Modular Robot Research Articles

Decentralized fault-tolerant control of modular robot manipulators with actuator saturation: neural adaptive integral terminal sliding mode-based control approach

A novel neural adaptive integral terminal sliding mode control for decentralized fault-tolerant control strategy, including the integral terminal sliding mode surface, the nonlinear disturbance observer, the radial basis neural network and robust controller, is presented in this paper to achieve fault-tolerant control of modular robot manipulators. First, the integral terminal sliding mode is designed for the fault-tolerant controller. Then, to boost the performance of the controlled system, the radial basis neural network and disturbance observer are introduced to approximate the nonlinear terms and disturbances. The reconstructed approximate uncertainty term is applied as compensation. Next, the super-twisting technique is employed to compensate for estimation errors to ensure stability. In addition, for the actuator saturation problem, the radial basis function neural network-based compensation control is proposed. Finally, the stability of the closed-loop robotic system is demonstrated based on Lyapunov theory. Computer simulations verified the efficiency and advantages of the presented approach.

Cooperative Game-Based Approximate Optimal Control of Modular Robot Manipulators for Human-Robot Collaboration.

Major challenges of controlling human-robot collaboration (HRC)-oriented modular robot manipulators (MRMs) include the estimation of human motion intention while cooperating with a robot and performance optimization. This article proposes a cooperative game-based approximate optimal control method of MRMs for HRC tasks. A harmonic drive compliance model-based human motion intention estimation method is developed using robot position measurements only, which forms the basis of the MRM dynamic model. Based on the cooperative differential game strategy, the optimal control problem of HRC-oriented MRM systems is transformed into a cooperative game problem of multiple subsystems. By taking advantage of the adaptive dynamic programming (ADP) algorithm, a joint cost function identifier is developed via the critic neural networks, which is implemented for solving the parametric Hamilton-Jacobi-Bellman (HJB) equation and Pareto optimal solutions. The trajectory tracking error under the HRC task of the closed-loop MRM system is proved to be ultimately uniformly bounded (UUB) by the Lyapunov theory. Finally, experiment results are presented, which reveal the advantage of the proposed method.

Computational Systems Design of Low-Cost Lightweight Robots

With the increased demand for customisation, developing task-specific robots for industrial and personal applications has become essential. Collaborative robots are often preferred over conventional industrial robots in human-centred production environments. However, fixed architecture robots lack the ability to adapt to changing user demands, while modular, reconfigurable robots provide a quick and affordable alternative. Standardised robot modules often derive their characteristics from conventional industrial robots, making them expensive and bulky and potentially limiting their wider adoption. To address this issue, the current work proposes a top-down multidisciplinary computational design strategy emphasising the low cost and lightweight attributes of modular robots within two consecutive optimisation problems. The first step employs an informed search strategy to explore the design space of robot modules to identify a low-cost robot architecture and controller. The second step employs dynamics-informed structural optimisation to reduce the robot’s net weight. The proposed methodology is demonstrated on a set of example requirements, illustrating that (1) the robot modules allow exploring non-intuitive robot architectures, (2) the structural mass of the resulting robot is 16 % lower compared to a robot designed using conventional aluminium tubes, and (3) the designed modules ensure the physical feasibility of the robots produced.

Tansig type NLESO-based integral terminal sliding mode control for modular robot manipulators with uncertain disturbance: Theory and experimental verification

To overcome the mutual constraints between the system chattering and the arrival time in conventional sliding mode control in this paper, a tangent excitation function (tansig) type nonlinear extended state observer (NLESO)-based integral terminal sliding mode control is proposed to solve the trajectory tracking control task of the modular robot manipulators (MRMs) possessing strong coupling and complex time-varying properties. Through the joint torque feedback technology, the MRM system dynamic model is formulated; on this basis, the proposed NLESO based on tansig is utilized to estimate and compensate the system uncertain information, which are consisted of the friction, interconnected dynamic coupling, and external disturbance. The proposed NLESO can simplify the traditional extended state observer design parameters for theoretical analysis and practical application. The proposed tansig-type NLESO-based integral terminal sliding mode control integrated adaptive term exponential reaching law for MRMs is designed to diminish the arrival time when the system state converges to the equilibrium point. The presented algorithm improves the robotic system antidisturbance ability. Furthermore, the Lyapunov theory is utilized to verify stability of the proposed NLESO and closed-loop robotic system. Finally, the availability of the proposed algorithm is demonstrated by experiments.

Event‐triggered adaptive fuzzy optimal control of modular robot manipulators using zero‐sum differential game through value iteration

SummaryIn this article, a novel event‐triggered zero‐sum differential game through value iteration (VI)‐based adaptive fuzzy control is proposed for modular robot manipulators (MRMs) with uncertain disturbance. The subsystem dynamic of MRMs is established by joint torque feedback (JTF) technology. An identifier based on fuzzy logic model (FLM) is established to identify unknown system dynamics. Then, the trajectory tracking control issue of MRMs with uncertain disturbance is transformed into a two‐player zero‐sum differential game. On the basis of event‐triggered mechanism and VI algorithm, the event‐triggered Hamilton–Jacobi–Issacs (HJI) equation is approximately solved through establishment of actor‐critic structure. Different from the traditional adaptive dynamic programming (ADP) algorithm, this scheme is based on adaptive fuzzy optimal control through an actor‐critic structure, which refers to adaptation of both optimal cost function and optimal control policy. Moreover, tracking errors of closed‐loop MRM system are demonstrated to be uniformly ultimately bounded (UUB) under Lyapunov stability theorem. At last, experimental examples and comparisons show the reliability and effectiveness of this method.

Motion Planning for Variable Topology Trusses: Reconfiguration and Locomotion

Truss robots are highly redundant parallel robotic systems that can be applied in a variety of scenarios. The variable topology truss (VTT) is a class of modular truss robots. As self-reconfigurable modular robots, a VTT is composed of many edge modules that can be rearranged into various structures depending on the task. These robots change their shape by not only controlling joint positions as with fixed morphology robots but also reconfiguring the connectivity between truss members in order to change their topology. The motion planning problem for VTT robots is difficult due to their varying morphology, high dimensionality, the high likelihood for self-collision, and complex motion constraints. In this article, a new motion planning framework to dramatically alter the structure of a VTT is presented. It can also be used to solve locomotion tasks that are much more efficient compared with previous work. Several test scenarios are used to show its effectiveness.

Integration of a surgical robotic arm to the connected operating room via ISO IEEE 11073 SDC

PurposeSince 2019, intraoperative networking with ISO IEEE 11073 SDC has, for the first time, enabled standardized multi-vendor data exchange between medical devices. For seamless plug-and-play integration of devices without previous configuration, further specifications for device profiles (“device specializations”) on top of the existing core standards must be developed. These generic interfaces are then incorporated into the standardization process.MethodsAn existing classification scheme of robotic assistance functions is being adopted and used as a baseline to derive functional requirements for a universal interface for modular robot arms. Additionally, the robot system requires machine-machine interfaces (MMI) to a surgical navigation system and a surgical planning software in order to carry out its function. Further technical requirements are derived from these MMI. The functional and technical requirements motivate the design of an SDC-compatible device profile. The device profile is then assessed for feasibility.ResultsWe present a new modeling of a device profile for surgical robotic arms intended for neurosurgery and orthopedic surgery. The modeling in SDC succeeds for the most part. However, some details of the proposed model cannot yet be realized within the framework of the existing SDC standards. Some aspects can already be realized, but could be better supported in the future by the nomenclature system. These improvements are being presented as well.ConclusionThe proposed device profile presents a first step toward a uniform technical description model for modular surgical robot systems. The current SDC core standards lack some functionality to fully support the proposed device profile. These could be defined in future work and then included in standardization efforts.

Complete coverage path planning for reconfigurable omni-directional mobile robots with varying width using GBNN([formula omitted

Self-reconfigurable robots which can change their footprint’s width have demonstrated the ability to access confined areas. To enhance the efficiency of area coverage in complete coverage path planning (CCPP) for complex and confined environments, it is advantageous to cover areas fast in wide areas while maintaining the ability to navigate through tight spaces and cover hard to access areas. This can be achieved by leveraging the flexibility of an omnidirectional self-reconfigurable robot footprint. The widest footprint is used to speed up the area coverage when there are no obstacles around, while the smallest footprint is used to navigate through tight spaces. However, the generation of robotic width reconfiguration state during autonomous CCPP generation, poses challenges. In this paper, a CCPP for omni-directional robots of varying width with n-reconfiguration states is proposed. To this end, the proposed CCPP is a modified GBNN with n-reconfiguration states (GBNN(n)). It generates the global path autonomously, which determines the robot width as per the nth reconfiguration states so as to increase area coverage in open areas and reduce robot footprint in tight spaces. The proposed complete coverage path planning are compared against state-of-the-art GBNN and CCPP optimization using depth-limited search and successfully demonstrate that the proposed algorithm helps robots of varying widths achieve higher area coverage in lesser steps, energy and distance. The supporting simulation and experimental video link11Link for simulation and experimental video: https://tinyurl.com/2n4n7ry9. is also provided to highlight the outcomes.

An RRT That Uses MSR-Equivalence for Solving the Self-Reconfiguration Task in Lattice Modular Robots

A modular self-reconfigurable robot can change its configuration to effectively adapt itself to perform a wide range of tasks. The Modular Self-reconfigurable Robot (MSR) is constructed from a collection of connected modules that can move relative to each other and change the system's shape. This research presents a novel sliding Triangular Lattice Modular Robot (STRIMOR). We use a Rapidly-exploring random tree (RRT*) algorithm to solve the self-reconfiguration (SR) problem. This algorithm is computationally expensive due to the configuration space dimension. In order to increase the number of modules we define ‘Topological Configuration’ which better describes the configuration's unique properties. This new approach significantly reduces the tree size by representing a set of MSR-equivalence configurations as a single topological configuration. Furthermore, we examine the advantage of moving several module-groups in a single movement and choose their number by several density functions. Our improvements are presented using a set of simulated SR problems with different complexity levels. On top of that, We designed and fabricated the STRIMOR's dual-type modules and discuss how such a design simplifies the system's mechanical complexity.

Fuzzy logic nonzero-sum game-based distributed approximated optimal control of modular robot manipulators with human-robot collaboration

In this paper, a fuzzy logic nonzero-sum game-based distributed approximated optimal control scheme is presented for modular robot manipulators (MRMs) with human-robot collaboration (HRC) tasks. The MRM dynamic model is formulated by using joint torque feedback (JTF) technique. Based on the differential game strategy, the optimal control problem of HRC task-oriented MRM systems is transformed into a nonzero-sum game problem of multiple robotic subsystems. By taking advantage of the adaptive dynamic programming (ADP) algorithm, the distributed approximate optimal control policy under HRC tasks is developed by a novel fuzzy logic nonzero-sum game manner for solving the coupled Hamilton–Jacobian (HJ) equation. The trajectory tracking error under HRC task of the closed-loop MRM system is proved to be ultimately uniformly bounded (UUB) using the Lyapunov theory. Finally, experiment results have been presented, which demonstrate the advantage and effectiveness of the developed method.

Modular reconfiguration of DNA origami assemblies using tile displacement.

The power of natural evolution lies in the adaptability of biological organisms but is constrained by the time scale of genetics and reproduction. Engineeringartificial molecular machines should not only include adaptability as a core feature but also apply it within a larger design space and at a faster time scale. A lesson from engineering electromechanical robots is that modular robots can perform diverse functions through self-reconfiguration, a large-scale form of adaptation. Molecular machines made of modular, reconfigurable components may form the basis for dynamic self-reprogramming in future synthetic cells. To achieve modular reconfiguration in DNA origami assemblies, we previously developed a tile displacement mechanism in which an invader tile replaces another tile in an array with controlled kinetics. Here, we establish design principles for simultaneous reconfigurations in tile assemblies using complex invaders with distinct shapes. We present toehold and branch migration domain configurations that expand the design space of tile displacement reactions by two orders of magnitude. We demonstrate the construction of multitile invaders with fixed and variable sizes and controlled size distributions. We investigate the growth of three-dimensional (3D) barrel structures with variable cross sections and introduce a mechanism for reconfiguring them into 2D structures. Last, we show an example of a sword-shaped assembly transforming into a snake-shaped assembly, illustrating two independent tile displacement reactions occurring concurrently with minimum cross-talk. This work serves as a proof of concept that tile displacement could be a fundamental mechanism for modular reconfiguration robust to temperature and tile concentration.

Genetic algorithm-based optimal design of modular robot topology based on distributed parallel kinematic modeling and analysis

Task-oriented design of modular robot topology is a crucial issue to be solved in modular robot research. In this paper, a Genetic algorithm-based optimal design strategy for the modular robot topology is proposed. Four tuples are used to represent the modular robot topology. By establishing a database of motion transmission forms for a modular unit, a distributed parallel kinematic modeling and analysis method for modular robots is introduced. Taking the four-tuple representation of the modular robot topology as decision variables, an optimization model of the topology design for modular robots is established. To solve the model, under the framework of the Genetic algorithm, the genotype and genetic operations are designed to improve the effectiveness of individual generating in the optimization process. Finally, a modular robot is taken as an example to carry out simulations. It indicates that the modular robot under the designed topology can successfully execute tasks with as few modular units as possible, and the computing time for the kinematics modeling and analysis of modular robots can be reduced by at least an order of magnitude.

Meta‐Module Mutual Assistance: A Bioinspired Design for Self‐Assembly of Modular Space Robot

The theoretical capability of modular robots to organize the overall robot into different structures with different functions has broad prospects in space exploration. Therefore, a novel modular space robot named Space Module is developed, and a self‐assembly strategy inspired by biological cooperative and mutual assistance behaviors is proposed. First, the meta‐module method is utilized to endow nonmobile modules with the mobility. Second, the mutual assistance is presented to achieve position and posture reachability of the assembly unit while minimizing the effect of meta‐modules on granularity. Then, according to the unique motion characteristics of the meta‐module and mutual assistance, an assembly planner is designed to obtain the assembly sequences to realize the self‐manufacturing of desired configurations. Finally, the validity and feasibility of the proposed assembly strategy are further confirmed by demonstrations. An interactive preprint version of the article can be found here: https://doi.org/10.22541/au.167264888.81237462/v1.

Auto-Optimizing Connection Planning Method for Chain-Type Modular Self-Reconfiguration Robots

Chain-type modular robots are capable of self-reconfiguration (SR), where the connection relationship between modules is changed according to the environment and tasks. This article focuses on the connection planning of SR based on multiple in-degree single out-degree (MISO) modules. The goal is to calculate the optimal connection planning solution: the sequence with the fewest detachment and attachment actions. To this end, we propose an auto-optimizing connection planning method that contains a polynomial-time algorithm to calculate near-optimal solutions and an exponential-time algorithm to further optimize the solutions automatically when some CPUs are idle. The method combines rapidity and optimality in the face of an NP-complete problem by using configuration pointers, strings that uniquely specify the robot's configuration. Our polynomial-time algorithm, in-degree matching (IM) uses the interchangeability of connection points to reduce reconfiguration steps. Our exponential-time algorithm, tree-based branch and bound (TBB) further optimizes the solutions to the optimum by a new branching strategy and stage cost. In the experiments, we verify the feasibility of the auto-optimizing method combining IM and TBB, and demonstrate the superiority of IM over Greedy-CM in the SR of MISO modules and the near-optimality of IM compared to the optimal solutions of TBB.

Factors Impacting Diversity and Effectiveness of Evolved Modular Robots

In many natural environments, different forms of living organisms successfully accomplish the same task while being diverse in shape and behavior. This biodiversity is what made life capable of adapting to disrupting changes. Being able to reproduce biodiversity in artificial agents, while still optimizing them for a particular task, might increase their applicability to scenarios where human response to unexpected changes is not possible. In this work, we focus on Voxel-based Soft Robots (VSRs), a form of robots that grants great freedom in the design of both morphology and controller and is hence promising in terms of biodiversity. We use evolutionary computation for optimizing, at the same time, morphology and controller of VSRs for the task of locomotion. We investigate experimentally whether three key factors—representation, Evolutionary Algorithm (EA), and environment—impact the emergence of biodiversity and if this occurs at the expense of effectiveness. We devise an automatic machine learning pipeline for systematically characterizing the morphology and behavior of robots resulting from the optimization process. We classify the robots into species and then measure biodiversity in populations of robots evolved in a multitude of conditions resulting from the combination of different morphology representations, controller representations, EAs, and environments. The experimental results suggest that, in general, EA and environment matter more than representation. We also propose a novel EA based on a speciation mechanism that operates on morphology and behavior descriptors and we show that it allows to jointly evolve morphology and controller of effective and diverse VSRs.

Modular robot interface for a smart operating theater.

This paper discusses the architecture and implementation of a modular component of the smart operating theater digital twin, designed to control robotic equipment-the robot interface module. This interface is designed to ensure equipment operation both in a real smart operating theater and in the virtual space of its digital twin-the computer simulation. Using such an interface in the digital twin will make it possible to use it in computer-assisted training of surgeons, preliminary planning, post-analysis, and simulation, preceding the operation of real equipment. An experimental implementation of a prototype robot interface to enable the KUKA LBR Med 14 R820 medical robot operation using the FRI protocol was developed, and experiments were performed on real equipment and its digital twin.

Distributed Size-constrained Clustering Algorithm for Modular Robot-based Programmable Matter

Modular robots are defined as autonomous kinematic machines with variable morphology. They are composed of several thousands or even millions of modules that are able to coordinate to behave intelligently. Clustering the modules in modular robots has many benefits, including scalability, energy-efficiency, reducing communication delay, and improving the self-reconfiguration process that focuses on finding a sequence of reconfiguration actions to convert robots from an initial shape to a goal one. The main idea of clustering is to divide the modules in an initial shape into a number of groups based on the final goal shape to enhance the self-reconfiguration process by allowing clusters to reconfigure in parallel. In this work, we prove that the size-constrained clustering problem is NP-complete, and we propose a new tree-based size-constrained clustering algorithm called “SC-Clust.” To show the efficiency of our approach, we implement and demonstrate our algorithm in simulation on networks of up to 30000 modules and on the Blinky Blocks hardware with up to 144 modules.

Self-reconfiguration of PARTS: A parallel reconfiguration algorithm based on surface flow

In this paper, we present a parallel reconfiguration algorithm for shape-shifting modular robots with a triangular structure. The reconfiguration planning is based on partitioning the robot’s surface into source and sink sections for modules, using the largest common topology as a reference. Reconfiguration is realized by a synchronous surface flow of modules guided by the prior determination of module sources and sinks. Individual reconfiguration steps are carried out by a multi-step optimization framework, ensuring that intermediate configurations required for topology changes are valid and collision-free. With a configuration containing n modules, the algorithm completes the reconfiguration in O(n) reconfiguration steps and allows for a distributed and asynchronous hardware implementation. We demonstrate the performance of the proposed algorithm on multiple example configurations and compare the results to other reconfiguration approaches.

Cone–hole docking mechanism for a modular reconfigurable mobile robot and its characteristic analysis

Purpose The purpose of this paper is that the modular mobile robots reformed the multimachine joint mode to achieve obstacle-crossing, climbing and other multifunctional inspection in unstructured environment under the connection of the cone–hole docking mechanism. Design/methodology/approach An arc-shaped docking cone head with a posture-maintaining spring and two arc-shaped connecting rods that formed a ring round hole were designed to achieve large tolerance docking. Before active locking, the coordination between structures was used to achieve passive locking, which mitigated the docking impact of modular robots in unstructured environment. Using the locking ring composed of the two arc-shaped connecting rods, open-loop and closed-loop motion characteristics were obtained through the mutual motion of the connecting rod and the sliding block to achieve active locking, which not only ensured high precision docking, but also achieved super docking stability. Findings The cone–hole docking mechanism had the docking tolerance performance of position deviation of 6mm and pitch deviation of 8° to achieve docking of six degrees of freedom (6-DOF), which had a load capacity of 230 N to achieve super docking stability. Under the connection of the cone–hole docking mechanism, the modular mobile robots reformed the multimachine joint mode to achieve obstacle-crossing, climbing and other multifunctional inspection in unstructured environment. Originality/value Based on mechanical analysis of universal models, a cone–hole docking mechanism combining active and passive functions, six-dimensional constraints could be implemented, was proposed in this paper. The characteristics of the posture-maintaining spring in the cone docking head and the compression spring at the two ends of two arc-shaped connecting rods were used to achieve docking with large tolerance. Passive locking and active locking modules were designed, mitigating impact load and the locking did not require power to maintain, which not only ensured high precision docking, but also achieved super docking stability.

Enabling Modular Robotics with Secure Transducer Identification Based on Extended IEEE 21450 Transducer Electronic Datasheets

In robotics, there are many different sensors and actuators mounted onto a robot which may also, in the case of modular robotics, be interchanged during operation. During development of new sensors or actuators, prototypes may also be mounted onto a robot to test functionality, where the new prototypes often have to be integrated manually into the robot environment. Proper, fast and secure identification of new sensor or actuator modules for the robot thus becomes important. In this work, a workflow to add new sensors or actuators to an existing robot environment while establishing trust in an automated manner using electronic datasheets has been developed. The new sensors or actuators are identified via near field communication (NFC) to the system and exchange security information via the same channel. By using electronic datasheets stored on the sensor or actuator, the device can be easily identified and trust can be established by using additional security information contained in the datasheet. In addition, the NFC hardware can simultaneously be used for wireless charging (WLC), thus allowing for wireless sensor and actuator modules. The developed workflow has been tested with prototype tactile sensors mounted onto a robotic gripper.