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Reconfigurable modular robots, which can actively assemble and disassemble on command, offer the possibility of mesoscale (milliscale and microscale) manufacturing with robustness and controllability. In this study, we present a design of a scalable modular subunit with embedded permanent magnets in a 3-D printed cubic body. The subunit can be wirelessly controlled by an external uniform magnetic field. We also present controlled assembly–disassembly techniques for these subunits. Our modular robotic platform is highly reconfigurable and can create programmable, predetermined patterns based on open-loop control. The 2-D motion planner computes all reachable polyomino shapes from an arbitrary initial configuration and provides the shortest movement sequences to form each shape. Experimental results match computational modeling, demonstrating robust and reproducible behavior of the modular robotic platform that is promising for mesoscale manufacturing applications. Two cube sizes were tested: 10-mm edge lengths and 2.8-mm edge lengths.

This paper presents a review of modular and reconfigurable space robot systems intended for use in orbital and planetary applications. Modular autonomous robotic systems promise to be efficient, versatile, and resilient compared with conventional and monolithic robots, and have the potential to outperform traditional systems with a fixed morphology when carrying out tasks that require a high level of flexibility. Based on a set of fundamental concepts in modular self-reconfiguring robotics, advances in applying modular self-organizing robotics technologies to aerospace applications and space mission concepts are summarized for the purpose of identifying relevant requirements and solutions. Based on this survey, critical guidelines for the implementation of in space assembly and operation using modular autonomous robotic systems are identified.

Robots are required to be significantly compliant and versatile to work in unstructured environments. In a number of studies, robots have positively exploited the environments during interactions and completed tasks from a morphological viewpoint. Modular robots can help realize real-world adaptive robots. Researchers have been investigating the actuation, coupling, and communication mechanisms among these robots to realize versatility. However, the diverse force transmission among modules needs to be further studied to achieve the adaptive whole-body dynamics of a robot. In this study, we fabricated a modular robot and proposed the realization of force transmission on this robot, by constructing fluid transferable network systems on the actuation modules. By exploiting the physical property variations of the modular robot, our experimental results prove that the robot’s motion can be changed by switching the connection pattern of the system.

Modular robots have the potential for an unmatched ability to perform versatile and robust locomotion. However, designing effective and adaptive locomotion controllers for modular robots is challenging, resulting in a number of model-based methods that typically require various forms of prior knowledge. Deep reinforcement learning (DRL) provides a promising model-free approach for locomotion control by trial-and-error. However, current DRL methods often require extensive interaction data, hindering many possible applications. In this letter, a novel two-level hierarchical locomotion framework for modular quadrupedal robots is proposed. The approach combines a low-level central pattern generator (CPG)-based controller with a high-level neural network to learn a variety of locomotion tasks using DRL. The low-level CPG controller is pre-optimized to generate stable rhythmic walking gaits, while the high-level network is trained to modulate the CPG parameters for achieving task goals based on high-dimensional inputs, including the robot states and user commands. The proposed approach is employed on a simulated modular quadruped. With a limited amount of prior knowledge, the proposed method is demonstrated to be capable of learning a variety of locomotion skills such as velocity tracking, path following, and navigating to a target. Simulation results show that the proposed method can achieve higher sample efficiency than the model-free DRL method and are substantially more robust than the baseline methods to external disturbances and irregular terrain.

Many modular robots and active matter platforms are inspired by natural aggregates of single-celled organisms that exhibit complex emergent behaviors far beyond the capability and range of individuals. These behaviors stem primarily from short range chemical and mechanical interactions. Past work has focused largely on wireless communication akin to chemicals, however, physical interactions require a shift from rigid to soft, durable robots capable of inducing and measuring strain. Here, we present a platform to support such studies. The hardware consists of stand-alone, soft, pneumatic robots capable of radial expansion and contraction. The robots are cheap and fast to produce; they have 3 directional strain sensors, and 6 magnet pairs to loosely couple with their neighbors. We characterize force profiles, sensors, elastic modulus, magnetic interaction, as well as exploratory aggregate motions. Finally, we demonstrate their ability to synchronize, locomote, and fracture in a complimentary quasi-static simulator, with a coupled oscillator model, and discuss evaluation metrics. We hope that this platform will serve to further insights on how simple physical interactions between locally informed agents may lead to complex emergent behaviors.

Various morphologies and motions have been investigated extensively for robots on substrates or interfaces. However, the locomotion of modular robots in 3D free spaces remains challenging owing to zero dependence on boundaries, in particular at small scales. Here we propose simply modularized miniature propellers (mini-propellers) with vertical mobility. These mini-propellers perform directional self-assembly and on-demand disassembly for increasing adaptability in complex environments. They can travel through a 3D maze, and the 3D locomotion is realized by regulating programmable magnetic fields. Furthermore, the mechanism of vertical swimming and disassembly is theoretically analyzed and experimentally verified. We find that the mini-propeller in critical poses is capable of swimming upstream against flows. Optimal actuation conditions for conducting effective upstream motion are determined. Mini-propellers may help exploit more off-boundary swimming behaviors, and the simple assembly and disassembly strategy is anticipated to be a useful and scalable method to develop small-scale modularized robots for versatile functionalities.

Motion planning in high-dimensional space is a challenging task. In order to perform dexterous manipulation in an unstructured environment, a robot with many degrees of freedom is usually necessary, which also complicates its motion planning problem. Real-time control brings about more difficulties in which robots have to maintain the stability while moving towards the target. Redundant systems are common in modular robots that consist of multiple modules and are able to transformed into different configurations with respect to different needs. Different from robots with fixed geometry or configurations, the kinematics model of a modular robotic system can alter as the robot reconfigures itself, and developing a generic control and motion planning approach for such systems is difficult, especially when multiple motion goals are coupled. A new manipulation planning framework is developed in this paper. The problem is formulated as a sequential linearly constrained quadratic program (QP) that can be solved efficiently. Some constraints can be incorporated into this QP, including a novel way to approximate environment obstacles. This solution can be used directly for real-time applications or as an off-line planning tool, and it is validated and demonstrated on the CKBot and SMORES-EP modular robot platforms., Comment: 25 pages, 20 figures

On-orbit assembling technology provides a promising way for deploying large space systems. Transporting a substructure by adjusting its orbit and attitude states is a key to assembling it to the main body of a large space system. This paper develops a robust coordinated control mechanism for multiple modular robots when they cooperate to control the orbit and attitude movements of a substructure. By designing a distributed information-based scheme for control allocation, the modular robots are able to obtain their control inputs through their local interaction. The computation and communication burdens are therefore distributed among the modular robots. Nonlinear disturbance observers are introduced to suppress the adverse effects caused by external disturbances, parameters uncertainties and orbit-attitude coupling effect of the substructure. A new input saturation compensator is designed to suppress the effect caused by input saturation whenever the commanded control inputs of the modular robots exceed the permissible range. The stability of the closed-loop system is analyzed using the Lyapunov method, and the tuning conditions of the coordinated control mechanism are obtained. Numerical simulations demonstrate the effectiveness of the proposed method.

Even though many prototypes of 3D self-reconfiguring modular robots (SRMRs) have been developed in recent years, a demonstration involving 1'000 modules remains a challenge. This is largely due to complex mechanics needed to achieve connection, disconnection and especially actuation in such a system. This work introduces "Kubits," which is, to the best of our knowledge, the first SRMR that achieves these functionalities without moving parts, i.e. in solid-state. Each module contains a kind of programmable magnet whose magnetization can be controlled. The simultaneous control of touching magnet pairs of two modules is used to create attraction (connection), neutrality (disconnection) and actuation (repulsion), which results in self-reconfiguration by a cube pivoting around an edge. We detail the design of the system and demonstrate a series of successful flips, including a jumping mode. The energy-efficient, lightweight and robust (both in terms of mechanics and control) method in Kubits is a promising path for scalable self-reconfiguration.

To date, many gait generation strategies have been designed for robots with leg configurations that model those of natural creatures. However, their leg configurations are limited to the $2\times N$ type, such as hexapod or myriapod; hence, simultaneously, the potential ability of legged robots is implicitly limited. We consider single-legged modular robots that can be arranged to form a cluster with arbitrary 2-D leg configurations. By choosing configurations appropriately, these robots have the potential to perform several types of tasks, as is the case for reconfigurable modular robots. However, to use appropriate configurations for a given task, a unified gait generation system for various configurations of a cluster is required. In this article, we propose an autonomous distributed control system for each single-legged modular robot to collectively achieve static walking of the cluster with various leg configurations on planar ground. Moreover, our system is an autonomous distributed system with scalability and fault tolerance, in which each module determines the moving pattern of its foot through local communication without global information, such as the entire leg configuration of the cluster. We verified that several types of clusters achieved static walking using our system not only in dynamic simulations, but also in real robot experiments.

SUMMARYThis paper proposes an approach for the high-level programming of co-designed robots that reduces programming complexity. Particularly, the work presented focuses on the programming framework of an intelligent system, based on the IEEE Standard Ontologies for Robotics and Automation, which allows users the automatic design of robots and the automatic implementation of controllers in the Robot Operating System (ROS). In our approach, the co-designed robot functionalities are automatically translated into visual programming blocks allowing non-expert users an easy robot programming by means of a visual programming language. Several robot configurations and three case studies are provided as a proof of concept. The validation, in terms of usability, of the framework has been carried out with inexperienced users showing promising results.

Group process assessment is one of the methodological challenges in computer-supported collaborative learning (CSCL). The aim of this study is to analyze the group process dimensions in a problem-solving task with modular robotics in which creative components of fluidity, flexibility and innovation can be observed. The analysis of group process dimensions in relation to the creative components aims to understand the way group processes can support the creativity process in a problem-solving task. For this objective, 24 dyads of in-service teachers in a creative problem-solving task with modular robotics were engaged. The group process dimensions of conversation, social interaction and problem-solving was identified based on a CSCL coding schema developed for the Virtual Math Team environment. The creative components of fluidity, flexibility and innovation are operationalized based on Guilford’s Alternate Uses Test’s components. The results show the creative component of innovation is related to interactions of support within the dyad. Moreover, the participants dedicating more time to solve the task are engaged not only in more problem-solving interactions with their dyad but also in building more innovative figures, and they also make more figures together. Those results lead us to consider the importance of a positive emotional environment in the context of collaborative creation.

Reconfigurable modular robots (RMRobots) can change their shape and functionality (e.g., locomotion styles) to fit different environments, and have been widely investigated in applications, such as exploration and inspection. In this paper, we present a new application of RMRobots for improving human spatial ability which plays a significant role in developing an individual’s performance and achievement in science, technology, engineering, and mathematics (STEM). Two user studies are conducted, and the results show that: 1) the task performance of interacting with RMRobots has a significant positive relationship with mental rotation, a widely used measure of spatial ability; and 2) interacting with RMRobots can effectively improve the performance on a task related to spatial reasoning skills according to behavioral data and electroencephalograph (EEG) indices. Our presented study broadens RMRobot research in the area of human-robot interaction.

Purpose The purpose of this study is to explore using real-observation quantum genetic algorithms (RQGAs) to evolve neural controllers that are capable of controlling a self-reconfigurable modular robot in an adaptive locomotion task. Design/methodology/approach Quantum-inspired genetic algorithms (QGAs) have shown their superiority against conventional genetic algorithms in numerous challenging applications in recent years. The authors have experimented with several QGAs variants and real-observation QGA achieved the best results in solving numerical optimization problems. The modular robot used in this study is a hybrid simulated robot; each module has two degrees of freedom and four connecting faces. The modular robot also possesses self-reconfiguration and self-mobile capabilities. Findings The authors have conducted several experiments using different robot configurations ranging from a single module configuration to test the self-mobile property to several disconnected modules configuration to examine self-reconfiguration, as well as snake, quadruped and rolling track configurations. The results demonstrate that the robot was able to perform self-reconfiguration and produce stable gaits in all test scenarios. Originality/value The artificial neural controllers evolved using the real-observation QGA were able to control the self-reconfigurable modular robot in the adaptive locomotion task efficiently.

Modular design methodology enables rapid reconfigurability for functional changes, robustness to failures and space utilisation for transportation. In the case of planetary exploration robots, there is promise in modular robots that are able to reconfigure themselves for exploration of unknown terrains. This paper presents a design and controller architecture for modular field robots that can be rapidly assembled in a variety of functional configurations. A key challenge of building a functional robot out of modular units is the ability to seamlessly add, remove and replace individual units to enable functional improvements as well as adapt to terrain requirements. We present a representative modular wheel design and a distributed controller architecture able to create a range of bespoke multi-wheeled configurations capable of traversal on a variety of terrains during simulated failure scenarios. The self-contained wheeled unit has energy, computation communication, and actuation modules and does not require any modification or physical customization in the field during deployment enabling a seamless plug and play behaviour. The hierarchical control structure runs a body controller node that decomposes a whole body motion requested from a higher level planner to generate a sequence of actuation goals for each of the modules, while a local controller node running on each of the modules ensures that the desired actuation is adapted to the configuration, load and terrain characteristics.

Modular robots hold the promise of versatility in that their components can be re-arranged to adapt the robot design to a task at deployment time. Even for the simplest designs, determining the optimal design is exponentially complex due to the number of permutations of ways the modules can be connected. Further, when selecting the design for a given task, there is an additional computational burden in evaluating the capability of each robot, e.g., whether it can reach certain points in the workspace. This work uses deep reinforcement learning to create a search heuristic that allows us to efficiently search the space of modular serial manipulator designs. We show that our algorithm is more computationally efficient in determining robot designs for given tasks in comparison to the current state-of-the-art.

We introduce a discrete approach to robotic construction that enables the integration of structure, mechanism, and actuation and offers a promising route to on-demand robot fabrication. We demonstrate this with the assembly of two centimeter-scale electromechanical systems: a Discretely Assembled Walking Motor (DAWM) capable of producing large scale linear or rotary motion from five millimeter-scale part types as well as a Modular Tiny Locomoting Element (MOTILE) that can locomote on a variety of ferrous surfaces. The five part types each embody a limited capability including rigid (strut and node), flexural, magnetic, or coil. Through their arrangement in a three-dimensional lattice, we demonstrate the assembly of actuated mechanical degrees-of-freedom in useful small-scale machines. This work extends prior research in discrete material systems with the inclusion of flexural and actuation components. Actuation is accomplished with the use of voice coil actuator components that produce up to 42 mN of force and strokes of 2 mm. This performance compares well with other millimeter scale actuators and provides sufficient force to lift 28 connected nodes in our assembled lattice, or 7 other actuator components. DAWM is capable of stepping at rates of up to 35 Hz, resulting in velocities of up to 25 mm/s. Multiple DAWM systems can be stacked to add force and can be driven in-phase or out-of-phase to produce intermittent or continuous force, respectively. MOTILE can climb vertical surfaces at speeds of 2.46 body-lengths per second, representing the fastest vertical climbing robot in recently reported research. This approach to robot fabrication discretizes robotic systems at a much finer granularity than prior work in modular robotics and demonstrates the possibility of assembling useful small-scale machines from a limited set of standard part types.

Two types of reconfiguration—heterogeneous reconfiguration, in which all the modules are considered to be nonidentical, and homogeneous reconfiguration, in which they are considered to be identical—are widely studied in modular robotics research. Homogeneous reconfiguration can manage the transformation of the whole configuration of the robot structure, whereas heterogeneous reconfiguration can manage not only this transformation, but also the permutation process for navigating all modules to their exact target positions in the transformed configuration. However, the transformation process and time-consuming permutation process cannot always be executed simultaneously in heterogeneous reconfiguration. As a result, it takes much more time to reconfigure heterogeneous modular robots than it does to reconfigure homogeneous ones. In the previous research, linear homogeneous transformation algorithms for modular robots have been proposed. However, only quadratic permutation algorithms are available for heterogeneous lattice modular robots. This article studies a reconfiguration algorithm for heterogeneous lattice modular robots with linear operation time cost. The algorithm is based on simultaneous tunneling and permutation; a robot transforms its configuration via homogeneous tunneling motion, but permutation of each module's position is carried out simultaneously during the tunneling transformation. The approach also makes the time cost for the whole permutation process linear. To achieve this, the idea of a transparent meta-module is introduced, which allows the modules belonging to a meta-module to pass through the spaces occupied by other meta-modules. The algorithm is provided in both centralized and distributed form. We prove the correctness and completeness of the proposed algorithm. We also show some results of the reconfiguration simulation of heterogeneous modular robots with three-dimensional 2 × 2 × 2 cubic meta-modules by the proposed algorithm.

Modular robotics provides the unique advantage over traditional robotic technologies in terms of re- configurability, reusability, and ease in manufacturing. They are also cost-effective and time saving because each module is identical; thus simplifying the manufacturing process. This paper presents a modular legged robot which combines the advantages of both the modular robot and legged robot. This proposed idea is a hexapedal modular robot with each leg having three degrees of freedom. This paper discusses about the advantages of a modular robot over other robots; gait motion patterns, part design, various application and future scope.

Accurate position sensing is important for state estimation and control in robotics. Reliable and accurate position sensors are usually expensive and difficult to customize. Incorporating them into systems that have very tight volume constraints such as modular robots are particularly difficult. PaintPots are low-cost, reliable, and highly customizable position sensors, but their performance is highly dependent on the manufacturing and calibration process. This paper presents a Kalman filter with a simplified observation model developed to deal with the non-linearity issues that result in the use of low-cost microcontrollers. In addition, a complete solution for the use of PaintPots in a variety of sensing modalities including manufacturing, characterization, and estimation is presented for an example modular robot, SMORES-EP. This solution can be easily adapted to a wide range of applications., Comment: 10 pages, 28 figures

Today's use of large-scale industrial robots is enabling extraordinary achievement on the assembly line, but these robots remain isolated from the humans on the factory floor because they are very powerful, and thus dangerous to be around. In contrast, the soft robotics research community has proposed soft robots that are safe for human environments. The current state of the art enables the creation of small-scale soft robotic devices. In this article we address the gap between small-scale soft robots and the need for human-sized safe robots by introducing a new soft robotic module and multiple human-scale robot configurations based on this module. We tackle large-scale soft robots by presenting a modular and reconfigurable soft robotic platform that can be used to build fully functional and untethered meter-scale soft robots. These findings indicate that a new wave of human-scale soft robots can be an alternative to classic rigid-bodied robots in tasks and environments where humans and machines can work side by side with capabilities that include, but are not limited to, autonomous legged locomotion and grasping.

The coordinated rehabilitation of the upper limb is important for the recovery of the daily living abilities of stroke patients. However, the guidance of the joint coordination model is generally lacking in the current robot-assisted rehabilitation. Modular robots with soft joints can assist patients to perform coordinated training with safety and compliance. In this study, a novel coordinated path planning and impedance control method is proposed for the modular exoskeleton elbow–wrist rehabilitation robot driven by pneumatic artificial muscles (PAMs). A convolutional neural network-long short-term memory (CNN-LSTM) model is established to describe the coordination relationship of the upper limb joints, so as to generate adaptive trajectories conformed to the coordination laws. Guided by the planned trajectory, an impedance adjustment strategy is proposed to realize active training within a virtual coordinated tunnel to achieve the robot-assisted upper limb coordinated training. The experimental results showed that the CNN-LSTM hybrid neural network can effectively quantify the coordinated relationship between the upper limb joints, and the impedance control method ensures that the robotic assistance path is always in the virtual coordination tunnel, which can improve the movement coordination of the patient and enhance the rehabilitation effectiveness.

International audience; Shape-changing User Interfaces (UIs) explore the ability of a UI to change its physical shape to support multiple interaction modalities for users’ input and/or system’s output. An approach currently studied to implement such interfaces at a high resolution is based on mm-sized, round, and self-actuated modules. The problem we tackle in this paper is to find the range of usable sizes of such modules, to better inform the trade-off between usability and technological feasibility. We assessed four sliders in a controlled user study: a standard slider and three sliders made of mock-up rounded modules of ø1 mm, ø2.5 mm, and ø5 mm. Experimental results show that (1) ø5 mm modules significantly impair performance for the pursuit task and subjective perception for both tasks, (2) performance increases when the size of modules decreases, but (3) users reportedly enjoyed the haptic feedback provided by ø1 mm to ø2.5 mm modules. These results provide deeper understanding on the impact of the size of modules on performance and subjective perception to inform current technological development of physical user interfaces made of small robotic modules.

Architected modular origami structures show potential for future robotic matter owing to their reconfigurability with multiple mobilities. Similar to modular robots, the units of modular origami structures do not need to be assembled in a fully packed fashion; in fact, disconnection can provide more freedom for the design of mobility and functionality. Despite the potential of expanded design freedom, the effect of the disconnection of units on the mobility and physical properties has not yet been explored in modular origami structures. Determining the mobility and weak spots of modular origami structures is significant to enable transformation with minimum energy. Herein, we investigate the effect of the disconnection of units on the mobility and stiffness of architected modular origami structures with deformable units using angular kinematics of geometry and topology of units and closed loops. Angular kinematics provides a valuable tool for investigating the complex mobility of architected modular origami structures with the disconnection of loops. The mobility of the network structure is a function not only of the number of disconnections but also of the topology of the loop. In contrast to the conventional negative perception of defects or disconnection in these materials, the disconnection can potentially be used to expand the design space of mobility for future robotic matter. Our findings can be used to develop powerful design guidelines for topologically reconfigurable structures for soft modular robots, active architected materials, implanted modular devices, deployable structures, thermal metamaterials, and active acoustic metamaterials.

In order to meet the assist requirements of extravehicular activity (EVA) for astronauts, such as moving outside the international space station (ISS) or performing on-orbit tasks by a single astronaut, this paper proposes an astronaut robotic limbs system (AstroLimbs) for extravehicular activities assistance. This system has two robotic limbs that can be fixed on the backpack of the astronaut. Each limb is composed of several basic module units with identical structure and function, which makes it modularized and reconfigurable. The robotic limbs can work as extra arms of the astronaut to assist them outside the space station cabin. In this paper, the robotic limbs are designed and developed. The reinforcement learning method is introduced to achieve autonomous motion planning capacity for the robot, which makes the robot intelligent enough to assist the astronaut in unstructured environment. In the meantime, the movement of the robot is also planned to make it move smoothly. The structure scene of the ISS for extravehicular activities is modeled in a simulation environment, which verified the effectiveness of the proposed method.

This chapter studies the dynamic issues of modular robots. Since a modular robot has unfixed configurations, it is very difficult to derive the closed-form dynamic model manually. Following the geometric modeling approach, a dynamic formulation scheme is introduced, which can generate both the recursive and the closed-form dynamic models of a tree-structured modular robot from a given Assembly Incidence Matrix (AIM) automatically.

We propose a unified approach for automatically generating multiple robot-model descriptions for modular robot manipulators. Modular robots need models for kinematics and dynamics to deploy motion control schemes with high performance as it is possible for their fixed structure counterparts. Additionally, these models are also necessary for enabling the optimization of their assembly to optimally meet task and environmental constraints. Manual derivation of models for every different assembly is impractical as the number of possible arrangements can be large. We propose a systematic approach for characterizing single modules and for automatically generating the following widely adopted robot model descriptions for kinematics and dynamics: standard and modified Denavit-Hartenberg, product of exponentials formulation, and the unified robot description format. We numerically verify our approach also considering experimental data from a real modular robot manipulator.

Undulatory swimming represents an ideal behavior to investigate locomotion control and the role of the underlying central and peripheral components in the spinal cord. Many vertebrate swimmers have central pattern generators and local pressure-sensitive receptors that provide information about the surrounding fluid. However, it remains difficult to study experimentally how these sensors influence motor commands in these animals. Here, using a specifically designed robot that captures the essential components of the animal neuromechanical system and using simulations, we tested the hypothesis that sensed hydrodynamic pressure forces can entrain body actuation through local feedback loops. We found evidence that this peripheral mechanism leads to self-organized undulatory swimming by providing intersegmental coordination and body oscillations. Swimming can be redundantly induced by central mechanisms, and we show that, therefore, a combination of both central and peripheral mechanisms offers a higher robustness against neural disruptions than any of them alone, which potentially explains how some vertebrates retain locomotor capabilities after spinal cord lesions. These results broaden our understanding of animal locomotion and expand our knowledge for the design of robust and modular robots that physically interact with the environment.

This work introduces an array prototype based on a Frequency Modulation (FM) encoding architecture to transfer multiple sensor signals on a single wire. The use case presented adopts Hall-effect sensors as an example to represent a much larger range of sensor types (e.g., proximity and temperature). This work aims to contribute to large area artificial skin systems which are a key element to enhance robotic platforms. Artificial skin will allow robotic platforms to have spatial awareness which will make interaction with objects and users safe. The FM-based architecture has been developed to address limitations in large-scale artificial skin scalability. Scalability issues include power requirements, number of wires needed, as well as frequency, density, and sensitivity bottlenecks. In this work, eight sensor signals are simultaneously acquired, transferred on a single wire and decoded in real-time. The overall taxel array current consumption is 36 mA. The work experimentally validates and demonstrates that different input signals can be effectively transferred using this approach minimizing wiring and power consumption of the taxel array. Four different tests using single as well as multiple stimuli are presented. Observations on performances, noise, and taxel array behaviour are reported. The results show that the taxel array is reliable and effective in detecting the applied stimuli.

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%.

This paper presents a self-reconfigurable modular robot with an integrated two degrees-of-freedom (DOF) active docking mechanism. Active docking in modular robotic systems has received a lot of interest recently as it allows small versatile robotic systems to coalesce and achieve the structural benefits of large systems. This feature enables reconfigurable modular robotic systems to bridge the gap between small agile systems and larger robotic systems. The proposed self-reconfigurable mobile robot design exhibits dual mobility using a tracked drive for longitudinal locomotion and a wheeled drive for lateral locomotion. The 2-DOF docking interface allows for efficient docking while tolerating misalignments. To aid autonomous docking, visual marker-based tracking is used to detect and re-position the source robot relative to the target robot. The tracked features are then used in Image-Based Visual Servoing to bring the robots close enough for the docking procedure. The hybrid-tracking algorithm allows eliminating external pixelated noise in the image plane resulting in higher tracking accuracy along with faster frame update on a low-cost onboard computational device. This paper presents the overall mechanical design and the integration details of the modular robotic module with the docking mechanism. An overview of the autonomous tracking and docking algorithm is presented along with a proof-of-concept real-world demonstration of autonomous docking and self-reconfigurability. Experimental results to validate the robustness of the proposed tracking method, as well as the reliability of the autonomous docking procedure, are also presented.

Most evolutionary robotics studies focus on evolving some targeted behavior without taking the energy usage into account. This limits the practical value of such systems because energy efficiency is an important property for real-world autonomous robots. In this paper, we mitigate this problem by extending our simulator with a battery model and taking energy consumption into account during fitness evaluations. Using this system we investigate how energy awareness affects the evolution of robots. Since our system is to evolve morphologies as well as controllers, the main research question is twofold: (i) what is the impact on the morphologies of the evolved robots, and (ii) what is the impact on the behavior of the evolved robots if energy consumption is included in the fitness evaluation? The results show that including the energy consumption in the fitness in a multi-objective fashion (by NSGA-II) reduces the average size of robot bodies while at the same time reducing their speed. However, robots generated without size reduction can achieve speeds comparable to robots from the baseline set.

Highly deformable bodies are essential for numerous types of applications in all sorts of environments. Joint-like structures comprising a ball and socket joint have many degrees of freedom that allow mobility of many biomimetic structures. Recently, soft robots are favored over rigid structures for their highly compliant material, high-deformation properties at low forces, and ability to operate in difficult environments. However, it is still challenging to fabricate complex designs that satisfy application constraints due to the combined effects of material properties, actuation method, and structural geometry on the performance of the soft robot. Therefore, a combination of a rigid joint and a soft body can help achieve modular robots with fully functional body morphology. Yet, the fabrication of soft parts requires extensive molding for complex shapes, which comprises several processes and can be time-consuming. In addition, molded connections between extremely soft materials and hard materials can be critical failing points. In this paper, we present a functionally graded 3D-printed joint-like structure actuated by novel contractile actuators. Functionally graded materials (FGMs) via 3D printing allow for extensive material property enhancement and control which warrant tunable functionalities of the system. The 3D-printed structure is made of 3 rigid ball and socket joints connected in series and actuated by integrating twisted and coiled polymer fishing line (TCPFL) actuators, which are confined in the FGM accordion-shaped channels. The implementation of the untethered TCPFL actuation system can be highly beneficial for deployment in environments that require low vibrations and silent actuation. The fishing line TCP actuators produce an actuation strain up to 40% and bend the joint up to 40° in any direction. The TCPFL can be actuated individually or as a group to control the bending trajectory of the modular joint, which is beneficial when deployed in areas that contain small crevices. Obtaining complex modes of bending, the FGM multidirectional joint demonstrated a great potential to achieve different functionalities such as crawling, rolling, swimming, or underwater exploration.

This paper details development of a functional hexapod configuration by means of MECABOT robotic modules, previously designed and built at Universidad Militar Nueva Granada. For this configuration, the modules received a series of upgrades which allowed reduction of size and weight, as well as an increase in torque. In order to develop control software, Webots IDE was used to simulate workspace characteristics in a virtual environment. Once the configuration was assembled using the improved modules, locomotion tests were carried out for forward and lateral motions, for which robot speed was measured and compared with simulation results. Analysis of the divergence between simulated and practical speeds led to a series of design considerations that will require further evaluation in future work.

If a robot system can take various shapes, then it can play various roles, such as humanoid, dog robot, and robot arm. A modular robot is a robot system in which robots are configured using multiple modules, and it is possible to configure robots of other shapes by varying the combinations of the modules. In conventional modular robots, the shape is restricted by gravity, and configurable shapes are limited. In this study, we propose a gravity compensation modular robot to solve this problem. This paper describes the design and prototyping of the gravity compensation modular robot, and provides examples of robot shapes configured using the gravity compensation modules and motion experiments of the robots. In the experiments, there were motions that the robots could perform and could not perform. We considered the lack in the gravity compensation level and module rigidity as the main factor of the failures. This paper also discusses the solutions to these problems.

Self-reconfigurable modular robots are usually composed of multiple modules with uniform docking interfaces that can be transformed into different configurations by themselves. The reconfiguration planning problem is finding what sequence of reconfiguration actions are required for one arrangement of modules to transform into another. We present a novel reconfiguration planning algorithm for the SMORES form of modular robots. The algorithm compares the initial configuration with the goal configuration efficiently. The reconfiguration actions can be executed in a distributed manner so that each module can efficiently finish its reconfiguration task which results in a global reconfiguration for the system. In the end, the algorithm is demonstrated on the SMORES-EP self-reconfigurable modular robot hardware and some reconfiguration task examples are provided.

As a static network, the slow learning of back propagation (BP) neural network is an insurmountable disadvantage facing dynamic system identification. In contrast, dynamic neural network provides a potential choice, representing the development direction of neural network modeling, identification and control. Based on dynamic Elman network, a new learning neural network structure, called fast learning neural network, is proposed in this paper. It not only conforms to the basic characteristics of biological neural network, but also has the advantages of simple algorithm, fast learning convergence and high identification accuracy of linear and non-linear systems. Based on the experimental data, this network is applied to the identification of the motion model of modular robots to obtain the non-linear kinematics model of robots. Therefore, this kind of neural network is very suitable for robot kinematics model identification and motion control. The simulation results show that it is appropriate to identify the kinematics model and control the motion of the robot based on fast learning neural network; and the combination of fast learning and ELM network speeds up the training efficiency.

Development of modular robot control systems poses serious challenges associated with the robot’s construction subject to changes and the presence of a large number of degrees of freedom. The goal of this work was developing a versatile control system for modular hyper-redundant systems, able of independently finding ways to control robots with an arbitrary design from a certain given class. For solving the problem, a model of a control system was proposed, using logical-probabilistic knowledge discovery methods, adapted for control tasks. In accordance with the proposed approach, the task of control system training was reduced to finding patterns in an array of system’s environment interaction statistical data. For making the system independent on the chosen robot design, including modules’ spatial connection data specified in a data tree was proposed. Using this information during the training process allows the control system to independently tune in to control the robot, regardless of its design. For testing the proposed model’s performance and effectiveness, experiments in training a class of robots with different designs to move forward, which have confirmed both the learning rate and control quality being high.

Self-reconfigurable modular robots (SRM-robots) can autonomously change their shape according to different tasks and work environments, and have received considerable attention recently. Many reshaping/reconfiguration algorithms have been proposed. In this paper, we present a theoretical analysis of computational complexity on a reshape planning for a kind of lattice-type 3D SRM-robots, whose modules are of cubic shape and can move by rotating on the surfaces of other modules. Different from previous NP-completeness study on general chain-type robots (i.e. the motion of any chains and the location of modules can be arbitrary), we consider more practical constraints on modules’ shape (i.e. cubic shape), position (lying in 2D/3D grids) and motion (using orthogonal rotations) in this paper. We formulate the reshape planning problem of SRM-robots with these practical constraints by a (p, q) optimization problem, where p and q characterize two widely used metrics, i.e. the number of disconnecting/reconnecting operations and the number of reshaping steps. Proofs are presented, showing that this optimization problem is NP-complete. Therefore, instead of finding global optimization results, most likely approximation solution can be obtained for the problem instead of seeking polynomial algorithm. We also present the upper and lower bounds for the 2-tuple (p, q), which is useful for evaluating the approximation algorithms in future research.

This paper describes the assembling process of a quadrupedal architecture using the modular robotic system Mecabot. Several possible topologies are addressed to finally opt for a design that allows the use of an active column. Based on this, the mathematical model of the control is proposed to perform the movements of displacement, open turn and rotation. The locomotion profiles for these first two movement modalities are bio-inspired. For the rotation modality, a characteristic quadrupedal robot transition is used to allow the correct rotation execution without using a great number of degrees of freedom. The robot is tested on structured and unstructured terrains by measuring its speed in function of the movement frequency variation. For the open turn modality, the described circumference radius is measured in function of the offset variation. With the tests, the second Mecabot configuration with legs is finally obtained complementing the research work carried out for apodal configurations (snake, wheel caterpillar) and hexapod.

This paper studies the effects of changing environments on the evolution of bodies and brains of modular robots. Our results indicate that environmental history has a long lasting impact on the evolved robot properties. We show that if the environment gradually changes from type A to type B, then the evolved morphological and behavioral properties are very different from those evolving in a type B environment directly. That is, we observe some sort of “genetic memory”. Furthermore, we show that gradually introducing a difficult environment helps to reach fitness levels that are higher than those obtained under those difficult conditions directly. Finally, we also demonstrate that robots evolved in gradually changing environments are more robust, i.e., exhibit a more stable performance under different conditions.

This paper presents a novel modular differential drive soft robot (MDDSR) and its nonlinear model of crawling locomotion. The MDDSR consists of three differential drive soft modules (DDSMs) in series, and each module constructed by two bending soft actuators in parallel to be equally or differentially driven can achieve inchworm-like crawling movement with capability of straight and steering motion. Through sequential motions of the DDSMs, the MDDSR can perform straight and steering crawling locomotion. A nonlinear state-space kinematic model with the principle of minimum frictional work is built to characterize the MDDSR’s crawling locomotion, which is based on nonlinear bending behavior of the soft actuator and nonlinear crawling motion of the DDSMs. Feasibility of the proposed robot and its model is verified through locomotion experiments.

In this paper, adaptive neural networks (NNs) are employed in the event-triggered feedback control framework to enable a robot manipulator to track a predefined trajectory. In the proposed output feedback control scheme, the joint velocities of the robot manipulator are reconstructed using a nonlinear NN observer by using the joint position measurements. Two different configurations are proposed for the implementation of the controller depending on whether the observer is co-located with the sensor or the controller in the feedback control loop. Besides the observer NN, a second NN is utilized to compensate the effects of nonlinearities in the robot dynamics via the feedback control. For both the configurations, by utilizing observer NN and the second NN, torque input is computed by the controller. The Lyapunov stability method is employed to determine the event-triggering condition, weight update rules for the controller, and the observer for both the configurations. The tracking performance of the robot manipulator with the two configurations is analyzed, wherein it is demonstrated that all the signals in the closed-loop system composed of the robotic system, the observer, the event-sampling mechanism, and the controller are locally uniformly ultimately bounded in the presence of bounded disturbance torque. To demonstrate the efficacy of the proposed design, simulation results are presented.

Self-assembling robots have the potential to undergo autonomous morphological adaptation. However, due to the simplicity in their hardware makeup and their limited perspective of the environment, self-assembling robots are often not able to reach their potential and adapt their morphologies to tasks or environments without external cues or prior information. In this paper, we present supervised morphogenesis — a control methodology that makes self-assembling robots truly flexible by enabling aerial robots to exploit their elevated position and better view of the environment to initiate and control (hence supervise) morphology formation on the ground. We present results of two case studies in which we assess the feasibility of the presented methodology using real robotic hardware. In the case studies, we implemented supervised morphogenesis using two different aerial platforms and up to six self-assembling autonomous robots. We furthermore quantify the benefits attainable for self-assembling robots through cooperation with aerial robots using simulation-based studies. The research presented in this paper is a significant step towards realizing the true potential of self-assembling robots by enabling autonomous morphological adaptation to a priori unknown tasks and environments., SCOPUS: ar.j, info:eu-repo/semantics/published

The decentralized self-reconfiguration of modular robots has been a challenging problem. This work proposed a biological method inspired by the plant growth for the distributed self-reconfiguration of UBot systems. L-systems are implemented to construct target topology, and turtle interpretation is extended to lead the self-reconfiguration process. Parametric reproduction rules introduce the external influence to the reconfiguration process by distributed modules’ local sensing. Each module can move independently to change relative positions, and robotic structures develop in the natural growth style. This leads to a convergent and environmentally sensitive control method for the distributed self-reconfiguration. Reconfiguration processes can converge to desired configuration and are scalable to module numbers by reproducing predefined substructures in principle. The overall performance of the proposed strategy is evaluated with simulations and 11 experiments. Simulation and experimental results turn out to be convergent and environmentally sensitive.

Soft modular robots have the advantages of both bionic continuum robots and modular self-reconfigurable robots. They have potential application for working in narrow space and uneven terrain. The outstanding abilities of the soft modular robots are infinite degrees of freedom and a changeable configuration. However, these advantages also incur difficulty in mathematical modeling and continuous motion control, especially for docking, which is the key to realizing a changeable configuration. Therefore, the development of a soft modular robot requires a modeling method to guide the module's motions accurately. In this paper, we design and manufacture a pneumatic soft modular robot with a novel connecting mechanism to achieve docking. A nonlinear dynamic model of our soft module is established. In particular, the analytic solutions of the module plane docking are presented. Based on the nonlinear dynamic modeling, three experiments are performed under different conditions. The experimental results pertaining to module plane bending agree with the theoretical values, which verify the accuracy of the modeling method. Moreover, fixed point docking and two modules docking experiments demonstrate the validity of the modeling method and the docking ability. The modeling method proposed in this paper can well guide the docking of soft modular robots.

In this work, the dynamics of "n" legged modular miniature robots with a soft body is modeled. The dynamic formulation is obtained using Newton-Euler formulation that depends on the contact parameters and the feet closed-chain kinematic analysis. The dynamic model determines the locomotion parameters of each module as an individual system as well as the dynamics of the whole robot in a 3D space; i.e., the robot is modeled as one system, and modules are considered to be sets of flexible links connected within this system. Kinematic constraints among these modules are obtained by considering the type of backbone integrated into the modular robot. Various types of backbones are used that are classified into three groups: rigid, only torsional, and soft. The model is verified using SMoLBot, an origami-inspired miniature robot made of multiple modules and soft/rigid backbones. Additional to the dynamic model, the effect of different sets of design parameters on the locomotion of the legged soft-bodied modular miniature robots is studied. Analyses comparing the velocity of SMoLBot with a different number of modules and various types of backbones are presented using the proposed dynamic model. Our results show the existence of an optimum backbone torsional stiffness for legged miniature modular robots and an optimum number of legs for a given backbone stiffness that maximizes the robot's velocity. In this research, presented results and locomotion study show that the robot's design should be iteratively improved based on specific optimum goals for exclusively defined task to satisfy the operational needs. (C) 2021 Elsevier B.V. All rights reserved.