Modular robots adapt to complex tasks through self-reconfiguration but often lack global geometric knowledge for efficient shape analysis. We propose a fully distributed message-passing algorithm for border tracing in lattice-based modular robots that requires no centralized memory. The algorithm has two phases: first, it identifies all internal and external borders and builds communication bridges via shortest-path spanning trees; second, it enables any module to traverse the robot’s boundaries using these bridges. Simulations on different configurations demonstrate that the algorithm is scalable, with linear time complexity, and supports efficient border traversal for distributed geometric reasoning.

Navigating dynamic environments is a fundamental challenge in distributed robotic systems, particularly when faults occur within the system itself, resulting in a changing connectivity graph. Classical graph search algorithms such as A* provide optimal paths as long as the graph is static. However, faults are a part of real life applications and cannot be ignored; classical approaches scale poorly in such scenarios because updating the graph topology requires extensive inter-robot communication, recombination of local maps, and replanning. This paper proposes a reinforcement learning (RL)-based approach that enables agents to learn navigation policies without requiring global knowledge of the graph. Each agent observes only its immediate neighborhood, making locally reasonable decisions about navigating toward a target location that collectively achieve near-optimal global performance. Through training on randomly chosen faults, our model learns robust traversal behaviors that adapt online to topology changes, reducing communication overhead compared to a more basic A*-based approach in a faulty environment.

Self-reconfigurable modular robotic systems require reliable mechanisms to detect structural failures that may compromise successful reconfiguration. In this work, we propose a sensor fusion approach for real-time breakage detection in modular robot-based programmable matter. Force-sensitive resistors (FSRs) and inertial measurement units (IMUs) were integrated on selected modules to monitor lateral strain and tilt during reconfiguration. Their outputs were processed by a fuzzy inference system running on a stationary processing unit to estimate structural stability. The approach was experimentally validated using a cantilever configuration built with Blinky Blocks, where progressive loading was simulated by sequentially adding blocks. The fusion system successfully identified pre-failure conditions, providing early warnings prior to mechanical breakage. This sensor-based method offers a complementary safety mechanism to existing model-based mechanical failure detection algorithms, enabling real-time failure detection based on direct physical measurements.

Distributed modular robotics systems, combined with a distributed communication-based algorithm, can produce self-reconfigurable programmable matter that can change the shape of objects on demand. A Digital Twin is a powerful way of connecting the digital and real worlds by creating and managing the same concepts in both environments. This connection can be used by both worlds to detect faults in the real world or to access vast resources from the digital world that are necessary for planning self-reconfiguration. In this paper, we present a Digital Twin that mirrors the physical system based on Blinky Blocks in real-time, reconstructs the full configuration from local interactions, and tracks events such as block additions, removals, and color changes. This provides a coherent global view, enabling safe testing, system-level monitoring, and a foundation for AI-driven reconfiguration and fault recovery.

In this paper, we present a fully distributed method that allows a group of magnetically connected modular robots to assess the mechanical stability of their structure. Stability is verified based on four mechanical phenomena: vertical and rotational sliding and rotational debonding for vertical and lateral connectors. We present a mechanical model applicable to the Blinky Blocks robot system. We also propose an efficient, fully distributed algorithm that runs simultaneously on all robots, enabling them to check global stability and detect the type and the breakage positions. Our algorithmic solution avoids the global resolution of the system and is effective for both free-loop and complex loop or multi-loop configurations; for the latter, it systematically enumerates all possible spanning trees and traverses each one to assess the stability of the modular structure. The approach was validated both in simulation with VisibleSim and on real Blinky Blocks hardware. Experiments demonstrate accurate and robust detection of all four breakage types in a wide variety of configurations, with low computational overhead and excellent scalability. These results confirm that distributed spanning-tree-based analysis provides an effective alternative to solving global equilibrium equations in modular robotics, enabling reliable structural integrity assessment even in large and highly connected assemblies.

Modular self-reconfigurable robots are composed of many connected modules that must move in coordination to transform from one shape to another. Effective reconfiguration requires Multi-Agent Path Finding (MAPF) methods that manage both spatial and temporal conflicts; in this paper, we compare three such coordination strategies: Cooperative A* (CA*), Conflict-Based Search (CBS), and a flow-based pairing and streamline construction method. Our results show that CA* provides fast and lightweight planning suitable for sequential or limited-parallel motion. CBS produces lower-cost solutions and stronger conflict handling but requires more computation as the number of conflicts increases. The MaxFlow approach offers an efficient way to assign targets and organize movement, reducing ambiguity and improving coordination during structured reconfiguration stages.

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.

Modular self-reconfigurable robots can be used to build objects either by filling the space entirely with robots (densely packed) or by constructing a hollow internal structure (scaffolding) and coating the outside. Scaffolding offers many advantages for building an object versus a densely packed representation. However, the different approaches have never been compared. In this article, we define and evaluate metrics for comparing scaffolding algorithms and we test them on three existing scaffolding algorithms.

Satellites, high-altitude drones, and 5 G links in space now support critical services such as air traffic control, financial transactions, and weather alerts. However, most of this equipment was not originally designed to face modern cyber threats. Ground stations connected to the cloud can be breached, GPS signals can be jammed, and some components in the supply chain may include hidden malware. There is still no shared list of known vulnerabilities and no secure environment for testing spacerelated cyber defense. This paper identifies eleven key research gaps. These include secure routing, onboard attack detection, automated recovery, trusted supply chains, post-quantum encryption, zero-trust implementation, and real-time service impact monitoring. For each topic, we describe the challenge, explain why it matters, and propose a guiding research question. We also explore how a team of small, task-specific artificial intelligence agents, what we call an agentic (multi-agent) approach, could improve onboard defense without relying on large hardware.The paper ends with a proposed five-year roadmap. This includes flight tests of post-quantum and quantum key distribution (QKD) links, open-access cyber-ranges for practical testing, better rules for sharing vulnerabilities, and initial deployments of multi-agent security on operational spacecraft. Moving toward these proactive, modular defenses will help prevent outages like the KA-SAT incident.

Programmable Matter (PM) is composed of materials that can be programmed to modify their physical characteristics, such as alterations in shape and other properties. This paper proposes the implementation of an Artificial Neural Network (ANN) on a modular robot and PM system in order to enhance its computational and self-reconfiguration capabilities. The idea is to imbue these systems with computational intelligence, enabling it to adapt itself dynamically according to its internal constraints and/or its surrounding environment. Indeed, programmable matter is a distributed system that suffers from several challenges, like high number of modules, limited energy and computational capabilities, storage and communication. Deploying Artificial Intelligence (AI) techniques directly on modules will increase the ability of the systems to autonomously and dynamically optimize the use of the available resources. However, most of existing AI solutions need high computational capabilities and rely on centralized servers which are not suitable for PM systems with limited resources. In this paper, we study the feasibility of fusing AI and PM by implementing a distributed artificial neural network model directly on PM systems. The objective of this model is to let the system dynamically and in a distributed manner to determine its current shape. Our approach is demonstrated both by simulations and by implementation on a real Blinky Blocks platform. The conducted experimentation shows that the results are exactly the same as an ANN executed in a centralized manner.

Modular self-reconfigurable (MSR) robots have the ability to change their structure through processing, communication, and movement in a process called self-reconfiguration. Although dynamic self-reconfiguration has shown promising results in different applications, robots still face uncertainties while performing tasks in the real world. For example, they may encounter unexpected obstacles that can divert them from their goals or even cause the system to shut down. This paper introduces a new approach to help robots detect and adapt to obstacles, allowing them to continue achieving their original objectives. This method focuses on the movement and direction of two-dimensional self-reconfigurable robots. We use VisibleSim to test and evaluate this algorithm in different scenarios, from dealing with a single obstacle to navigating a full maze.

Modular robots consist of multiple individual robotic modules that are connected to form various configurations. They are also equipped with locomotion capabilities so they can move to change their interconnections and self-reconfigure into different configurations. In this paper, we propose a distributed algorithm for configuration recognition designed for large-scale 2D lattice-based modular robots. The algorithm consists of searching the set of borders to be then transmitted to the modules in order to enable them to collectively discover an efficient global representation of their current configuration. To assess the performance of the proposed algorithm, we conducted simulations on three different configurations and compared it to a box-based approach. The results highlight significant reductions in communication complexity across all configurations. Additionally, our algorithm demonstrated improved memory efficiency in two out of three configurations.

Self-Reconfigurable Modular Robots typically consist of high number of modules with uniform docking interfaces, allowing them to transform into various shape. Recognizing the shape of such a system composed of hundreds of modules is a significant challenge. Given a new configuration, a modular robots system must be able to determine and update its shape dynamically and in a distributed manner. In a previous work, we developed an algorithm that identifies overlapping boxes to cover the entire robot configuration through message-passing, enabling robots to determine a representation of their current shape. However, this algorithm was static and did not react to changes in real time. In this paper, we introduce an updated shape recognition algorithm that dynamically and in real-time recognizes the addition of modules to update the shape description of the entire configuration using local information. The dynamic algorithm to update the shape description is tested in a simulated environment and compared to re-executing the shape recognition algorithm on the whole configuration. The results show the efficiency of our algorithm in updating the robot’s current shape in real time.

In this paper, we propose a fully distributed method for scheduling the movements of modular robots in a set while maintaining its balance. We explore self-reconfigurable modular robots, which are robots that can change their shape and behavior on their own. We focus on the important problem of verifying their stability and keeping these robots stable while they change shape. Our approach involves developing an algorithm that helps the robots stay balanced in real-time, even when they are changing shape. At the heart of this algorithm is the mechanical notion of the “support polygon” and the concept of “Mobile Object ” in message data, which enables a fast stability verification. We test this algorithm using a programmable matter simulator called VisibleSim, we show that it works well for validating a current static situation and for predicting the status after one or more movements. Additionally, we introduce the concept of dimensional reduction from 3D to 2D, simplifying stability analysis while maintaining accuracy. By addressing stability challenges, our research aims to make self-reconfigurable modular robots more useful in various applications.

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.

Programmable matter refers to material that can be programmed to alter its physical properties, including its shape. Such matter can be built as a lattice of attached robotic modules, each seen as an autonomous agent with communication and motion capabilities. Self-reconfiguration consists in changing the initial arrangement of modules to form a desired goal shape, and is known to be a complex problem due to its algorithmic complexity and motion constraints. In this paper, we propose to use a max-flow algorithm as a centralized global planner to determine the concurrent paths to be traversed by modules through a porous structure composed of 3D Catoms meta-modules with the aim of increasing the parallelism of motions, and hence decreasing the self-reconfiguration time. We implement a traffic light system as a distributed asynchronous local planning algorithm to control the motions to avoid collisions. We evaluated our algorithm using VisibleSim simulator on different self-reconfiguration scenarios and compared the performance with an existing fully distributed synchronous self-reconfiguration algorithm for similar structures. The results show that the new method provides a significant gain in self-reconfiguration time and energy efficiency.

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.

This paper proposes a novel approach that integrates hierarchical clustering (HC) into reinforcement learning algorithms to address the simultaneous resolution of hybrid assembly line balancing (ALB-1) and assembly sequence planning (ASP). The proposed approach attempts to capture implicit constraints, derived from accumulated experiences and industry-specific knowledge, enhancing the adaptability of solutions. The inclusion of the clustering algorithm enhances the decision-making process of the reinforcement learning agent through the introduction of a problem-specific similarity reward. To evaluate the effectiveness of the approach, three adapted methods are implemented and tested: QL-HC, SARSA-HC, and SARSA without HC. The experimental results demonstrate the superior performance of our novel approach compared to traditional techniques, with SARSA-HC exhibiting particularly impressive results with 82

In this paper, we propose an extension to the Blinky Block robot (named XBlock) by adding wireless communication capabilities. This enables the creation of modular robots with multiple network layers. We create a set of heterogeneous modules where two types of robots coexist: classic Blinky Blocks and XBlocks. We propose a distributed algorithm for building clusters from XBlocks, which can then communicate wirelessly with each other and with a master computer while handling the control of their local cluster through wired, multi-hops, point-to-point communications. We'll show that the use of multiple networks on modular robots brings gains in terms of latency, jitter and packet losses, communication quality and security. It also allows for a clever balancing of control, significantly easing the job of the main controller. We propose several experiments (available in video) with a set of 168 connected modules running the same program. From a web interface on the master computer, we order all XBlocks to locally recruit members into their own clusters. Those cluster-heads then sends feedback to the master controller in the form of the shape of their recruited members, which is displayed.

In this paper, we explore the field of self-reconfigurable modular robots, representing a significant advance in robotic technology. These robots have many capabilities, offering high adaptability and flexibility for a variety of applications. However, computing the stability is challenging as it is computationally intensive, it needs to be distributed and fast, as close as possible of real-time. In this article, we introduce a distributed algorithm designed to overcome these challenges while taking mechanical constraints into account. At the heart of this algorithm is the notion of the "support polygon", which enables the stability of a modular robot to be assessed in real time. The algorithm is based on a fully distributed tree partitioning approach, facilitating efficient communication and collaboration between modules. The algorithm also uses a polygon merging approach to reduce the number of messages when creating the polygon support, thus significantly reducing response time. In fact, the response time of the method used is very small compared to other research. We also present simulation results on a simulator, VisibleSim, as well as experimental validation on real robotic modules, which underlines the practical viability of the approach. Overall, this work lays a solid base for further advances aiming to guarantee the stability of modular robots.