as usability, lack of customization, expensive technology, and lack of holistic models and design principles for AR [START_REF] Ez-Zaouia | A design space of educational authoring tools for augmented reality[END_REF][START_REF] Dengel | A review on augmented reality authoring toolkits for education[END_REF][START_REF] Akc ¸ayır | Advantages and challenges associated with augmented reality for education: A systematic review of the literature[END_REF][START_REF] Yang | XR-Ed Framework: Designing Instruction-driven andLearner-centered Extended Reality Systems for Education[END_REF][START_REF] Ibáñez | Augmented reality for STEM learning: A systematic review[END_REF][START_REF] Nebeling | The Trouble with Augmented Reality/Virtual Reality Authoring Tools[END_REF].

While these studies provided insights into the design and use of AR, there is still a lack of design-based research into AR authoring tools for education. To the best of our knowledge, studies that involve teachers in the design process of AR authoring tools are very limited in number. We build upon previous studies [START_REF] Ez-Zaouia | A design space of educational authoring tools for augmented reality[END_REF]Dengel et al., 2022, e.g.,) to better understand if and how authoring approaches can benefit the design and use of AR activities in educational settings. We engaged with teachers in an iterative and participatory design process. We designed an authoring tool to make education AR accessible to non-export users, taking into account design considerations of the authoring workflows, customization, multimodality, and interactivity.

TEACHER-CENTERED ITERATIVE DESIGN PROCESS

We conduct our work in the context of a design-based research project that involves end-users, namely teachers, learners, and educational managers (who train teachers). We followed an iterative teacher-centered design process and went through four main iterations (Ez-Zaouia, 2020). Figure 1 summarizes our four main iterations.

Understanding AR Use In Classrooms

To understand AR use, teachers' practices, and challenges related to integrating AR in everyday classrooms, we created a partnership with CANOPE, a public network that offers professional training for teachers in France. One of the main focuses of CANOPE is to help teachers integrate innovative technologies in their classrooms. This partnership allowed us to identify challenges that CANOPE's educational designers/managers experienced first-hand in their recent work with teachers on educational AR. Over several weeks, we conducted several meetings with the educational managers where they shared with us (i) their work with teachers, (ii) existing AR technologies used by teachers, and (iii) challenges that teachers face with existing AR technologies.

The recurrent challenges in existing AR technolo-gies that were evoked were: usability difficulties, lack of ways to customize the experiences and the contents, inadequacy of existing technologies for teachers, and difficulty to design effective AR activities. These findings corroborate previous results from systematic reviews [START_REF] Dengel | A review on augmented reality authoring toolkits for education[END_REF][START_REF] Akc ¸ayır | Advantages and challenges associated with augmented reality for education: A systematic review of the literature[END_REF][START_REF] Yang | XR-Ed Framework: Designing Instruction-driven andLearner-centered Extended Reality Systems for Education[END_REF][START_REF] Ibáñez | Augmented reality for STEM learning: A systematic review[END_REF][START_REF] Nebeling | The Trouble with Augmented Reality/Virtual Reality Authoring Tools[END_REF], suggesting that these challenges are not addressed yet. We traced most of these challenges to the authoring approaches of existing AR technologies for education, see a review [START_REF] Ez-Zaouia | A design space of educational authoring tools for augmented reality[END_REF]. On one hand, many of the existing AR authoring technologies offer either off-theshelf content or editable templates for AR. However, off-the-shelf and editable templates provide pre-made activities (one-size-fits-all) that teachers cannot customize and personalize to suit their needs best. This is a major barrier for teachers because it prevents them from creating personalized content for more situated AR learning, which is shown to have the highest impact on learners [START_REF] Garzón | How do pedagogical approaches affect the impact of augmented reality on education? A meta-analysis and research synthesis[END_REF]. On the other hand, some tools allow users to create AR experiences using their content. However, most of these tools do not enable the creation of pedagogical activities that can address classrooms' needs. [START_REF] Ez-Zaouia | Emodash: A dashboard supporting retrospective awareness of emotions in online learning[END_REF] found that existing AR authoring tools lack "built-in" support for helping teachers and learners use AR to its full pedagogical capacity [START_REF] Ez-Zaouia | A design space of educational authoring tools for augmented reality[END_REF].

In sum, our domain exploration revealed three main gaps in existing research and practice around educational AR: (1) lack of pedagogical approaches in existing authoring tools, (2) lack of authoring approaches that are suitable for teachers, and (3) lack of guidance and principles on how to design effective educational AR activities.

Understanding Teachers-needs for AR Activities

After our domain exploration, we felt important to formally engage with teachers to understand their needs for AR. Through CANOPE, we recruited 19 pilot teachers from various disciplines: AR elicitation and ideation: For each session, we started with a 30-minute elicitation phase, during which teachers explored 11 AR educational applica- tions4 . We then asked them to paper prototype one or more AR activities for their course. We opted for paper prototyping to remove technical constraints.

We provided teachers with a toolkit, i.e., large paper worksheet, markers, tablet screens printed out on paper, and guidelines to help them describe (i) the context in which the AR activity is used (ii) the objects they wanted to augment, (iii) the augmentations they wanted to add, and (iv) the interactions they wanted their students to have access to via the tablet's screen.

After the paper prototyping, we asked the participants to present their prototypes to the group. We videotaped the workshops. We collected the recordings and 24 worksheets. Three authors analyzed the recording and the worksheets to identify teachers' needs. The collected data is open online [START_REF] Author | Anonymized. Mendeley Data[END_REF].

Pedagogical AR activities that teachers want: In our analysis, we identified five types of pedagogical AR activities and two ways of combining AR activities together to create sequences. The most common type of activity is image augmentation. In this activity, teachers would like to add multimodal resources (texts, images, videos, audios, 3D models, and interactive menus) to images, such as posters, books, and exercise sheets. For example, a primary school teacher wanted to augment the pages of a book with audio recordings of her narrating textual vocabulary (e.g., "The reindeer has four hooves and 2 antlers"). She also wanted to augment imaginary illustrations of animals in a book with real images so that kids associate real and imaginary illustrations. Another teacher wanted to augment the photos of his high-school students with 3D models they created in technology class. It is not surprising that most teachers have thought of this type of activity since it is the one available the most in the authoring tools but it is quite limited in the number or type of resources that we can create. The second type of activity is image annotation in which teachers wanted to associate information (e.g., legends) to specific points of an object. For example, a teacher wanted to add specific legends to different areas on a map. The third type of activity is image validation, in which teachers wanted to create activities that learners can complete on their own by using AR to validate automatically if the chosen image is correct or not. For example, a middle school science teacher wanted to ask students to assemble the pieces of a map correctly. Another teacher wanted students to identify a specific part of a machine (e.g., motors) by scanning image markers attached to the machine. The fourth type of activity is images association in which teachers wanted to display multimodal resources when two image markers are visible on the scene at the same time. For example, a primary school teacher wanted children to practice recognizing the same letter, written in capital and small letters. Another teacher wanted to use this type of activity for learning concepts by associating different modalities, for example, learning fruit vocabulary using words (e.g., banana) and imagery. The fifth type of activity is a images superposition in which teachers wanted to display layers of information on the top of an image. For example, a university geology teacher wanted students to be able to activate or deactivate layers of information showing various types of rocks and tectonic plates on geographic maps. Finally, teachers wanted to create activity clusters and activity paths by combining activities without or with predefined order. Clusters and paths are essential for teachers who design their activities in a form of a pedagogical sequence. Apart from image augmentation, the activities that the teachers wanted are rarely supported by the existing authoring tools.

Iterative Prototyping and Preliminary User-studies

To address teachers' needs, we iteratively designed, over a period of five months, three main prototypes leading to a working prototype, We decided to use web technologies for AR, which we knew will facilitate rapid prototyping and also support teachers' various needs.

In the first month, we were able to design the first prototype with the image augmentation activity. This was important for us to rapidly gather user feedback using a concrete tool. We conducted a first preliminary user study with more than 20 teachers in a local seminar about serious games. We prepared a worksheet with 5 activities. In each activity, we asked the participants to augment an image with different media resources, namely, text, image, audio, video, and 3D object. We had several issues with the software during this user study. In part, the image, audio, video, and 3D object were not loaded correctly in the AR view. We ended up using only text augmentations with the participants. Other issues relate to teachers adding multiple augmentations to a marker. The prototype had issues handling positions of multiple augmentations. Teachers also wanted to take a photo of their objects (e.g., books, flyers) and add them as markers but the first prototype allowed only uploading existing images from the user's device. Even though this first prototype had many issues, most participants reacted positively to the visual authoring approach of the tool (see Section 4). They highlighted that the interface is simple and user-friendly. Also, the steps we designed in the editor provided guidance in helping the user incrementally create an AR activity.

In the following month, we designed a second prototype that addressed all the issues that we faced during the first preliminary user study. Similar to the first prototype, we focused on the image augmentation activity. We then conducted a second user study with the help of CANOPE, for which we invited 12 of our partner teachers to evaluate the new prototype. We asked teachers to bring their educational materials (e.g., books, flyers, images) so that they can use the prototype to create their AR activity. We also had some minor issues. One issue was that markers that are photos taken by the camera took too long to process for an AR view, partly because the teachers' tablets have limited memory and processing units. However, apart from minor issues, the prototype worked well. In this study, we validated the design choices of the visual authoring approach. All the participants reacted positively to using user-friendly interactions (click, drag, resize), and configuration menus with smart-default pa-rameters to personalize the look and feel of the content (e.g., text and image styles). In addition, the participants highlighted some improvements, such as adding the possibility to record audio or a video rather than uploading a file from the user's device.

We decided to combine image annotation and augmentation since the position of the augmentations is precise enough to annotate a precise part of an image marker. In the following two months, we refined the tool and designed authoring workflows for the other types of activities. We refined the augmentation/annotation and designed authoring workflows for the association, validation, cluster, and path activities.

Formal Study With Teachers

To evaluate whether an authoring approach can support teachers in creating various AR activities, we designed a formal study with two groups of teachers. One group was composed of pilot teachers who partnered with the project through CANOPE. The second group was composed of teachers who were unfamiliar with the project. We provide details about the study in the method and report the results in the results section.

MIXAP: An Authoring Tool For

Educational AR

We designed MIXAP to make creating educational AR activities easier and less technical for non-expert users, such as teachers and learners. Based on our domain exploration with teachers and literature review, we derived three design goals to support teachers' needs.

Design Goals

DG1: Incorporate authoring workflows to facilitate creating pedagogical AR activities: A major challenge in authoring tools for AR is the lack of support and guidelines to help users in creating AR content for classroom use [START_REF] Ez-Zaouia | A design space of educational authoring tools for augmented reality[END_REF][START_REF] Dengel | A review on augmented reality authoring toolkits for education[END_REF]. To alleviate this challenge, we designed five authoring workflows in MIXAP to guide end-users in creating the AR activities that we identified during the participatory design with teachers (i.e., augmentation, validation, association, superposition, cluster, and path). Each workflow has a specific set of incremental authoring steps. DG2: Provide a visual authoring approach to reduce the cognitive burden of creating educational AR content: A major challenge hindering the wider adoption

(1) Naming Consists of giving a title, a description and an instruction. The latter is displayed in learner view.

(2) Marker Consists of taking a picture or adding a picture of the object to be increased. For the association activity, the teacher two pictures.

( of AR in education is, in part, related to the technical complexity of authoring AR activities. A visual authoring approach is more suitable for teachers because it leverages user familiarity with widely-used tools in education, such as Google docs and PowerPoint. Such an approach enables users to author content using user-friendly interactions, such as drag-and-drop and configuration menus. DG3: Support users in personalizing the look and feel of an AR activity content: Another major limitation in existing AR applications for education is that teachers can not customize the content. Because personalization is crucial for learning [START_REF] Mayer | The Cambridge Handbook of Multimedia Learning[END_REF], MIXAP enables users to personalize all aspects of an AR activity. In terms of content, users can create or import different media resources. For example, users can import files from their devices, such as images, 3D models, audio, and videos. They can take photos and record audios and videos. They can also fine-tune the styles, such as size, color, shape, and fonts of the resources of an AR activity.

User Interface

The user interface of MIXAP (Figure 2) consists of four main views: a library, dashboard, editor, and player.

Library View: Lists all the AR activities that all users created and shared publicly. Users can create an AR activity by clicking on the button "add activity" and then selecting the type of activity they want to create (DG1, Figure 2-b).

Dashboard View: This view is similar to the library view, however, it only lists the AR activities of the user (Figure 2-a). Similarly, user can also create their AR activities by clicking on the button "add activity".

View:

The editor provides specific authoring workflows for each of the five AR activities (DG1). Each authoring workflow provides a few steps to create an AR activity using visual and user-friendly interactions (DG2). For example, one step is naming activity providing a title and a description (Figure 2-c). adding a marker image of the activity, which consists either uploading an image or a photo of an object (Figure 2-d). In the case of an association we prompt the user to two marker Another step is adding multimodal augmentations on the top of a marker (Figure 2-e). The augmentations are the media resources that the user can add to an AR activity, audio, video, modal, 3d objects. Another step enables trying the activity in AR view (Figure When the user launches an activity in AR view, we recognize marker object through the user's device camera, and we the digital (virtual) augmentations on the tangible (real) marker object. In addition, users can customize the size, color, shape, and fonts of the resources of an AR activity. Finally, users create clusters of paths from existing activities by selecting two or more activities and grouping them with or without order.

Player: Launches an activity in AR View (similar to Figure 2-f but in full screen). The player opens the user's device camera and loads the marker as well as all the augmentations of the activity. The player, then, (i) scans the scene to detect the maker of the activity and (ii) projects the augmentations on the scene.

User Interaction and Customization Modalities

All the resources in MIXAP have built-in smartdefault parameters (DG1). In addition, MIXAP provides user-friendly modalities so that users can customize the look and feel of an AR activity (DG2, DG3). The users can upload resources from their local devices or create and edit resources using built-in media-resources tools (images, audios, videos, and 3D models). In terms of interaction, the users can rotate resources in 3D three-dimensional space. They, scale up or down the size of the resources. And they can drag resources to arrange them in unique ways on the canvas. Further, MIXAP offers options to change text size, font, color, and background.

Implementation

We implemented the user interface of MIXAP using Typescript5 and Reactjs6 . We use MindARjs7 for image tracking. We use RxDB8 to store the resources of the AR activities in the user's device. We use Supabase9 to enable sharing AR activities between different devices.

TEACHER STUDY

We conducted a user study with 39 teachers to examine whether they can easily use MIXAP to create highquality educational activities in augmented reality. We used mixed methods analysis and followed an overall factorial design with the following factors and levels:

• Group of participants: Pilot Teachers (P) & nonpilot teachers (NP).

• Category of evaluation: Usability, Learnability, Utility, Emotion, Cognitive Load, and

• Demographics: Participants' Age and Discipline (STEM, Non-STEM, Computer Science Other)

Hypothesis

We examine seven main hypotheses:

• Usability:

-H1: The usability of MIXAP will be higher than the median value (Likert = 3).

-H2: The ease of use of MIXAP will be higher than the median value (Likert = 3).

• Utility:

-H3: The utility of MIXAP in creating AR activities for teachers will be higher than the median value (Likert = 3).

• Acceptability:

-H4: The emotional experience of MIXAP will be higher than the median (Likert = 3).

-H5: The cognitive emanating from using MIXAP will be lower than the median value (Likert = 3).

• Transfer:

-H6: There will be no differences between the partner and non-partner teachers in terms of usability, ease of use, emotional experience, and cognitive load.

-H7: There will be no differences between participants' age and discipline in usability, learnability, utility, emotional experience, and cognitive load.

Participants

The participants were 39 (N=39) teachers [31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50], > 50 = 7; Discipline: STEM = 22, Non-STEM = 7, Computer Science = 1, Other = 9]. We divided teachers into two groups: the pilot group (P) with 12 teachers and the non-pilot group (NP) with 27 teachers. The pilot teachers were familiar with the MIXAP. We recruited the non-pilot teachers during a local workshop, and they were not familiar with the MIXAP prior to the study. All teachers voluntarily participated in the study without compensation and signed a consent for using and analyzing their data for research. Figure 3: An example of the AR activity that the teachers created to associate two cards. We introduced this activity to the teachers as: "This type of activity allows you to visualize augmentations if two images are visible in the scene at the same time (e.g., side by side). You can thus create an automatic correction for your association activities (e.g., an association of a picture with a word, an association of a word in French with a word in another language, etc.)"

Educational AR Activities

We prepared four different AR activities10 for teachers to create using MIXAP (see the example, Figure 3). One activity involved augmenting a book with multimodal resources. Another activity involved associating images. The third activity involved validating an image. And, the final activity involved creating a learning cluster or a path from the first three activities.

Apparatus

We used three apparatuses: MIXAP, paper sheets of the four activities, and different props. We installed MIXAP on 12 tablets (Samsung SM-X200, SM-T500, 11 inches, 2G RAM, 32G Hard Drive). We printed out the steps to create the four activities on paper. We selected a set of props: cards from the Dixie game, books, and posters, which we provided the participants with to create resources for AR activities, such as markers, images, and videos.

Procedure

The procedure consists of three steps. We first introduced the study and the four AR activities to the teachers. Then, we asked the teachers to create the four activities (see example in Figure 3). And, finally, we asked the teachers to answer a questionnaire 11 .

Data Collection and Analysis

We collected 39 responses to the questionnaire from the participants; 12 responses from the partner group and 27 responses from the non-partner group. For the usability (H1-2), we used the SUS questions [Q1-10] to assess the total SUS score as well as ease of use [Q1,2,3,5,6,7,8,9] and learnability [Q4,10] [START_REF] Brooke | Sus-a quick and dirty usability scale[END_REF][START_REF] Lewis | The factor structure of the system usability scale[END_REF]. We formulated the question [Q15] along with an open-ended question [Q16] to assess the utility (H3). For the acceptability (H4-5), we formulated [Q12,14] to assess emotional experience and [Q11,13] to assess the cognitive load.

For transfer (H6-7), we analyzed the variance of responses with respect to the factors Group and Demographics (Disciple and Age). Questions were on a 5-point Likert scale, except for [Q16].

RESULTS

We report and interpret our results using both p-values for statistical significance and Effect Sizes for quantifying the main difference (small: < .3, moderate: .3 -.5, large: > .5), with a 95% confidence interval [START_REF] Robertson | An introduction to modern statistical methods in hci[END_REF]. We used a nonparametric Wilcoxon test for One-sample tests of participants' Likert responses. We used nonparametric factorial analyses of the variance of participants' Likert responses using the Aligned Rank Transform (ART) [START_REF] Wobbrock | The aligned rank transform for nonparametric factorial analyses using only anova procedures[END_REF].

Overview

Overall, almost all participants completed the activities at about the same time. Figure 6 summarizes the responses to the questionnaire, highlighting participants' positive reactions to the tool.

Usability

We followed [START_REF] Brooke | Sus-a quick and dirty usability scale[END_REF][START_REF] Lewis | The factor structure of the system usability scale[END_REF] and calculated scores (on a scale of 0 to 100) 11 https://forms.gle/GP1NCaEpAtihzqbY6

Figure 4: Utilisability: On the left is a boxplot with median, min-max, and outliers; right is effect sizes with 95% confidence intervals.

for SUS [Q1-10], ease of use [Q1,2,3,5,6,7,8,9], and learnability [Q4,10]. As illustrated in Figure 4 (legend P NP), the mean SUS score for all participants was 73.91, which is above the standard SUS average of 68 points and is classified as "good." A one-sample Wilcoxon test showed that teachers' scores were significantly higher than 68 points, with a medium effect size for total SUS (p = .015, r = .389), a medium effect size for ease of use in hand (p = .049, r = .315), and a large effect size for learnability (p = .009, r = .756).

Analysis by Group showed that the difference was not significant for SUS, with a medium effect size for the pilot (P) group (p = 0.107, r = 0.476) and a trend toward significance for the non-pilot (NP) group (p = 0.062, r = 0.360). The difference in ease of use was not significant with a mean effect size for both groups: P (p = 0.288, r = 0.318) and NP (p = 0.104, r = 0.314). In contrast, the difference between the groups for learnability was significant, with a large effect size for the P group (p = 0.022, r = 0.756) and a medium effect size for the NP group (p = 0.022, r = 0.443).

Utility

We performed a one-sample Wilcoxon test to assess whether the utility [Q14] is greater than the median value of 3. For the data combined from both groups, the utility was not significant with a small effect size (p = 0.25, r = 0.186). Analysis by Group showed that the difference in utility was significant for the pilot group (P) with a large effect size (p = 0.043, r = 0.598) and not significant for the non-pilot group (NP) (p = 0.684, r = -0.084). Some teachers elaborated on new features they would like to have in the tool, such as, for example, an AR activity capable of recognizing multiple markers automatically and displaying augmentations; adding videos to sheets and interactive menus; or exploring details in large image markers. Others have elaborated on technical issues such as accuracy, stability, or responsiveness of the augmentations. These elements can have an impact on the participants' perceived utility.

Acceptability

As illustrated in Figure 5, a one-sample Wilcoxon test showed that Likert responses were significantly different from the median with a large effect size for emotional experience [Q12,14] (p < 0.001, r = 0.666) and a large (negative) effect size for cognitive load [Q11,13] (p < 0.001, r = -0.827). Analysis by Group showed that the difference in emotional experience was significant with a large effect size for the pilot (P) (p < 0.0001, r = 0.814) and non-pilot (NP) groups (p < 0.0001, r = 0.631). Similarly, the cognitive load was significantly lower than the median value of 3 with a large effect size for the pilot (N) (p < 0.0001, r = -0.898) and non-pilot (NP) groups (p < 0.0001, r = -0.825).

Transfer

We conducted a non-parametric factorial analysis of the variance of Likert responses using the ART procedure [START_REF] Wobbrock | The aligned rank transform for nonparametric factorial analyses using only anova procedures[END_REF]. The analysis showed that there was no significant difference in responses between the levels of the Category: usability, learnability, utility, emotional experience, and cognitive load (p = n.s) with respect to the participants' Group (p = .180), Age (p = .889), and Discipline (p = .520).

DISCUSSION

We first summarize our results on the main effects of the authoring approach. We then discuss our results concerning the design and use of AR authoring tools in classrooms.

Impact of AR authoring approach

The analysis of SUS scores validates H1. The ease of use and the learnability were significant, validating H2. Thus, the usability of MIXAP is quite reasonable and comparable between pilot and non-pilot teachers. Both groups were able to create all four activities at about the same time. The acceptability of the tool is important, participants showed positive reactions (also observed during the experiments). The emotional experience was significantly strong, validating H3. Since we propose authoring workflows with incremental steps and intuitive interactions, the cognitive load was significantly low, validating H4. Regarding the transfer, the factorial analysis of variance showed no significant difference between the pilot and nonpilot teachers, validating H6 and H7, which shows that this approach may be beneficial for a wider audience of teachers. In contrast, the perceived utility was significantly high only for the pilot group, partially validating H5. In our next iterations, we will further examine the utility aspects, particularly the questions of (1) the appropriation and (2) the longitudinal impact of our approach. We believe teachers will appropriate better the tool as we plan to provide them with video tutorials and resources to support them in creating educational AR content.

Participatory design of AR authoring tools

An authoring tool aims at providing a wide range of functionalities to support a wider audience of users with different technological familiarities -while allowing them to (1) create artifacts without programming and (2) appropriate the tool easily [START_REF] Lieberman | End-user development: An emerging paradigm[END_REF][START_REF] Ez-Zaouia | A design space of educational authoring tools for augmented reality[END_REF]. In this sense, a participatory design with teachers allowed us to identify pedagogical needs that we were able to transpose into small building blocks to enable users in creating AR activities using user-friendly interactions. The transfer analysis showed that this approach is viable. Even though the tool was only designed based on 19 pilot teachers, the evaluation with 27 non-pilot teachers showed that there was no difference between the pilot and non-pilot teachers in terms of ease of use, learnability, usability, emotional experience, and cognitive load. Similarly, there were no differences in the age and discipline of the participants. That said, the participatory approach also poses some challenges. Combining various users' needs without making an authoring tool too complex can be a difficult task. This constraint requires making design choices such as selecting, validating, and even rejecting some needs (e.g., needs that are specific to a context of use or that do not meet common needs or risk making the tool more complex for the majority of teachers). The constraint is even more severe when the needs have been identified in the form of complete pedagogical scenarios or activities. Some teachers may have difficulty identifying their needs in the tool -as they had expressed them during the participatory design, which could negatively impact their perception of the tool's utility. Our analysis of utility supports this point. Some teachers did not identify their needs in the tool in an explicit way. An authoring tool requires an appropriation effort from the end-users to adapt its functionalities to meet unique needs.

Integration of AR authoring tools in the classroom

Beyond the technical aspects, creating AR activities by end-users poses a cognitive difficulty. This difficulty stems from the fact that end-users have little familiarity with AR content creation. This type of production

has not yet become a convenient task compared to, for example, multimedia productions using Google Docs or Powerpoints. Teachers may find it difficult to develop instructional activities in AR or to adapt their traditional activities to immersive AR learning. In addition, teachers may not have training in multisensory and experiential learning. Even if an authoring tool removes the technical barrier, creating this type of content requires skills, such as principles of multimedia, coherence, multimodality, and personalization [START_REF] Yang | XR-Ed Framework: Designing Instruction-driven andLearner-centered Extended Reality Systems for Education[END_REF].

Potentially, a library of educational activities where teachers can find, create and share AR activities can help address the above challenges. Our approach goes in this direction and aims at proposing small building blocks that teachers can use and combine in an easy manner to create more advanced activities or even new activities that they have not thought of. For example, in the context of learning to associate concepts in a collaborative mode, a teacher was able to create an original activity where learners combine AR with tangible artifacts: books, post-its, images, and cards to complete. Learners in pairs visualize multimodal augmentations of a book, identify associations related to the augmentations, and paste post-its and images on the card. The teacher was able to appropriate the tool while using everyday artifacts in the classroom and was able to create an immersive environment for learning associations of concepts. Sharing these types of activities among teachers can help in the successful integration of AR authoring tools in classrooms.

Another important aspect that can improve the appropriation of AR authoring tools by teachers is learning analytics (e.g., dashboards). We believe providing teachers with feedback about learners' experiences, such as emotional state [START_REF] Ez-Zaouia | Emodash: A dashboard supporting retrospective awareness of emotions in online learning[END_REF]Ezzaouia andLavoué, 2017), progression andengagement (Ez-zaouia. et al., 2020) can support them in fine-tuning AR activities for their classrooms best.

CONCLUSION

AR offers an interesting environment to facilitate multisensorial, immersive, and engaging learning. However, creating AR activities is far from an easy task. We examined an "authoring tool" approach to making AR less technical and more accessible to non-expert teachers. We presented our process for designing MIXAP in an iterative, participatory design approach with pilot teachers. We evaluated our approach with 39 teachers. The results are very encouraging to further explore this approach, especially the analysis of usage, appropriation, and activities created by teachers in their classrooms. We hope that our work provides researchers and designers with ideas for the design and use of AR-authoring tools to democratize AR for education.

  [Gender: (women = 8, men = 11), Teaching Years: (min = 2, max = 40), School Level: (elementary = 5, middle = 11, high = 2, university = 1)]. Teachers have various technology-use expertise in classrooms, AR Use: 26.3% and Smartphone Use: 63.2%. We conducted two 3-hour codesign sessions where teachers paper-prototyped AR activities they wanted to create for their classrooms.

Figure 1 :

 1 Figure 1: Teacher-centered design process we followed in this work, including (1) understanding AR use in classrooms, (2) understanding teachers-needs for AR, (3) iterative prototyping and preliminary user studies, and (4) formal study with teachers.

f

  Editor View: The editor provides creation workflows for the six AR Each workflow provides steps to create an activity visual and interactions. Here are the steps for creating the image augmentation activity:

Figure 2 :

 2 Figure 2: Main views of the user interface of MIXAP. (a) is the dashboard view which displays all the AR activities user. (b) the list of AR activities that teachers can create. (c, e, f) are views of the editor. Each AR activity has a set of steps (workflow) to guide the user creating the activity -steps are on top of the editor: naming, (d) marker, (e) augmentation, and (f) try the activity. The marker is placed in the center of the editor. The palette of tools to add multimodal resources as augmentations to an activity(text, image, video, audio, modal, and 3D object) are on the left of the editor. Users add resources drag and drop them on the canvas. MIXAP allows users to customize all aspects of augmentations in terms of content and appearance (rotate 3D assets, change sizes and styles of assets).

Figure 5 :

 5 Figure 5: Acceptability: On the left is a boxplot with median, min-max, and outliers; on the right are the effect sizes with the 95% confidence interval.

Figure 6 :

 6 Figure 6: A summary of Likert responses of all the participants.

https://unity.com/

https://www.ptc.com/en/products/vuforia

https://developers.google.com/ar

Foxar, SpacecraftAR, Voyage AR, DEVAR, ARLOOPA, AnatomyAR, ARC, Le Chaudron Magique, SPART, Mountain Peak AR, SkyView Free

https://www.typescriptlang.org/

https://reactjs.org/

https://github.com/hiukim/mind-ar-js

https://rxdb.info/

https://supabase.com/

https://drive.google.com/file/d/11I4vJTB-GgW1Xtmb- sAL0gWPiWVyksUL/view

ACKNOWLEDGMENT

We thank the Rising Star program of Pays de la Loire, France, for funding the MIXAP research project. Special thanks to the teachers, including the pilot: Annabel Le GOFF, Camille POQUET, Damien DUMOUSSET, Delphine DESHAYES, Elisabeth PLANTE, Frederic LLANTE, Laurent HUET, Nicolas JOUDIN, Tony NEVEU, Vanessa FROC, Yannick GOURDIN, Régis MOURGUES, Morgane ACOU-LE NOAN, and Adeline JAN.


Thibaut Cuvelier
email: tcuvelier@google.com

Frederic Didier

Vincent Furnon
email: vfurnon@google.com

Steven Gay
email: stevengay@google.com

Sarah Mohajeri
email: mohajeri@google.com

Laurent Perron
email: lperron@google.com

OR-Tools' Vehicle Routing Solver: a Generic Constraint-Programming Solver with Heuristic Search for Routing Problems

Keywords: vehicle-routing problems, constraint programming, heuristics, metaheuristics

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

OR-Tools [1] is the general-purpose optimisation toolbox open-sourced by Google in 20151 , being in development since 2008. This toolkit provides a uniform interface to several solvers, both first-and third-party. In particular, it offers a high-level interface for vehicle-routing problems (VRPs). OR-Tools contains several solvers, in particular two CP solvers, CP* (since the first open-source release) and CP-SAT (gold-medal winner at several MiniZinc competitions [START_REF] Stuckey | The MiniZinc Challenge[END_REF], developed since 2009), , but also two linear solvers: the simplex-based Glop (since 20142 ), and PDLP [START_REF] Applegate | Practical large-scale linear programming using primal-dual hybrid gradient[END_REF], a first-order large-scale linear solver. OR-Tools is being actively developed, with approximately quarterly releases. Outside Google, the solver suite is easily accessible via Google Cloud, either for solving VRPs3 or mixedinteger linear programs, although the latter API is not yet in general access 4 .

The routing component has historically played a strong role in the development of the overall solver; its major focus is on solving large-scale industrial vehicle-routing problems with complex constraints: vehicle capacities with various starting/ending depots, client time windows considering road traffic and driver breaks, pick-up-and-delivery precedence rules, incompatible shipments within the same vehicle, solution similarity to a previous call to the solver, etc. To this end, a high-level modelling API is proposed to the users in Python, C++, Java, and C#, using only routing concepts, even though the user has access to the underlying constraint-programming model.

From an algorithmic point of view, the routing solver is organised in three parts: (i) first-solution heuristics generate good potential vehicle tours; (ii) local search improves the first solutions, with metaheuristics to guide the search; (iii) a CP engine proves the optimality of the best solution or improves upon it. The main difference with many academic solvers is the focus on generality in the solver, including its heuristics.

Modelling vehicle-routing problems

VRP-like models can be directly entered using the routing modelling API, a high-level interface dedicated to this family of problems. The solver ingests the distance-weighted directed graph through a callback function (RoutingModel:: RegisterTransitCallback). Vehicles are represented by a set of optimisation variables (RoutingModel::Start and RoutingModel::Next). The solver represents quantities that accumulate along a path, like distance, time, or vehicle load, with RoutingDimension and CumulVar. This mechanism is used to model time windows.

Some specific problems have a dedicated interface, like pickup and delivery, where the same vehicle must first pickup a parcel on its route before delivering it (RoutingModel::AddPickupAndDelivery).

Arc-routing problems are not supported out of the box, but users can perform the transformation to usual vehicle-routing problems by representing arcs to visit by nodes [START_REF] Longo | Solving capacitated arc routing problems using a transformation to the CVRP[END_REF]. Edge routing corresponds to arc routing, the only difference being that traversing each edge once (in either direction) is sufficient: the distinction can be modelled using a disjunction (RoutingModel::AddDisjunction).

Solving vehicle-routing problems

OR-Tools provides several kinds of first-solution heuristics, like savings or a greedy, cheapest-arc algorithm. The next step of the solver is to call iterative improvement techniques to refine the first solutions, using heuristic operators. Many such algorithms have been implemented for VRP-like problems5 . Their goal is to alter the first solutions slightly to improve them by exploring the neighbourhood they define. This process generates many candidate solutions (typically, at least hundreds of thousands per second): not all of them are feasible or improve the solution; a filtering system ensures that only feasible solutions are further considered.

To continue improving the solution when basic local-search heuristics cannot make any more progress, a metaheuristic is used. OR-Tools implements simulated annealing, tabu search, and guided local search (GLS). The end of the search with OR-Tools is performed with a CP solver: either CP* or CP-SAT.

https://groups.google.com/g/or-tools-discuss/c/OMDgOZE7z 4/m/hkwZaKTRla4J

https://ai.googleblog.com/2014/09/sudoku-linear-optimization-and-ten-cent.html

https://cloud.google.com/optimization/docs/overview

5https://github.com/google/or-tools/blob/v9.2/ortools/constraint solver/routing parameters.proto#L115-L370


domain: spi info

text:
Cesare Tonola

Marco Faroni

Nicola Pedrocchi

Manuel Beschi

Anytime informed path re-planning and optimization for human-robot collaboration

Keywords: Path planning, Anytime motion planning, Replanning, Human-robot collaboration, Autonomous robots

published or not. The documents may come

Anytime informed path re-planning and optimization for human-robot collaboration

I. INTRODUCTION

Human-Robot Collaboration (HRC) is the research area that deals with the spatio-temporal overlap between humans and robots' workspaces, and it has undergone a significant increase of interest in recent years [START_REF] Koppenborg | Effects of movement speed and predictability in human-robot collaboration[END_REF]. The collaboration between humans and robots might be very advantageous for industries: on the one hand, robots are fast and precise, yet they are still unable to adapt their behavior to what is happening in the workstation; on the other hand, humans can react to environmental changes, but they are not repeatable and quickly tired. Their combination could allow exploiting the potential of both.

Most industrial solutions do not provide for a synergic collaboration between humans and robots. Usually, robots are enclosed into physical or virtual cages and, when the operator decides to enter, robots stop to guarantee operator safety [START_REF] Faroni | An MPC framework for online motion planning in human-robot collaborative tasks[END_REF]. Other approaches exist, such as the speed and separation monitoring (SSM) defined in the ISO-TS15066, which allows collaborative robots to operate without fencing but requiring to control the robot in such a way as to ensure that the robot can stop its movement to prevent collisions. The most widespread, yet conservative application of SSM provides that, depending on the operator position, the robot works at full speed, at reduced speed, or stops if the operator is inside a low, medium, or high danger area [START_REF] Magrini | Human-robot coexistence and interaction in open industrial cells[END_REF], [START_REF] Villani | Survey on human-robot collaboration in industrial settings: Safety, intuitive interfaces and applications[END_REF]. In this case, the operator safety is guaranteed despite the absence of fencing, but this solution often reduces throughput. Real-time techniques have been implemented to reduce safety stops and optimize the speed reduction on a This work is partially supported by ShareWork project (H2020, European Commission -G.A. 820807). 1 Dipartimento di Ingegneria Meccanica e Industriale, University of Brescia {c.tonola001,manuel.beschi}@unibs.it 2 STIIMA-CNR -Institute of Intelligent Industrial Technologies and Systems, National Research Council of Italy {marco.faroni,nicola.pedrocchi}@stiima.cnr.it pre-defined path, for example, via linear programming [START_REF] Zanchettin | Safety in human-robot collaborative manufacturing environments: Metrics and control[END_REF], PID control [START_REF] Faroni | Real-time trajectory scaling for robot manipulators[END_REF], or model predictive control [START_REF] Faroni | An MPC framework for online motion planning in human-robot collaborative tasks[END_REF]. However, if the operator continuously blocks the robot's trajectory, the robot repeatedly reduces its speed or remains completely stationary, resulting in a substantial throughput reduction and weak support for the operator during the task execution. A natural and safe collaboration should require the robots to rapidly change their path to complete the task when humans interfere with their motion, avoiding any mobile obstacles with limited idle time.

Online path re-planning modifies an initial path during its execution to avoid collisions or to improve the trajectory. For example, it computes a new path when the current one is obstructed. Some works are based on RRT-like planners [START_REF] Lavalle | Rapidly-exploring random trees: a new tool for path planning[END_REF] and prune and modify the search tree when changes of the configuration space happen [START_REF] Ferguson | Replanning with RRTs[END_REF], [START_REF] Connell | Dynamic path planning and replanning for mobile robots using RRT[END_REF], [START_REF] Zucker | Multipartite RRTs for rapid replanning in dynamic environments[END_REF], [START_REF] Otte | RRTx: Real-time motion planning/replanning for environments with unpredictable obstacles[END_REF]. Time dimension can be added to the search tree to plan a new path foreseeing possible future collisions with mobile obstacles [START_REF] Zhang | A Predictive Path Planning Algorithm for Mobile Robot in Dynamic Environments Based on Rapidly Exploring Random Tree[END_REF], [START_REF] Van Den Berg | Anytime path planning and replanning in dynamic environments[END_REF], [START_REF] Chen | Horizon-based lazy optimal RRT for fast, efficient replanning in dynamic environment[END_REF]. Another typical approach consists of modifying the robot's trajectory applying virtual repulsive forces to the robot to move it away from the obstacles [START_REF] Brock | Elastic strips: A framework for motion generation in human environments[END_REF]. These algorithms provide intuitive results; however, they are not efficient in managing complex cells, and they can get trapped in local optima.

From an HRC point of view, a re-planner should be fast enough to react to operator movements reducing as much as possible idle time, and it should find solutions easily predictable and readable by the operator. These are challenging requirements to be reached by typical approaches. Industrial environments, such as collaborative cells, make algorithms computationally challenging, resulting in the detriment of robot reactivity. Furthermore, only a local deformation of the current trajectory is often considered, resulting in a replanned path that is far from optimal.

A. Contribution

This paper proposes a reactive motion planning strategy for HRC that combines a fast path re-planning and optimization algorithm [START_REF] Tonola | Anytime informed path re-planning and optimization for robots in changing environments[END_REF] with speed modulation. This framework combines reactivity with the evaluation of multiple solutions to obtain an increasingly better result in an anytime fashion. The path re-planning algorithm searches online for new trajectories exploiting a set of paths computed offline. Notice that this is helpful in HRC as the obtained motion results to be more readable from the operator point of view. A speed monitoring module based on safety rules from ISO/TS 15066 [START_REF]:2016 Robots and robotic devices -Collaborative robots[END_REF] is then in charge of reducing the robot speed if the human is close to the robot. The goal of the replanner is to drastically reduce the need to scale the speed of the robot.

The main contributions of this paper are:

• It proposes a novel planning framework for HRC applications combining online path re-planning and optimization [START_REF] Tonola | Anytime informed path re-planning and optimization for robots in changing environments[END_REF] with speed modulation from ISO/TS 15066 [START_REF]:2016 Robots and robotic devices -Collaborative robots[END_REF]; • It evaluates the performance of the proposed approach with experimental tests, showing that the proposed solution outperforms a standard SSM strategy.

A video of the tests can also be found in the attached material. This paper is organized as follows: the overall framework is presented in Section II; the reactive planning module is explained in Section III; experimental results are provided and discussed in Section IV; finally, conclusions are drawn in Section V.

II. HRC MOTION PLANNING FRAMEWORK

The proposed framework follows a common paradigm in HRC. As shown in Figure 1, it is made up of an offline path planner and a reactive planner, which is composed, in turn, by a path re-planner and a speed modulation module. The offline planner deals with the offline planning of the robot motion; the reactive planner provides a reactive behaviour to the operator movements. The framework relies on current information about the environment and the human state; this information is typically acquired through a vision system. In the next paragraphs, the two blocks will be explained in details.

A. Offline path planner

It computes the initial trajectory from a start position to the desired goal position. Based on information about the static obstacles, the algorithm computes paths in the robot configuration space using path planning algorithms. In our framework, the offline planner computes a set of feasible paths. Then, one of them is selected as the current path (typically the one which minimizes a cost function, e.g. the path length). The current path is parametrized by means of a path parametrization method (e.g., TOPP [START_REF] Pham | A general, fast, and robust implementation of the time-optimal path parameterization algorithm[END_REF]) and the result is the robot's nominal trajectory defined as a set of timed waypoints.

The proposed framework is independent from the path planning algorithm used to find the initial set of paths. Usually, sampling-based algorithms, such as RRT-Connect [START_REF] Kuffner | RRT-Connect: An efficient approach to single-query path planning[END_REF], are used to carry out this task for they can efficiently deal with high-dimensional search spaces of robot manipulators. It is worth noticing that the paths can be computed just before the movement, so that information on the human state at planning time can be exploited. This is why Figure 1 shows a dashed connection between the vision system and the offline planner. In this regard, human-aware path planners can be used to reduce the probability of collision with humans or to find paths that optimize human comfort [START_REF] Sisbot | A human-aware manipulation planner[END_REF], [START_REF] Tarbouriech | Bi-objective motion planning approach for safe motions: Application to a collaborative robot[END_REF].

B. The reactive planner

It is at an intermediate layer between the offline planner and the robot controller. This layer provides the robot with a reactive behaviour to make it able to avoid the operator during the task execution to obtain a safe and efficient collaboration. As a matter of fact, the robot path may be obstructed by the human (which behaves like a moving obstacle) during the movement.

Most times, only a speed modulation block is implemented to slow down or stop the robot and prevent collisions. In this work, we deploy a tiered approach that also integrates a path re-planning module. In this way, the robot is able to change its path at run-time in case the current one becomes infeasible or is sub-optimal.

The online planner receives as inputs the set of paths computed by the offline planner, the current robot's trajectory, and the information about mobile obstacles from the vision system. As output, it provides the new robot state target to be given to the robot controller; this state is obtained sampling the new computed trajectory, based on the path modified by the re-planner and considering the velocity scaling calculated by the speed modulation technique.

Note that the strategy of switching between pre-computed paths offers advantages to HRC applications. Firstly, it enhances the search speed of the algorithm, improving the robot reactivity. Secondly, it allows to obtain more readable solutions for the operator since the robot switches between paths calculated offline and which can be known to the operator.

III. REACTIVE PLANNER

This section describes the algorithms used by the reactive planner. The first sub-section deals with the path re-planning method, the second one describes the speed modulation block.

A. Re-planning framework

The re-planning framework used is the one proposed in [START_REF] Tonola | Anytime informed path re-planning and optimization for robots in changing environments[END_REF], which consists of three different threads:

• the trajectory execution thread;

• the collision checking thread;

• the path re-planning thread. The trajectory execution thread receives the set of paths and the current robot trajectory computed by the offline planner. It samples the trajectory at a high rate and sends the new robot state target to the robot controller. In this case, the thread communicates with the speed modulation module to define the time instant along the trajectory at which it will be sampled. The collision checking thread deals with obtaining information from the vision system to verify if the current path, or one belonging to the set of pre-computed paths, collides. The path re-planning thread receives the current path, the set of the available paths, and the current robot configuration as inputs, tries to find a new path that optimizes the current one or which avoids mobile obstacles that are obstructing it and computes a new trajectory based on this The re-planning takes place through two algorithms that communicate with each other, pathSwitch and informe-dOnlineReplanning, which will be briefly explained in the following paragraphs. See [START_REF] Tonola | Anytime informed path re-planning and optimization for robots in changing environments[END_REF] to get more information and pseudocode.

1) pathSwitch algorithm: pathSwitch algorithm aims to create a path from a node N 1 of the current path to one of the other available paths. As input, it receives the current path, the set of the available paths, and the node N 1 of the current path from which starting to search a new one. It tries to find a connecting path from N 1 to each node N 2 of all the available paths. The solution will be made up of the connecting path and of the subpath from N 2 to the goal, as represented in Figure 2. This search is conducted smartly to reduce the computational requirements of the algorithm and, therefore, the time required for execution. First of all, a connecting path between N 1 and the selected node N 2 is searched if and only if (1) is verified:

N 1 -N 2 < c best -c subpath2 (1)
where c best and c subpath2 are the costs (i.e. the path length) of the best solution found up to now and of the subpath of the selected path from N 2 to the goal, respectively. Until a first solution is found, c best is equal to c subpath1 , the cost of the subpath of the current path from N 1 to the goal (referring to Figure 2). According to informed sampling [START_REF] Gammell | Informed sampling for asymptotically optimal path planning[END_REF], if (1) is verified, a connecting path between N 1 and N 2 is searched inside the following ellipsoid:

X ell = {x ∈ X free | x -N 1 + x -N 2 < c best } (2)
where x is a generic configuration of the robot and X free the set of all collision-free configurations.

2) informedOnlineReplanning algorithm: This algorithm manages the whole re-planning procedure by calling several times pathSwitch and giving it the required inputs. In particular, it enriches the set of available paths adding the portion of the current path that lies beyond the obstacle that is obstructing it; furthermore, it defines the set of free nodes from which pathSwitch will be called. This set comprises all the free nodes of the current path between the robot current configuration and the obstacle. The idea is to start calling pathSwitch from the farthest node from the current configuration to have enough time to find at least a first feasible solution before traveling through that node. Every time pathSwitch is called from a node, the cost of the best available solution found up to now is used in [START_REF] Koppenborg | Effects of movement speed and predictability in human-robot collaboration[END_REF]. The algorithm considers the nodes of the current solution located before the obstacle. When the replanner finds a new solution, the new nodes are added to the searching set to conduct more profound research towards better and better solutions. The algorithm ends when all the nodes have been analyzed or when the computing time exceeds the maximum allowed time. In the path re-planning thread, informedOnlineReplanning is called at each cycle, to continuously optimize the current path in an anytime fashion. The maximum allowed time may vary. When an obstacle blocks the current path, a small amount of time should be given to the algorithm, in order to find quickly a feasible solution; in the next iterations, when the aim is to optimize the current solution, a bigger amount of time can be used by the re-planner. Note that the responsiveness of the replanner is critical to not interrupting the task execution waiting for the replanner output.

B. Reactive speed modulation

Although the main focus of the paper is the replanning approach, a speed modulation strategy is implemented to meet ISO/TS 15066. In this way, the robot is able to avoid collisions even while the replanning is computing the new path. Safety standard defines a relation between the robot velocity and the distance between the manipulator and the human. This relation is an inequality to ensure that the human-robot distance S(t 0 ) is large enough for the robot to stop without colliding with the human. To this purpose, SSM defines the protective distance S p and requires that

S p = v h T r + v r a s + v r T r + v 2 r 2a s +C ≤ S(t 0 ) (3)
where v r is the robot velocity towards the human, v h is the human velocity towards the robot, a s is the maximum Cartesian deceleration of the robot towards the human, T r is the reaction time of the robot, and C is a parameter accounting for the uncertainty of the perception system. Under the assumption of constant Cartesian deceleration, it is possible to derive a maximum robot velocity (towards the human) v max , given the current human-robot relative distance and the human speed:

v max = v 2 h + (a s T r ) 2 -2a s C -S(t 0 ) -a s T r -v h (4)
In order for the robot to respect the safety specifications given in [START_REF]:2016 Robots and robotic devices -Collaborative robots[END_REF], its speed should be monitored and, at each time instant, limited to v max .

To do so, we use a trajectory scaling algorithm that calculates the robot speed override in real-time [START_REF] Faroni | An MPC framework for online motion planning in human-robot collaborative tasks[END_REF]. The trajectory scaling algorithm receives as input the robot nominal velocity qnom and the maximum allowed human-robot relative velocity v max and computes the robot speed scaling factor by solving an optimal control problem: minimize q,s qs qnom 2 [START_REF] Zanchettin | Safety in human-robot collaborative manufacturing environments: Metrics and control[END_REF] subject to v r ≤ v max . Details on the algorithm to solve (5) can be found in [START_REF] Faroni | Predictive joint trajectory scaling for manipulators with kinodynamic constraints[END_REF]. The robot speed scaling factor s is a scalar value that represents the slowdown of the robot due to the human proximity from 0 (robot stop) to 1 (nominal velocity).

IV. EXPERIMENTAL RESULTS

Experimental tests have been performed to evaluate the capabilities of the proposed strategy. The experiments have been executed in the industrial cell of Figure 3, which is composed of a Universal Robots UR10e, a 6 degreeof-freedom collaborative robot, mounted upside down and working on a work-table in front of it.

The proposed method has been implemented in ROS (Robot Operating System) using MoveIt!. The offline planner uses RRT-Connect [START_REF] Kuffner | RRT-Connect: An efficient approach to single-query path planning[END_REF] to compute the set of paths and the less costly one (i.e, the shortest path) is selected as the current path. The vision system uses an Intel Realsense D435 camera with a framerate of 30 Hz. The speed modulation block runs at 500 Hz while the frequency of the path re-planner block depends on the time given to informe-dOnlineReplanning: the maximum allowed time is equal to 100 ms when an obstacle blocks the current path and 150 ms when the aim is to optimize the current path. Table I summarizes the values of the terms of (4) used during the tests.

Ten tests were carried out to compare the proposed framework effectiveness with a standard three-fixed-areas SSM strategy (namely, the robot works at full speed, reduced speed, or stops if it is in a low, medium, or high danger zone) [START_REF] Matthias | Example application of iso/ts 15066 to a collaborative assembly scenario[END_REF].

The tests were executed as follows. At the beggining of each test, a set of four paths is computed. The number of paths affects the performance of the replanner: the more the paths, the better the optimality of the solution could be but the greater the computational complexity. Furthermore, the more paths, the less readability of the approach as the human has to remember several possible paths. A trade off should be identified. Then, the robot moves from a selected start configuration to a selected goal configuration emulating a pick-and-place action, while an operator is doing some task on the workbench present in the cell. When the robot starts moving, the operator approaches the work table and stays for 60 s before leaving. Approaching the table, the operator obstructs the robot path, forcing it to reduce its speed and possibly re-plan a new path. In particular, when the proposed framework is applied, it is expected that the robot reduces its speed and follows a new path that avoids the obstacle, so that the robot can rapidly reach the goal. Instead, during the tests of the three-fixed-areas SSM technique, the presence of the operator in the shared work-space causes a larger reduction of the robot speed or even a robot stopping.

We measure the trajectory execution time from start to goal and the average reduction of the robot speed along the path during ten repetitions of the test.

Figure 7 shows how the re-planner works. The green box represents the operator box occupancy, while the available paths are represented, for simplicity, as coloured lines which connect the waypoints but the robot travels some sort of arc between them. The yellow path of Figure 7a is the result of the optimization of the initial travelled path, the green one. Then, in Figure 7b, the human obstructs the robot's trajectory, so the re-planner finds, firstly, a sub-optimal free path and, then, optimizes it, obtaining the yellow one. Figures 4,5,6 show the results of the tests.

In Figure 4, the boxplots of the execution times needed to complete the task, when the re-planner was active and when the classic SSM was active, are compared. The average time for task execution in the first case is equal to 55.6 s, while in the second case it is equal to 90.5 s. Similarly, the ratio between the execution time and the nominal time to complete the trajectory (when there are no mobile obstacles inside the cell) is computed and shown in Figure 5 (the closer this ratio is to 1, the less the robot slowdown due to the presence of the human compared to the case without the human). This time difference can be well understood by looking at Figure 6. The speed scaling factor is represented as a value between 0 and 100, where 0 means that the robot is stationary while 100 that the robot is moving at the nominal speed, and the average value is calculated for each test. As shown in Figure 6, when the re-planner is running, the average speed scaling is larger than that obtained during the classic SSM technique tests. Thanks to path re-planning, the robot always moves along a free path that keeps it at a greater distance from the operator, and, consequently, it can move at higher speeds.

A video of the experiments is also available in the material attached to this paper.

V. CONCLUSIONS

We have proposed a re-planning framework for Humanrobot collaboration applications to cope with robots and humans simultaneous presence inside a robotic cell. Industrial typical applications use a simple three-fixed-areas speed monitoring and separation technique, which guarantees operator safety but provides an inadequate and inefficient collaboration. The proposed framework exploits an advanced SSM that combines a speed-scaling technique with a replanning algorithm to make the robot more reactive to human movements.

Tests on an industrial cell have been executed to compare the proposed framework with a three-fixed-areas SSM approach, typically adopted in industries. Results show that (a) The robot follows the yellow path, which is the result of the optimization of the initial followed path, the green one. the proposed framework has outperformed industry most common practices.

Results show that the proposed framework makes the robot able to complete its task despite the presence of the human and requiring much less time than the three-fixedareas SSM strategy. In general, the differences between the performance of the two methods depend on the scenario, namely the human and robot tasks, and a broader analysis should therefore be conducted to obtain more general results. Nonetheless, the results draw the conclusion that online replanning combined with speed modulation can significantly enhance efficiency of HRC industrial applications.

Fig. 1 :Fig. 2 :

 12 Fig.1: Proposed planning scheme. The offline planner finds a set of paths from the initial to the desired final configuration and the best solution is execute while the path re-planning module keeps looking for improved solutions. The speed modulation module adapts the robot speed according to ISO/TS 15066.

Fig. 3 :Fig. 4 :Fig. 5 :Fig. 6 :

 3456 Fig. 3: Experimental setup used to test the proposed framework and to compare it with industrial common practice.

  (b) The operator obstructs the robot current path, so the re-planner find a new free path, coloured in yellow.

Fig. 7 :

 7 Fig. 7: Example of how the re-planner works in the industrial cell used during the tests. The green box represents the operator occupancy box. The available paths are represented as coloured lines which connect waypoints, for simplicity, but the robot travels some sort of arc between the waypoints.

TABLE I :

 I Speed scaling module parameters.

					v h (m/s)
	0.2	0.1	0.6	0.3	measured

T r (s)

a s (m/s 2 ) S p (m) C (m)