The modular robots community has developed many different systems throughout the years, however, very few have been tested with a large number of modules. In this article, we present an artistic creation made of 1824 Blinky Blocks . It is the first time a modular robot composed of such a number of modules has been assembled and programmed and this artwork has been certified by a Guinness World Record. We detail the hardware used for setting up this record as well as the software, from the firmware to the application as both aspects are very important to scale up in number of modules.

Self-reconfiguration of modular robots is one of the most challenging problems in the robotics field. The objective is to determine how a set of identical modular robots, with local knowledge of the system and limited capacities, can reorganize themselves into a target topology or shape. The problem has received great interest from the research community giving birth to many centralized and distributed algorithms. However, the lack of comparative study of these algorithms makes it difficult to choose one when faced with a given configuration. In this paper, we present a kind of high-level hybridization approach of these algorithms using a neural network technique. The objective is to propose a centralized pre-processing procedure that allows, according to the self-reconfiguration problem, to determine which algorithm is most suitable. We applied the Neural Network technique to two self-reconfiguration algorithms: C2SR and TBSR. The obtained results show that the machine learning tool succeeds 96.67

Blinky Blocks are cubic modular robots, which communicate with their neighbors through their faces, and change color using LEDs. We previously used sets of Blinky Blocks to display images on the basis of one Blinky Block being one pixel. In this paper, we build a multi-resolution screen with modular robots. This screen benefits from its distributed architecture: being able to work if some nodes fail, and being completely customizable. We propose a hardware architecture and related protocols to attach a 8x8 LED matrix to a Blinky Block. The architecture allows us to build distributed multi-resolution screens by mixing regular and enhanced Blinky Blocks. We demonstrate the usage of our integrated screen through experiments with continuously fed Blinky Blocks as well as an autonomous scrolling.

Lattice-based modular robots are composed of modules arranged on a lattice and forming 3D shapes, if these robots are small enough and many enough, they form a programmable matter. This work proposes a method for optimising data communication times between modules by compressing the data. We have first analysed the communication delay between the end device modules, then a set of recent lossless compression algorithms was tested to select the optimal one to implement with Blinky Block. Based on the results obtained, we propose to add a lossless data compression scheme to reduce the communicated data size and consequently communication delay. We found that the "Brotli" compression algorithm is the most suitable one for modular robot communication as it achieved a good balance between computing and communication overhead. Then, based on the compression ratio and the communication delay interpolation, a significant gain is achieved by reducing the communication delay by a factor of 5.

Localizing each module in a modular self-reconfigurable robot (MSR) is of paramount importance. In MSR, the communication graph is directly mapped to the real topology which makes the localization problem easy to solve. However, some types of connectors can lose the orientation of the modules, making the problem intractable. In this work, we propose to build a coordinate system for 3D lattice-based modular robots using a multiagent system. We present DisCo algorithm, that uses one agent per module which can only communicate with its connected neighbors and that does not need a central coordination system. We show that the agents can tackle any kinds of 3D lattice and we illustrate it with a Face Centered Cubic lattice (12 neighbors) and a cubic lattice (6 neighbors). Using communications and only four states, DisCo can also deduce the orientation of modules if the connectors do not provide this information.

While Modular Robots Systems (MRSs) are getting more popular, the challenge of effective self-reconfiguration of the robots persists. Actually, many distributed algorithms were proposed for the Modular Robots Self-Reconfiguration problem (MRSR), however, the efficiency of each algorithm according to the different cases is rarely studied. In a precedent work, we proved the relevance of using an Artificial Neural Network (ANN) component to select the most adapted distributed algorithm according to the current scenario. The proposed approach suffers, as all the other approaches in the literature, ignore the state of charge of the robots. Consequently, an MRSR algorithm may not achieve the MRS reconfiguration if the robots’ batteries are exhausted before the end of the algorithm. In this paper, we propose an ANN system that selects the most suited MRSR algorithm according to the MRSR scenario and the initial state of charge of the robots. The results of the tests show an accuracy of 97%.

In this paper, we present a novel approach to enhance data collection in Vehicular Ad-Hoc NETworks (VANETs). VANETs are a growing area of interest due to their unique characteristics and challenges, such as rapidly changing topology and frequent network disruptions. Efficient data collection is a critical issue in vehicular networks and has therefore become a focus of research. To address this challenge, we propose a stable clustering optimization solution based on adaptive multiple metrics. The cluster head selection is done based on both mobility metrics, such as position and relative speed, and Quality of Service (QoS) metrics, such as neighborhood degree and link quality. The proposed solution has been tested and evaluated through simulations using a vehicular mobility simulator in a realistic urban environment. The results show that the proposed approach provides more stable clusters with higher QoS, and allows for the selection of the appropriate cluster head to collect data from the vehicles and forward it to the destination.

Modular Robots Self Reconfiguration (MRSR) is one of the most challenging problems in nowadays robotics field. This problem consists in the determination of how a set of identical modular robots, with local knowledge of the system and limited energy and computational capacities, can reorganize themselves into a target topology or shape. MRSR has received great attention from the research community. Therefore, a lot of centralized and decentralized algorithms were designed to answer this problem. Unfortunately, the analysis of why and when an algorithm is better than another is less studied. In this paper, we proposed a hybrid centralized/distributed modular robots reconfiguration approach. In this approach, a convolution neural network system is used to estimate the most adapted distributed reconfiguration algorithm according to the initial shape formed by the modular robots and the target shape. Two distributed algorithms are studied: C2SR and TBSR. The designed CNN model allows determining which option is the best for a given reconfiguration problem: use of the C2SR algorithm, use of the TBSR algorithm, or both algorithms are equivalent. The obtained results show that the ML tool succeeds 97.25% of the time to determine the suitable algorithm based on the initial and the final shapes. In addition, the system can be extended to any number of algorithms. Our contribution is the production of a neural network built for the selection of the best modular robots self-reconfiguration algorithm.

The massive machine-type communication will be at the core of ambient connectivity, requiring for energy-efficient systems. Earlier studies highlighted the efficiency of positioning approaches based on channel-state information (CSI) in different environments. Many works limited the solution assessment to a single room in a fully indoor testbed. This article extends the application of CSI for indoor–outdoor detection on an unprecedented large area and considers mMTC-oriented long-term evolution and fifth-generation Internet of Things in the sub-GHz frequency band. Hinged on a novel long-term evolution protocol dedicated for machine-type communications, the results focus on a unique packet exchange with a single access point to save battery life and simplify deployment. The study evaluates different input features and investigates the target positioning accuracy for multiple unsupervised and supervised dimensionality reduction methods. We present a new dimension reduction scheme consisting of an unsupervised funnel on top of a supervised dimension reduction approach. Results show that the introduced Funnel Fukunaga–Koontz transform outperforms other dimension reduction approaches, regardless of the input features and the number of locations.

Modular robot network architectures are experiencing growing popularity. The problem of automatically reconfiguring a set of modular robots into a given target shape presents a real challenge to distributed computing.Many works on the subject restrict the nature of the constructed target forms. The bolder approaches focus on reducing the number of overall required movements. In this work, we propose a distributed asynchronous self-reconfiguration algorithm allowing to distribute the effort made by each robot to reach the final shape. This makes it possible to extend the life of the network of micro-robots.We compare our TBSR algorithm with C2SR self-reconfiguration algorithm using VisibleSim simulator. The obtained results show that globally TBSR outperforms C2SR except for rare cases. The TBSR algorithm allows reducing the number of required moves up to 17%. Besides, the ability of TBSR to balance the number of moves over the modular robots makes that the maximum number of moves per robot is reduced up to 40%.

The self-reconfiguration of large swarms of modular robotic units from one object into another is an intricate problem whose critical parameter that must be optimized is the time required to perform a transformation. Various optimizations methods have been proposed to accelerate transformations, as well as techniques to engineer the shape itself, such as scaffolding which creates an internal object structure filled with holes for easing the motion of modules. In this paper, we propose a novel deterministic and distributed method for rapidly constructing the scaffold of an object from an organized reserve of modules placed underneath the reconfiguration scene. This innovative scaffold design is parameterizable and has a face-centered-cubic lattice structure made from our rotating-only micro-modules. Our method operates at two levels of planning, scheduling the construction of components of the scaffold to avoid deadlocks at one level, and handling the navigation of modules and their coordination to avoid collisions in the other. We provide an analysis of the method and perform simulations on shapes with an increasing level of intricacy to show that our method has a reconfiguration time complexity of O(N3) time steps for a subclass of convex shapes, with N the number of modules in the shape. We then proceed to explain how our solution can be further extended to any shape.

Distributed self-reconfiguration in large-scale modular robots is a slow process and increasing its speed a major challenge. In this article, we propose an improved and asynchronous version of a previously proposed distributed self-reconfiguration algorithm to build a parametric scaffolding structure. This scaffold can then be coated to form the desired final object. The scaffolding is built through a continuous feeding of modules into the growing shape from an underneath reserve of modules which shows a reconfiguration time improved by a factor of 3√N compared to the previous and synchronous version of the algorithm, therefore attaining an O(N 1/3 ) reconfiguration time, with N the number of modules in the system. Our algorithm uses a local motion coordination algorithm and pipelining techniques to ensure that modules can traverse the structure without collisions or creating deadlocks. Last but not least, our algorithm manages uncertainty in the motion duration of modules without negatively impacting reconfiguration time.

Cloud computing technology hosts application for users to accesses computing as services. Its application has been widely used and increases to become a part of enterprises' computing infrastructures. However, Cloud computing latency and request deadline fulfillment issues are among the major problems. Therefore, there is a need for a solution to synchronize a request states in Cloud computing. In this paper, the resource allocation and scheduling problem for services in the cloud are addressed and a solution model is presented. Our approach is based on MAX-Plus algebra, to provide a deterministic and exact solution to the minimization of services queries response times. Our proposal is tested on various problems sizes to evaluate its performance and scalability.

La mobilite intelligente dans le contexte urbain connait depuis ces dernieres annees une expansion fulgurante. L’augmentation simultanee des flux de transport de personnes et de marchandises pousse les decideurs a trouver des solutions pour lutter contre plusieurs phenomenes propres au contexte urbain : congestion reguliere, stationnement indisponible, pollution environnementale et sonore, cout important du dernier kilometre. Les collectivites urbaines doivent reorganiser les infrastructures pour s’adapter en consequence aux besoins. On assiste ainsi a l’emergence de nombreux services urbains comme le co-voiturage dynamique, le transport a la demande, les vehicules « libre-service », le stationnement intelligent ou encore les centres de distribution urbains. Mais les challenges sont encore nombreux pour exploiter au mieux les divers modes disponibles afin de repondre aux besoins des usagers. L’UTBM (equipe OPERA) et l’UFC (Institut FEMTO-ST) ont decide de mutualiser leurs competences dans le projet MOBILITECH MISC : Mobility in Smart Cities, finance par la Region Bourgogne Franche-Comte, ainsi que par l’Etat dans le cadre du projet Systemes Communicants pour la Mobilite (CPER 2015-2020). Labellise par le Pole Vehicule du Futur, le projet consiste a developper la multimodalite au travers d’une plateforme d’aide a la mobilite urbaine des personnes et des biens. Il s’appuie sur le developpement des nouvelles techniques de communication qui permettent de collecter et d’exploiter des donnees a moindre cout, offrant des leviers strategiques decisifs : en favorisant la cooperation des acteurs logistiques (consolidation des flux, mutualisation des ressources), en assurant une tracabilite de bout en bout grâce a l’association des technologies de geolocalisation et de l’informatique embarquee (IoT, intelligence artificielle), enfin en optimisant la qualite de service selon les objectifs definis par les utilisateurs.

Multimedia Broadcast Multicast Service system supports efficient diffusion of multicast multimedia services in cellular networks. Our previous work shows that the radio resource management problem for MBMS can be modeled as a combinatorial optimization problem which tries to find optimal assignment of power and channel codes [1]. In this paper, we propose to solve such problem by using metaheuristic algorithm: Tabu Search (TS). In our work, we modify the general TS algorithm and map it onto our model. We also extend the classic TS procedure by proposing a tabu repair mechanism, which helps to explore new candidate solutions. The proposed algorithm is compared with two other metaheuristics: Greedy Local Search (GLS) and Simulated Annealing (SA). Simulations show that, within acceptable amount of time, TS can find better solution than GLS and SA.

Multimedia Broadcast Multicast Service (MBMS) system supports efficient diffusion of multicast multimedia services in cellular networks. Our previous work shows that the radio resource management (RRM) problem for MBMS can be modeled as an optimization problem which tries to find optimum assignment solution of power and channel codes in a given search space [1]. In this paper, based on the proposed model, we design a resource assignment approach by using the tabu search (TS) algorithm. Based on the model characteristics, we define three tabu memory structures and evaluate their search performance. We also extend the classic TS by proposing a tabu repair mechanism, which helps to avoid local optimum and improve the search efficiency. Simulation results show that the proposed TS algorithm outperforms the existing algorithms.

In this paper we present a mathematical modeling of Radio Resource Management (RRM) for multicast service diffusion based on Multimedia Broadcast Multicast Service (MBMS) standard. In this model, a flexible allocation approach named F2R2M is proposed, combining three candidate transport channels with scalable video transmission technology. The allocation procedure is implemented based on simulated annealing algorithm with a two- dimensional optimization objective and lexicographic order evaluation criteria. Experiments prove that, comparing with existing channel allocation approaches, F2R2M obtains allocation solution with equal QoS and lower transmission power consumption. Moreover, it reduces the possibility of achieving saturation of power or channelization codes when simulation scenarios have more users and heavy traffic load.