Roombots extended: Challenges in the next generation of self-reconfigurable modular robots and their application in adaptive and assistive furniture

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S. Hauser a 1, M. Mutlu a b 1, P.-A. Léziart a, H. Khodr a, A. Bernardino b, A.J. Ijspeert a

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https://doi.org/10.1016/j.robot.2020.103467Get rights and content

Highlights

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Depiction of how modular robots could be integrated into furniture in our living space.

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Definition of five key-tasks of adaptive and assistive furniture.

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Development of a dedicated GUI for an easy control of multiple Roombots (RB) modules.

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Hardware demonstrations of various sub-tasks involving up to 12 modules.

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Demonstrations of mobility, manipulation and human-module-interaction.

Abstract

This work presents a series of demonstrations of our self-reconfigurable modular robots (SRMR) “Roombots” in the context of adaptive and assistive furniture. In literature, simulations are often ahead of what currently can be demonstrated in hardware with such systems due to significant challenges in transferring them to the real world. Here, we describe how Roombots tackled these difficulties in real hardware and focus qualitatively on selected hardware experiments rather than on quantitative measurements (in hardware and simulation) to showcase the many possibilities of an SRMR. We envision Roombots to be used in our living space and define five key tasks that such a system must possess. Consequently, we demonstrate these tasks, including self-reconfiguration with 12 modules (36 Degrees of Freedom), autonomously moving furniture, object manipulation and gripping capabilities, human-module-interaction and the development of an easy-to-use user interface. We conclude with the remaining challenges and point out possible directions of research for the future of adaptive and assistive furniture with Roombots.

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Introduction

Modular robots (MRs) are the “Swiss army knives” of robotics. Instead of designing a specialized robot for a single task, the idea of modular robotics in general is to provide functionalities in robotic “modules”, and a robot can be constructed out of these modules. The advertised advantages of modular robots are threefold (based on [1]):

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versatility: different tasks require different functionalities; in a modular robot, these functionalities can be put together by assembling the according modules.

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robustness: if a module fails, only the functionality of this specific module is affected; the rest of the robot still can operate normally and the failing module can easily be replaced.

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low-cost: since each module is thought to only perform a simple function, it is in principle also simple in design, allowing cheap manufacturing; additionally, modules can more easily be mass-produced for wide availability, helping to reduce module costs.

There is an ongoing discussion if the current state of modular robots yet delivers any of these advantages: it can be challenging to realize a function in a compound of modules; more single robotic agents means more potential for failure; and most modular systems are still in their prototype stage, making them expensive to produce. Only the future development of the modular robotics field in general will show if the concept is able to overcome current limitations with more research, new ideas and new technologies.

Among modular robots, self-reconfigurable modular robots (SRMRs) form a special class. Not only are the modules now reconfigurable, meaning they can be assembled into different morphologies, but the system has the ability to perform the reconfiguration autonomously. This could potentially make such a system extremely independent since it could transform its morphology and add and remove functionalities all by itself, leading to an uncomfortable question: what would be the limitations of such a system? This inspired many fictional scenarios over the years, most recently in the Walt Disney movie “Big Hero 6” (2014) where the tiny “microbots” are shown to have almost omnipotent powers when working together in clusters of many thousands of modules.

As physical robots have to deal with real world effects however, namely gravity and other physical effects, such scenarios are far from being realistic and will remain fiction for the foreseeable future. Nevertheless, research on SRMR retains a futuristic touch, and in practice SRMRs pose interesting interdisciplinary challenges in hardware, control and their application for an end user.

The hardware presents an engineering challenge. The general idea of modular robotics is to combine “simple” modules into larger, more complex and thus usually more capable structures that possess the required functionality of a task at hand. A true reversible modular robotic system (meaning no permanent state-change) has three key functionalities: reversible connection between two modules, reversible disconnection between two modules, and actuation (either within one module or in a structure as a whole). Note that reversible connection does not automatically include disconnection as there can be different mechanical systems for both connection and disconnection. For the design of each of these systems, the overall dimensions of a module should be defined, which often are linked to the expected tasks. Lastly, one has to consider if all the sub-units are exact copies of each other (homogeneous MR) or if specialized units collaborate together (heterogeneous MR) [2], which influences the type of control and planning algorithms suitable for the system. In manually reconfigurable modular robot systems [2], the connection and disconnection procedures are outsourced to a human such that an MR mainly has to care only about the actuation system. This means that each module can take on almost any form with any mechanical properties, including soft modules. In contrast, SRMR [2] systems have to perform the connection and disconnection procedures autonomously. If modules are required to attach to specific locations to connect to another module, this introduces additional constraints on the rigidity. To enable (or at the very least simplify) self-reconfiguration, each module should be structurally rigid such that some form of alignment between modules is achievable – a usually necessary condition for self-connection – yet provide some form of movement to initiate self-reconfiguration, often requiring a delicate balance between the two. It is also possible to design modules that can attach anywhere on another module (as e.g. in [3]). Moreover, soft modules (e.g. [4], [5]) can have their own form of connection and disconnection methods. The property of self-reconfiguration is already challenging to achieve in 2D SRMR systems and adds a significant level of difficulty in 3D SRMR systems.

The control requires to deal with distributed systems. Multiple control frameworks can be studied on SRMRs. In a centralized way, either an external computation unit commands all modules or one of the modules can have the master role to command all other modules in the system. Alternatively, each module can act according to their own local perception in the decentralized control. Additionally, a way for a human to intuitively interact with one or more groups of modules may have to be developed such that the current state of the modular system can be understood and a future desired state can be communicated.

Lastly, one has to consider for which application the robotic platform is to be developed. Even though SRMR can shape-shift into various morphologies, this does not necessarily mean that one module should be as small as possible to build any arbitrary morphology; one module should be as small as the smallest unit required by the application. We must also define the added value of self-reconfiguration to justify the effort to overcome the challenges mentioned above: what is the utility of self-reconfiguration?

As one example that aims at addressing some of these challenges, over the last decade we developed “Roombots” (RB), a homogeneous 3D self-reconfigurable modular robot system with the application – among others – of serving as adaptive and assistive furniture. In this framework, multiple groups of modules adapt to the needs of a user by creating different pieces of furniture or augmenting the capabilities of existing furniture. Additionally, such pieces of furniture can also act somewhat autonomously and provide assistance specifically to the elderly by for example preventing falls as a result of a following chair, picking up and holding objects, and bringing items, all while retaining their functionality as furniture.

The first generation of Roombots proposed the general hardware design of our SRMR [6], [7], [8] together with algorithmic work on reconfiguration [9], [10], [11], locomotion [12], [13], [14], [15], and user interfaces [16], [17]. As a proof-of-concept, only a limited number of modules was needed to demonstrate the three key functions connection, disconnection and movement. However, to further the vision of using Roombots as adaptive furniture, more modules were needed and a few adaptations in hardware and control had to be made based on the experience with the developed prototypes.

To showcase some of the new capabilities of the upgraded Roombots system, we propose five tasks that we regard as core functionalities of adaptive and assistive furniture: self-reconfiguration, mobility, manipulation, human-module-interaction, and developing a user interface. Each of these tasks is demonstrated by Roombots, often through various sub-tasks (see Fig. 1).

The first task described herein is scalable self-reconfiguration which stands at the core of an SRMR system and fits well into the vision of creating adaptive furniture. For this demonstration, a loose group of RB modules self-reconfigures into a shape resembling that of a common piece of furniture. It should be noted that while this task essentially reduces to a series of connection, disconnection and movement actions which already have been demonstrated in e.g. [10], performing it with a larger number of modules is not trivial due to the scalability of the robotic system: continuous reliable reconfiguration with a group of modules rather than an isolated reconfiguration with two modules adds significant challenges in hardware and control to deal with real world physical phenomena that only occur for larger groups of modules. In the case of Roombots, the first hardware iteration experienced major elastic deformations and misalignment issues such that the autonomous formation of larger structures was not possible. Due to the various hardware modifications presented in Section 3.1 and closed-loop control described in Section 3.8, such tasks lie now within or at least much closer to the capabilities of the present hardware iteration.

Second, adaptive furniture should possess a certain degree of mobility such that pieces of furniture can dynamically move within a living space. Depending on the functionality of the structure, mobility can require certain adaptation of the furniture to the environment as well as autonomy to react to changes in the environment. We performed a set of demonstrations to showcase the mobility aspect, ranging from following and evading pieces of furniture to slope compensation and even stair climbing.

To fulfill more of the assistive aspects of the Roombots vision, robotic-enhanced furniture must possess a way to manipulate objects. Simple actions such as holding a book can already provide assistance, and it is clear that many of such actions require a method for manipulation, i.e. a gripper. Such a gripper can, for instance, be used for fetching a remote controller fallen on the ground. For the integration of a gripper into the RB framework – which should be able to manipulate a wide range of everyday objects – we revisited the concept of granular jamming that was used to develop a “Universal Gripper” [18]. By miniaturizing the mechatronic components, we were able to equip selected modules with this gripper and demonstrate common tasks such as a piece of furniture picking up an object from the ground and an RB structure helping to open a water bottle. Two gripper modules further are able to pass objects from one module to another, which will also be briefly discussed.

Users need an intuitive way of interacting with modules. Interaction could be through e.g. speech recognition where modules react to spoken commands, or through gesture control as e.g. in [17], [19], [20] where commands are created through the tracking of gestures. Further, modules should provide feedback to the environment such that the current state of a single module or a group of modules can easily be understood. A simple way to achieve this is through light where different colors can be assigned to different states. We revisited our approaches in the past and demonstrate again the interaction capabilities of RB.

Lastly, when complete information about the state of the modules is required, an appropriate visualization tool is needed. We describe the development of a Graphical User Interface (GUI) that allows for easy control of RB modules. The GUI greatly facilitated the development of many of the presented demonstrations and will simplify the development of future challenges. Such challenges (e.g. RB metamodule locomotion on-grid) are identified along with this work and analyzed and discussed in Section 5.

It is important to note that these five tasks are chosen by us and adapted in ways such that they can be demonstrated by Roombots, i.e. the actual task allocation is done by a human operator. It will be the work of future research to automatically extract tasks from specific conditions and allocate it to a set of modules; this is out of scope in the current work.

The main contribution of this work is showcasing hardware and control improvements of Roombots since [10]. Moreover, the demonstration of a variety of tasks can serve as a benchmark for the possibilities of such an SRMR, which we believe is so far absent in the community. The paper provides a framework to put all published works and the improvements on Roombots in this work in relation to adaptive and assistive furniture. Considerable hardware and control engineering work was needed to achieve such demonstrations, which would go unnoticed otherwise. Hence, these improvements are described in detail, and the paper comprises of multiple small topics. Another contribution of this paper is explicitly explaining the limitations of Roombots by giving examples and demonstrations as we believe that pointing out shortcomings is as important as explaining the potential benefits of SRMRs. Note that, unlike most other articles in SRMR by others and by us, this article is qualitative rather than quantitative. In particular, it does not provide many quantitative results, but only qualitative descriptions of proof-of-concept experiments as well as assessments of pros and cons of our Roombots. We hope that the article will still be useful to share our vision, our experience, and possible insights about future improvements in the fields of SRMR applied to adaptive and assistive furniture.

The manuscript is structured as follows. It first details the hardware and software modifications of the newest generation of Roombots and then reports significant demonstrations in hardware of selected scenarios. It then concludes with the limitations of the current platform and with a general discussion of challenges of self-reconfigurable modular robot systems.

Section snippets

Related work

Over the years, a large variety of 2D and 3D proof-of-concept SRMR systems has been developed. A comprehensive list of both mechanical designs and algorithms of SRMR can be found in [2] and [21]. If research includes experiments with prototype hardware modules, the basic functions of connection and disconnection are in many cases shown with a minimal number of modules, often in an isolated experiment involving two to five modules. Larger reconfigurations with more modules are usually shown in

Material and methods

The following subsections describe the different modifications of the RB hardware and software in detail: the adaptations on the module itself, the added sensors and the new control possibilities, the new gripper, added electronics, the Graphical User Interface (GUI), the external RGB-D camera and the passive parts (furniture).

Experimental results

To demonstrate the augmented capabilities of the modified RB system, we designed five main tasks which are demonstrated in various sub-tasks. The results of these experiments are presented in this section and the link to the supplementary video including all described experiments can be found in Table A.1.

Demonstrations of self-reconfiguration and adaptive/assistive furniture

The presented hardware demonstrations showcase the capabilities of the upgraded Roombots modules. We are aware that the outcomes of the demonstrations are rather of qualitative than of quantitative nature as this work aims at exploring a potential use of (SR)MR for adaptive and assistive furniture. In this context, we successfully presented proof-of-concept demonstrations of RB modules completing a large and complex self-reconfiguration task involving a significant number of autonomous

Conclusion

In this work, we presented the capabilities of the latest generation of our self-reconfigurable modular robotic system “Roombots”. We outlined five key tasks that we consider relevant to the vision of modular robotics for adaptive and assistive furniture and were able to successfully demonstrate various sub-tasks in hardware. This required various design improvements – especially in the mechanics of the modules – which are described in detail.

Concerning the demonstrations, modules performed a

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

We gratefully acknowledge the help of Ahmet Safa Öztürk, Stéphane Bussier, Quentin Golay and Théo-Tim Denisart for their contribution to the Roombots project. Special thanks to Jérémy Blatter for the development of the GUI. Further, we are enormously thankful to François Longchamp for his technical support in the design of the modules.

This project is funded by the Swiss National Science Foundation (Project 200021_153299) and the Fundação para a Ciência e Tecnologia (FCT) agency of Ministry for

Simon Hauser has a B.Sc. (2011) and M.Sc. (2014) in Mechanical Engineering from the Swiss Federal Institute of Technology in Zurich (ETHZ). He received his Ph.D. in Robotics, Control and Intelligent Systems in 2019 from the Swiss Federal Institute of Technology in Lausanne (EPFL) where he conducted research on modular robotics and legged locomotion in the Biorobotics Laboratory (BioRob). He recently joined the Bio-Inspired Robotics Lab (BIRL) at the University of Cambridge as a postdoctoral

Roombots: A hardware perspective on 3D self-reconfiguration and locomotion with a homogeneous modular robot

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A. Spröwitz, R. Moeckel, M. Vespignani, S. Bonardi, A.J. Ijspeert

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https://doi.org/10.1016/j.robot.2013.08.011Get rights and content

Highlights

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We designed, implemented, and tested the Roombots (RB) modular robots.

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We explain the RB design methodology: active connection mechanism and module.

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RB use locomotion on-grid (lattice-based environment), and off-grid locomotion.

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RB join into metamodules on-grid, and can transit from off-grid to on-grid.

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We demonstrate RB overcoming concave and convex edges, and climbing vertical walls.

Abstract

In this work we provide hands-on experience on designing and testing a self-reconfiguring modular robotic system, Roombots (RB), to be used among others for adaptive furniture. In the long term, we envision that RB can be used to create sets of furniture, such as stools, chairs and tables that can move in their environment and that change shape and functionality during the day. In this article, we present the first, incremental results towards that long term vision. We demonstrate locomotion and reconfiguration of single and metamodule RB over 3D surfaces, in a structured environment equipped with embedded connection ports. RB assemblies can move around in non-structured environments, by using rotational or wheel-like locomotion. We show a proof of concept for transferring a Roombots metamodule (two in-series coupled RB modules) from the non-structured environment back into the structured grid, by aligning the RB metamodule in an entrapment mechanism. Finally, we analyze the remaining challenges to master the full Roombots scenario, and discuss the impact on future Roombots hardware.

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Introduction

We are working towards the idea of a living environment where classic roomware components are merged and enhanced by elements from robotics and information technology. We tackle this task by designing Roombots (RB), an instance of self-reconfiguring modular robots (SR-MR) which are meant to be embedded into our living environment, to build intelligent furniture and other components of our daily life. Self-reconfiguring modular robots are highly integrated, self-sustaining robotic building blocks with limited degrees of freedom (DOF). A number of them can connect to each other with active connection mechanisms (ACM), creating a robot or structure with more capabilities than a single module. Eventually, we see the task of RB to build adaptive furniture that connects into different shapes, such as stools, tables, or sofas. It should also adapt to the user’s needs, by changing shape over time, and by moving around.

Several designs of self-reconfiguring modular robotic systems have been proposed, with the goal to assemble larger structures [1], [2]. Such structures could be complex assemblies, such as parts of a space station, remote, hard-to-reach and hard-to-maintain systems such as a modular satellite, deep-sea underwater structures, or scaffolds. For the Roombots project, we are aiming to assemble furniture-like structures. For our furniture, we want to minimize the number of active robotic modules by combining active modules and passive elements—a table assembled from light-weight elements and a few Roombots modules would be by far cheaper, less heavy and structurally more sound than one made entirely from modular robots. Currently, we envision a mix of Roombots modules and passive elements, as depicted in Fig. 1. Light-weight elements of several shapes and Roombots modules would create a patchwork, held together by Roombots modules. Connector “ports” play an important role in this scenario; these are distributed around the room as passive connectors in the floor, walls, ceilings, and within the light-weight elements. The same ports are also part of Roombots modules, as its active and passive connectors.

Towards this future goal, we tackle and present experimental results for five sub-scenarios in this work. Firstly, we imagine a stock of Roombots modules on the side, and modules detaching from it. Not all the floor will be equipped with connector ports. To reach a port-equipped area (“on-grid”), we show how pair-wise connected Roombots metamodules (MM) locomote with oscillating or wheel-like rotating motion patterns over the floor (“off-grid” locomotion, Fig. 1, subtask 1). Module locomotion is controlled by a central pattern generator (CPG)  [3] implemented as a network of coupled oscillators, distributed in the different modules. CPG are decentralized and well suited to organize rhythmic and non-rhythmic motions of large numbers of modular robots.

Next, modules have to become aligned to the grid. We propose a passive mechanism based on an entrapment-like structure (Fig. 1, subtask 2): Roombots metamodules slide into a sink-like entrapment mechanism (EM). In there the MM automatically aligns, the MM’s ACM grippers connect to a EM wall port. With a couple of predefined joint moves the MM leaves the EM, and “brachiates” over the grid connection ports  [4], [5] (simulation results). Roombots modules can perform on-grid locomotion by iteratively attaching and detaching to the ports. In some cases, modules fail to attach when controlled in open-loop because of excessive bending of the structure, and necessary hardware updates for this task are discussed in Section  5.

To build a larger structure from modules and passive elements, Roombots modules will have to approach, climb, and overcome walls and planes. Those obstacles present themselves either as “concave” edges, planes, or “convex” edges. Single Roombots modules are designed and controlled to overcome concave edges (Fig. 1, subtask 3), and to switch from a horizontal plane to a vertical plane. To go upwards and switch to the next-level horizontal plane a single Roombots module has to be merged into a full Roombots metamodule, together with a Roombots “helper module” (Fig. 1, subtask 4).

Roombots are meant to assemble larger structures from passive elements. In subtask 5 we present a brief first step towards handling light-weight structures. In fact, the future Roombots hardware will require a stiffness and power upgrade before being able to handle larger elements, as depicted on the left side of the figure. For now, two Roombots modules are picking up a pair of light-weight connector plates (Fig. 1, similar to 5). For the future of this project, remaining challenges will include the tasks of picking up light-weight elements by cooperating Roombots metamodules, transporting those elements to their assembly point (Fig. 1, two metamodules lifting a light-weight element to the table top), and finally mounting and assembling everything into meaningful structures. In this work we focus at the defined five subtasks and the necessary modular robot hardware to robustly solve these scenarios. Remaining challenges (e.g. multi-metamodule handling of passive elements, RB metamodule locomotion on-grid) are identified along with this work, and are analyzed and discussed in Section  5.

The paper is structured as follows: Section  2 gives a brief overview over modular robot (MR) classifications, and significant MR implementations. In Section  3 we present the mechanical, electrical, and control design of Roombots. In Section  4, we show five hardware experiments of reconfiguration and locomotion on-grid and off-grid, in 2D and in 3D, alignment of RB from off-grid to on-grid, and handling of light-weight elements through RB modules. We discuss our results and future work in Section  5, and conclude the paper in Section  6.

Section snippets

Related work

In this section, we survey and analyze a selection of modular robots, with a focus on module density, weight, and classification of modular robots. The background of this survey is the Roombots scenario, and the core tasks of RB modules (a) to attach and detach robustly with other RB modules and the environment, (b) to locomote with brachiating movements through the surrounding on-grid environment and move in the off-grid environment and (c) to handle other RB modules and light-weight elements.

Material and methods

This section gives details of Roombots’ mechanical design (Section  3.1). This includes its active connection mechanism (ACM), actuator design, and shell and grid design. Section  3.2 presents Roombots’ electrical components. Section  3.3 describes control schemes for distributed locomotion, and reconfiguration.

Experimental results

To demonstrate the capabilities of the RB system, we conducted five main experiments. The results are presented in this section. Video including all experiments is given below:

Discussion and future work

The presented hardware experiments show the capabilities of the current Roombots generation. These experiments also reveal the remaining challenges for the full, envisioned Roombots scenario. We successfully demonstrated that RB modules autonomously move on a 2D grid, overcome concave corners, form metamodules, and cooperate to overcome convex corners in a structured 3D-grid environment. We presented a proof-of-concept showing how RB modules can handle passive, light-weight parts. An entrapment

Conclusion

In this work we presented the design of the Roombots (RB) self-reconfiguring modular robotic system. In hardware experiments, we demonstrated locomotion and reconfiguration of single RB modules and RB metamodules (two modules connected in-series) in a 3D structured grid environment. Modules performed on-grid locomotion by “brachiating” along connector ports, overcame concave and convex edges, and climbed up structured walls. RB assemblies also locomoted off-grid, in the non-structured

Acknowledgments

We gratefully acknowledge the help of Soha Pouya, Ebru Aidin, Manon Picard, Alexandre Tuleu, Alessandro Crespi, Christophe Chariot, Jocelyn Lotfi, Simon Lepine, Emilie Badri, Philippe Laprade, Thanh-Khai Dinh, Anh The Nguyen, Frédéric Wilhelm, Manuel Stöckli, Yurij Perov, Efthymios Stavridis, Peter Loeppelmann, Jesse van den Kieboom, and Mikaël Mayer for the Roombots project. We gratefully acknowledge the technical support of André Guignard, André Badertscher, Philippe Vosseler, Mitchell

Alexander Spröwitz is a post-doctoral researcher. He worked until 2013 at the Biorobotics Laboratory, EPFL, Switzerland. He has a “Vordiplom” (B.Sc.) in Mechanical Engineering, and a “Diplom” (M.Sc.) in Biomechatronics from the University of Ilmenau, Germany, and a Ph.D. in Manufacturing Systems and Robotics from EPFL, Switzerland. His research includes design and control of self-reconfiguring modular robots and bipedal and quadrupedal legged robots, and biomechanics of legged robotic and

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