Modular Robot Research Articles

An Evolutionary Modular Product Development Under Circular Economy Approach

The incorporation of new technologies and materials, as well as the rapid evolution of products, makes it necessary to guide the product design and development process from bio-inspired perspectives that promote a circular economy. Among the proposals based on a bio-inspired solution is the framework called the Genomic Model of Eco-innovation and Eco-design, which integrates the Cradle-to-Cradle paradigm, material and substance flow analysis, along with all aspects present in the life cycle analysis of products. In this work, innovation is carried out through the integration of genetic algorithms as a methodological tool for optimizing the modular product design in the definition of the genotype within the aforementioned framework. Through a systematic approach, it analyzes how the modular reconfiguration of products, based on circular design principles and genetic optimization, can contribute to a significant reduction in resource use and waste generation. The proposed methodology is applied to a practical case based on the redesign of a small household appliance, demonstrating its potential to facilitate the implementation of circular economy strategies in the early stages of the product design and development process for studying product modularity, with the aims of improving its design and reducing its impact.

Research on stability of modular multi-legged robots with non-isomorphic configurations

Modular robots can be reconfigured and deformed into multiple non-isomorphic configurations. Different configurations of robots exhibit diverse motion and stability characteristics. Therefore, the stability of robots with different configurations is essential to improve their motion control robustness and expand their application range. In this article, a new reconfigurable modular multi-legged robot is designed. To analyze the stability of the various typical non-isomorphic configurations of modular robots, a hybrid discrimination method combining the static stability margin-force angle stability margin-stability cone is proposed. By conducting simulation experiments on the static minimum tilt angle of robots in tilt, pitched, and yawed terrains, as well as their combinations, and evaluating the performance index of tipping in the presence of external force interference, the effectiveness of the proposed method can be proved. Furthermore, the movement experiments of the robot in irregular terrain under the quadruped configuration and hexapod configuration were carried out, it is proven that the robot under different configurations can maintain the stability of the structure.

Linear-time quasi-static stability detection for modular self-reconfigurable robots

We address the problem of detecting potential instability in the planned motion of modular self-reconfigurable robots. Previous research primarily focused on determining the system’s unique physical state but overlooked the mutual compensation effects of connection constraints. We introduce a linear-time quasi-static stability detection method for modular self-reconfigurable robots. The internal connections, non-connected contacts, and environmental contacts are considered, and the problem is modeled as a second-order cone program problem, whose solving time linearly increases with the number of modules. We aim to determine the critical stable state of the system and that is achieved by finding the required minimum characteristic connection strength. By analyzing the critical stable state, we can assess the system’s stability and identify potential broken connection points. Furthermore, the internal stability margin is defined to evaluate the configuration’s stability level. The suspension and object manipulation configurations are first demonstrated in simulation to analyze the effectiveness of the proposed algorithm. Subsequently, a series of physical experiments based on FreeSN were carried out. The calculated stable motion ranges of manipulator configurations are highly consistent with the actual sampling boundaries. Moreover, the proposed algorithm successfully predicts stability and identifies broken connections in diverse configurations, encompassing quadruped and closed-chain configurations on both even and uneven terrains. The load experiment further demonstrates that the impacts from unmodeled factors and input errors under normal conditions can be on a small scale. By combining the proposed detection method and stability margin, we open the door to realizing real-time motion planning on modular self-reconfigurable robots.

Automated physics-based modeling of construction equipment through data fusion

Physics-based simulations are essential for designing autonomous construction equipment, but preparing models is time-consuming, requiring the integration of mechanical and geometric data. Current automatic modeling methods for modular robots are inadequate for construction equipment. This paper explores automating the modeling process by integrating mechanical data into 3D computer-aided design (CAD) models. A template library is developed with hierarchy and joint templates specific for equipment. During model generation, appropriate templates are selected based on the equipment type. Unspecified joint template data is extracted from technical specifications using a large language model (LLM). The 3D CAD model is then converted into a Universal Scene Description (USD) model. Users can adjust the part names and hierarchy within the USD model to align with the hierarchy template, and joint data is automatically integrated, resulting in a simulation-ready model. This method reduces modeling time by over 87 % compared to manual methods, while maintaining accuracy.

Dynamic localization based-utility decision approach under type-2 Pythagorean fuzzy set for developing internet of modular self-reconfiguration robot things

The Internet of Modular Robot Things (IoMRT) has emerged through the integration of robotic systems into the Internet of Things (IoT), offering a wide range of solutions to meet continuously growing demands. Six self-reconfiguration functionalities/criteria have been proposed for developing IoMRT. However, no study has fully developed an IoMRT that satisfies all the necessary functionalities. Additionally, there is a lack of scholarly research proposing a decision-based approach for evaluating and ranking IoMRT, which highlights a significant research gap. A complex multiple-criteria decision-making (MCDM) problem has arisen in evaluating and ranking IoMRT due to the diversity of functionalities, the need to prioritize these functionalities based on their importance, and data variability. To address this issue, the study proposes a novel decision-based approach for evaluating and ranking IoMRT, which consists of three phases: (i) Developing a novel weighting method called T2PFS-FWZICbIP (Type-2 Pythagorean Fuzzy Set–Fuzzy Weighted Zero Inconsistency based on Interrelationship Process) to measure the importance of the identified functionalities; (ii) Formulating a decision matrix by cross-referencing potential IoMRT developments with the six self-reconfiguration functionalities resulted in the selection of a random sample of 50 IoMRTs as proof of concept. Following this, the DLbU (Dynamic Localization-based Utility) method was proposed, integrating dynamic localization and utility procedures to manage binary data within the decision matrix; (iii) Developing a novel ranking method, T2PFS-DNMA (Type-2 Pythagorean Fuzzy Set–Double Normalization-based Multiple Aggregation), to address the diversity of functionalities and concerns regarding data variance. The results revealed that the Distributed functionality (C1) received the highest weight value of 0.4060 according to T2PFS-FWZICbIP, indicating its high importance in the ranking of IoMRT. In contrast, the High-Fidelity functionality (C5) received a weight value of 0.0733, indicating its very low importance in the ranking. IoMRT2 and IoMRT35 were identified as the most and least favored, respectively, according to T2PFS-DNMA. The robustness of the proposed approach was assessed through sensitivity analysis and comparative studies.

An efficient self-reconfiguration method to deploy space modular robots

An efficient self-reconfiguration method to deploy space modular robots

Age-related differences of the time-varying features in the brain functional connectivity and cognitive aging.

Brain functional modular organization changes with age. Considering the brain as a dynamic system, recent studies have suggested that time-varying connectivity provides more information on brain functions. However, the spontaneous reconfiguration of modular brain structures over time during aging remains poorly understood. In this study, we investigated the age-related dynamic modular reconfiguration using resting-state functional MRI data (615 participants, aged 18-88 years) from Cam-CAN. We employed a graph-based modularity analysis to investigate modular variability and the transition of nodes from one module to another in modular brain networks across the adult lifespan. Results showed that modular structure exhibits both linear and nonlinear age-related trends. The modular variability is higher in early and late adulthood, with higher modular variability in the association networks and lower modular variability in the primary networks. In addition, the whole-brain transition matrix showed that the times of transition from other networks to the dorsal attention network were the largest. Furthermore, the modular structure was closely related to the number of cognitive components and memory-related cognitive performance, suggesting a potential contribution to flexibility cognitive function. Our findings highlighted the notable dynamic characteristics in large-scale brain networks across the adult lifespan, which enhanced our understanding of the neural substrate in various cognitions during aging. These findings also provided further evidence that dedifferentiation and compensation are the outcomes of functional brain interactions.

Whole-brain modular dynamics at rest predict sensorimotor learning performance

Abstract Neural measures that predict cognitive performance are informative about the mechanisms underlying cognitive phenomena, with diagnostic potential for neuropathologies with cognitive symptoms. Among such markers, the modularity (subnetwork composition) of whole-brain functional networks is especially promising due to its longstanding theoretical foundations and recent success in predicting clinical outcomes. We used functional magnetic resonance imaging to identify whole-brain modules at rest, calculating metrics of their spatiotemporal dynamics before and after a sensorimotor learning task on which fast learning is widely believed to be supported by a cognitive strategy. We found that participants’ learning performance was predicted by the degree of coordination of modular reconfiguration and the strength of recruitment and integration of networks derived during the task itself. Our findings identify these whole-brain metrics as promising network-based markers of cognition, with relevance to basic neuroscience and the potential for clinical application.

Nonzero-sum game-based decentralized approximate optimal control of modular robot manipulators with coordinate operation tasks using value iteration

Nonzero-sum game-based decentralized approximate optimal control of modular robot manipulators with coordinate operation tasks using value iteration

Разработка архитектуры автономно управляемого роя роботов

Purpose: to create an innovative architecture for an autonomously controlled swadm of robots for effective exploration of smoke-filled rooms in designated areas. The problem of smoke-filled premises is relevant during emergencies such as fires and accidents, where a limited incident makes rescue operations difficult. The use of advanced technologies, including artificial intelligence and innovative methods, can improve the efficiency and safety of research in such settings. A promising direction for the development of this area is the use of artificial intelligence to improve the coordination and autonomy of robots, which will increase the accuracy and speed of task completion. Determine the type of robot and the required characteristics of the designed system include limited autonomous robots with highly sensitive sensors and sensors for cost-effective navigation, as well as self-directed coordination capabilities. Methods: comparison of existing methods for studying smokefilled rooms, as well as existing types. Comparison of the main types of sensors and sensors used for navigation in conditions of limited stability. Results: the type of robot was selected that is most suitable in terms of characteristics for the developed automated system, performing the functions of investigating smoke-filled rooms as efficiently as possible. The robot architecture has been developed. A diagram of the interaction of robot control tasks is presented. Practical importance: the architecture of the robot and the interaction scheme for robot control tasks have been developed. This software code for ensuring the safety and control of robots proposes a modular robot architecture, wide adaptation and flexible change of the system under different conditions, limited savings arising in rescue and search operations. The model being developed includes autonomous robots, methods for performing navigation, detecting obstacles, collecting data on the state of premises and ensuring interaction for coordinated research.

Design of a Multi-Environmentally Adaptable Modular Self-Reconfigurable Robot

Design of a Multi-Environmentally Adaptable Modular Self-Reconfigurable Robot

Dynamic Reorganization Patterns of Brain Modules after Stroke Reflecting Motor Function.

Advancements in neuroimaging technologies have significantly deepened our understanding of the neural physiopathology associated with stroke. Nevertheless, the majority of studies ignored the characteristics of dynamic changes in brain networks. The relationship between dynamic changes in brain networks and the severity of motor dysfunction after stroke needs further investigation. From the perspective of multilayer network module reconstruction, we aimed to explore the dynamic reorganization of the brain and its relationship with motor function in subcortical stroke patients. We recruited 35 healthy individuals and 50 stroke patients with unilateral limb motor dysfunction (further divided into mild-moderate group and severe group). Using dynamic multilayer network modularity analysis, we investigated changes in the dynamic modular reconfiguration of brain networks. Additionally, we assessed longitudinal clinical scale changes in stroke patients. Correlation and regression analyses were employed to explore the relationship between characteristic dynamic indicators and impairment and recovery of motor function, respectively. We observed increased temporal flexibility in the Default Mode Network (DMN) and decreased recruitment of module reconfiguration in the Attention Network (AN), Sensorimotor Network (SMN), and DMN after stroke. We also observed reduced module loyalty following stroke. Additionally, correlation analysis showed that hyper-flexibility of the DMN was associated with better lower limb motor function performance in stroke patients with mild-to-moderate impairment. Regression analysis indicated that increased flexibility within the DMN and decreased recruitment coefficient within the AN may predict good lower limb function prognosis in patients with mild to moderate motor impairment. Our study revealed more frequent modular reconfiguration and hyperactive interaction of brain networks after stroke. Notably, dynamic modular remodeling was closely related to the impairment and recovery of motor function. Understanding the temporal module reconfiguration patterns in multilayer networks after stroke can provide valuable information for more targeted treatments.

Dynamic parameter identification of modular robot manipulators based on hybrid optimization strategy: genetic algorithm and least squares method

Dynamic parameter identification of modular robot manipulators based on hybrid optimization strategy: genetic algorithm and least squares method

Multi-Level Behavioral Mechanisms and Kinematic Modeling Research of Cellular Space Robot

The cellular space robot (CSR) is a new type of self-reconfigurable robot. It can adapt the variety of on-orbit service tasks with large space spans through multi-level reconfiguration mechanisms. As the CSR has a large configuration space, kinematic solving becomes a key problem affecting on-orbit operation capability, and kinematic automatic solving research must be conducted. In order to solve this problem, firstly, the cellular space robot system capable of realizing multi-level self-reconfiguration is proposed for the demand of space on-orbit service, and the kinematic equations of modules are constructed by considering a single module function using screw theory. Secondly, the kinematics of the cellular space robot are encapsulated and divided into multiple levels, and the multilevel-assembly relationship-description method for robotic systems is proposed based on graph theory. On this basis, the pathway-solving algorithm is proposed to express the robot organization reachability information. Finally, the module–organ–robot multilevel kinematics solving algorithm is proposed in combination with screw theory. In order to verify the effectiveness of the algorithm in this paper, numerical simulation is used to compare with the proposed algorithm. The results show that compared with the traditional algorithm, the method in this paper only needs to update part of the assembly relations after organ migration, which simplifies the kinematic modeling operation and improves the efficiency of kinematic computation.

Modular reconfigurable rotary style soft pneumatic actuators

Modular soft robots have the advantage in their ability to be reconfigured and change their morphology on-demand according to a specified task. The actuator design and particularly the inter-unit connectivity strength commonly govern the usability and functionality of these modular robots and their applications. In this paper, we present development of reconfigurable modular rotary soft actuators that allow the construction of highly versatile soft robotic actuators and systems. These pneumatically driven modular actuators are composed of an inner balloon and a flexible arched reinforcement structure to produce a rotary motion. They serve as building blocks which may be combined in various configurations to construct linear assemblies capable of extensile or bending motions. The connection scheme allows for the larger actuators to grow or shrink with the addition or removal of modules. Using finite element simulations and physical characterizations, we systematically explore the mechanical response of individual modular actuators and the role of various connection schemes on their motions upon pressurization. A key conceptual contribution of this work lies in the idea that for a modular soft robotic system, offsetting and tuning actuator alignment relative to an axis of symmetry can lead to substantial changes in mechanical performance. Utilizing these modules we demonstrate the reconfigurability of this modular system by both reconfiguring a large actuator into a pair of smaller actuators that can grasp delicate objects, and reconfiguring actuators to develop a linear crawling robot.

How Perception, Actuation, and Communication Impact the Emergence of Collective Intelligence in Simulated Modular Robots.

Modular robots are collections of simple embodied agents, the modules, that interact with each other to achieve complex behaviors. Each module may have a limited capability of perceiving the environment and performing actions; nevertheless, by behaving coordinately, and possibly by sharing information, modules can collectively perform complex actions. In principle, the greater the actuation, perception, and communication abilities of the single module are the more effective is the collection of modules. However, improved abilities also correspond to more complex controllers and, hence, larger search spaces when designing them by means of optimization. In this article, we analyze the impact of perception, actuation, and communication abilities on the possibility of obtaining good controllers for simulated modular robots, that is, controllers that allow the robots to exhibit collective intelligence. We consider the case of modular soft robots, where modules can contract, expand, attach, and detach from each other, and make them face two tasks (locomotion and piling), optimizing their controllers with evolutionary computation. We observe that limited abilities often do not prevent the robots from succeeding in the task, a finding that we explain with (a) the smaller search space corresponding to limited actuation, perception, and communication abilities, which makes the optimization easier, and (b) the fact that, for this kind of robot, morphological computation plays a significant role. Moreover, we discover that what matters more is the degree of collectivity the robots are required to exhibit when facing the task.

Design and evaluation of a modular robot concept for external pipeline surface inspection

Design and evaluation of a modular robot concept for external pipeline surface inspection

Pareto Optimal Reconfiguration Planning and Distributed Parallel Motion Control of Mobile Modular Robots

Pareto Optimal Reconfiguration Planning and Distributed Parallel Motion Control of Mobile Modular Robots

A model-free method to learn multiple skills in parallel on modular robots

Legged robots are well-suited for deployment in unstructured environments but require a unique control scheme specific for their design. As controllers optimised in simulation do not transfer well to the real world (the infamous sim-to-real gap), methods enabling quick learning in the real world, without any assumptions on the specific robot model and its dynamics, are necessary. In this paper, we present a generic method based on Central Pattern Generators, that enables the acquisition of basic locomotion skills in parallel, through very few trials. The novelty of our approach, underpinned by a mathematical analysis of the controller model, is to search for good initial states, instead of optimising connection weights. Empirical validation in six different robot morphologies demonstrates that our method enables robots to learn primary locomotion skills in less than 15 minutes in the real world. In the end, we showcase our skills in a targeted locomotion experiment.

Peristaltic Motion Enabled by Pneumatic Artificial Muscles (PAMs) as Structural "Soft-Stiff" Actuators in a Modular Worm-Inspired Robot.

This paper considers the design, manufacture, and testing of a prototype "soft-stiff" worm-inspired robot referred to herein, as the PneumaticallyActuated PeristaLtic Advancing Modular (PALAM) robot. The robot has a modular structure, mimicking the segmented nature of earthworms, and each segment is individually actuated by a set of three pneumatic artificial muscles (PAMs). The PAMs contract when inflated by pressurised air, generating a pulling force and fulfilling the role of biological muscles in the robot. The PAMs are made from the elastomer silicone rubber, which affords the robot flexibility and enables a wide range of real-life applications. A control-system is designed which can inflate any PAM on demand, and hence replicate the peristaltic motion of earthworms in the PALAM robot. Finally, this paper discusses a successful, low-cost, and widely accessible approach for the manufacture of the PAMs utilised herein. The PAMs can be scaled dimensionally and made from different materials with varying mechanical properties and behaviours, meaning that they are suitable for use in a wide range of robotics applications.