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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.

The ability of real-time tracking of manipulator end-effect is critical for intelligent manipulators. This paper presents a control strategy for tracking the continuous moving object. Our research aims at converting the problem of tracking moving object to a planning problem of relative velocity vector by establishing Relative Velocity Coordinate (RVC) on manipulator end-effect. The key novelty of the proposed strategy about constructing the optimization problem lies in considering both relative velocity and relative position simultaneously. The Beetle Antennae Search (BAS) algorithm is introduced to tackle the formulated optimization function. Theory analysis and different simulation experiments are provided to illustrate the feasibility and efficiency of the proposed control strategy.

Pneumatic artificial muscle (PAM) is a popular bionic actuator featuring flexible actuation. However, the inherent hysteresis of PAM reduces its tracking accuracy. This paper proposed a feedforward-feedback composite control method to compensate for the hysteresis nonlinearity of the PAM in a 2 degrees-of-freedom (DOF) Delta mechanism. Direct inverse modeling approach was utilized to obtain the inverse hysteresis model of the PAM directly from the measurement without inversion calculation. Subsequently, the inverse hysteresis model was cascaded in the feedforward loop to attenuate the PAM's hysteresis. Further, PID feedback control was also integrated to account for the unmodeled dynamics and the modeling uncertainties of the inverse hysteresis model. Experimental results showed that most of the angular tracking error could be reduced to the range of ± 0.5 °, which indicated that the proposed feedforward-feedback composite control method could effectively compensate the hysteresis of the PAM.

Due to the backlash hysteresis, robust and accurate tracking control of a hysteresis system is a challenging problem. We propose in this paper an active observer based robust control scheme to reject the influence of hysteresis. Firstly, the normal piecewise C–H model of hysteresis plant was modified into a continuous form so that an active observer could be applied on. Secondly, an observer was designed based on the extended set-membership filter (ESMF). This intends to take the advantages of ESMF on the bound-guaranteed estimation and the unknown but bounded (UBB) noise tolerance. Thirdly, a robust controller was constructed using both the magnitude and the bounds of the hysteresis uncertainty estimated by the observer. The stability of the control scheme was theoretically proved. Experiments were conducted on a robotic flexible ureteroscope test-bed. The performance of the modified model, the ESMF-based observer, and the robust control were extensively verified, and the improvements benefited from this proposed scheme were demonstrated.

Soft modular robots have advantages, including infinite degrees of freedom and various configurations. Most soft robots are actuated by inflating air pressure into their chambers. However, each chamber is connected to a tube that provides the air supply, which incurs drag and intertwining problems that influence the robot&rsquo, s motion. Moreover, the number of chambers directly affects the deformations and motion capabilities of the robot. Therefore, the crucial issue is the structure of a soft modular robot that can share an air source without reducing the number of chambers and can guarantee the deformations of the robot. In this paper, a novel mechatronics-embedded soft module was designed and manufactured, which has an air supply sharing function. Therefore, the soft modular robot can be powered via a single air tube. In addition, a wireless platform to control the air pressure of the module was built, and an experimental model was established to obtain the relationship between the deformation and pressure of the module. Four experiments were performed under different conditions. The experiments&rsquo, results indicate the bending capability of the module. Moreover, hooking object, twisting motion, and bionic gesture experiments demonstrate the validity of the module&rsquo, s air pressure sharing function. Therefore, the air sharing supply approach proposed in this paper can be used as a reference to solve the tube drag problem of soft modular robots.

Self-reconfigurable robots are built by modules which can move in relationship to each other, which allows the robot to change its physical form. Finding a sequence of module moves that reconfigures the robot from the initial configuration to the goal configuration is a hard task and many control algorithms have been proposed. In this paper, we present a novel method which combines a cluster-flow locomotion based on cellular automata together with a decentralized local representation of the spatial geometry based on membrane computing ideas. This new approach has been tested with computer simulations and real-world experiments performed with modular self-reconfigurable robots and represents a new point of view with respect other control methods found in the literature. National Natural Science Foundation of China U1713201 State Key Laboratory of Robotics 2018-O10

The self-reparation of modular self-reconfigurable robots is a fundamental primitive that can be used as part of higher-lever functionality. A bio-inspired approach is proposed for distributed self-reparation, which can lead the robot recover from module fails to the initial configuration. This approach is inspired by the natural growth of plant. The L-systems for describing natural growth is translated to construct robotic structures. Robots reconstruct lost parts by leading other modules to needed positions through the symbol rewriting strategy in L-systems. Simulations and experiments on Seremo robots are provided to verify this method with successful reparation of module fails or removed from the global structure.

Rolling is a frequently used high-efficiency motion pattern for articulated loop robots. An important issue is planning the angle of each joint to keep a loop closed on the fly and to roll forward stably. Most of the current approaches achieve a gait table by designing a few key postures or a numerical approximation of a special loop geometry (e.g., an ellipse), which is difficult to implement for non-experts and makes motion planning time-consuming. The change in joint angles for many current loop gaits is not smooth. The serpenoid curve, whose curvature changes sinusoidally, exhibits the advantage of making angle changes smooth. Based on this feature, a generalized Serpenoid Polygon model for loop gait is proposed, which extends Hirose's Serpenoid Oval to Polygon. Furthermore, we derive its scalable planning model for loop robots with different numbers of joints, which is easy to implement. The influence of key parameters on the performance of this model is investigated through numerous dynamics simulations with a general linkage-mechanism. In addition, loop forming and rolling-pattern switching are studied to facilitate the implementation of this model. In the end, the applications of the model are discussed, its effectiveness is validated through experiments using the UBot modular robot. A serpenoid polygonal rolling model is proposed for articulated modular robots.The outer shape can be changed to adapt to varying environments and tasks.Investigating switching among various loop patterns facilitates the model application.The effectiveness is validated by physical experiments.The model is scalable and can be implemented in similar modular robots.

The most important feature of Modular Self-reconfigurable Robot (MSRR) is the adaption to complex environments and changeable tasks. A critical difficulty is that the operator should regulate a large number of control parameters of modules. In this paper, a novel locomotion control model based on chaotic Central Pattern Generator (CPG) is proposed. The chaotic CPG could produce various rhythm signals or chaotic signal only by changing one parameter. Utilizing this characteristic, a unified control model capable of switching variable locomotion patterns or generating chaotic motion for modular self-reconfigurable robot is presented. This model makes MSRR exhibit environmental adaptability. The efficiency of the control model is verified through simulation and experiment of UBot MSRR platform.

In recent years, soft modular robots have become popular among researchers with the development of soft robotics. However, the absence of a visual 3D simulation platform for soft modular robots hold back the development of the field. The three-dimensional simulation platform plays an important role in the field of multi-body robots. It can shorten the design cycle, reduce costs, and verify the effectiveness of the optimization algorithm expediently. Equally importantly, evolutionary computation is a very effective method for designing the controller of multi-body robots and soft robots with hyper redundancy and large parametric design space. In this paper, a tradeoff between the structural complexity of the soft modular robot and computational power of the simulation software is made. A reconfigurable soft modular robot is designed, and the open-source simulation software VOXCAD is re-developed to simulate the actual soft robot. The evolutionary algorithm is also applied to search for the most efficient motion pattern for an established configuration in VOXCAD, and experiments are conducted to validate the results.

Self-reconfiguration of modular self-reconfigurable robots is a fundamental function that can be used as part of higher-level functionality. Interaction with the environment is a key factor affecting the self-reconfiguration process of modular robots. In this article, a modeling framework that makes it possible to simulate and visualize the interactions at the level of decentralized modules will be introduced. The framework extends the formalism of Lindenmayer systems (L-systems) with constructs needed to model robotic information exchanged between decentralized modules and their surrounding environments. Both the construction of target configurations and environmental sensitive adaption can be handled by extending L-system symbols and reproduction rules. The proposed method is illustrated with simulations capturing the development of branching structures while adapting to environmental obstacles.

Modeling and controlling self-reconfiguration of modular robots is still a challenging problem in the field of distributed control. The two main constrains are the design of target shapes and the absence of global state for decentralized modules. We present a new way for those two problems inspired from the developmental process of plant growth. As a mathematical theory of plant development, L-systems capture the essence of growth process. We extend L-systems to the self-reconfiguration process of modules robots. Target configurations will be described in a string of symbols, and robotic structures capture fractal characters through the rewriting function. Extended graphical interpretation of L-system symbols can generate module-level predictions about robotic global states. Simulations of different self-reconfiguration processes illustrate the proposed method.

This paper presents a synthetic algorithm for tracking a moving object in a multiple-dynamic obstacles environment based on kinematically planar manipulators. By observing the motions of the object and obstacles, Spline filter associated with polynomial fitting is utilized to predict their moving paths for a period of time in the future. Several feasible paths for the manipulator in Cartesian space can be planned according to the predicted moving paths and the defined feasibility criterion. The shortest one among these feasible paths is selected as the optimized path. Then the real-time path along the optimized path is planned for the manipulator to track the moving object in real-time. To improve the convergence rate of tracking, a virtual controller based on PD controller is designed to adaptively adjust the real-time path. In the process of tracking, the null space of inverse kinematic and the local rotation coordinate method (LRCM) are utilized for the arms and the end-effector to avoid obstacles, respectively. Finally, the moving object in a multiple-dynamic obstacles environment is thus tracked via real-time updating the joint angles of manipulator according to the iterative method. Simulation results show that the proposed algorithm is feasible to track a moving object in a multiple-dynamic obstacles environment.

Like cellular systems--Modular Self-Reconfigurable Robots (MSRR)--accomplish certain tasks through coordination of numerous independent modules. At the center of Cellular Automation (CA) is the sliding cube model (SCM) that is a mainstay supporting theoretical developments. Motion constraints of physical modules limit the application of CA method in real robotic systems. This paper proposes a new strategy for implementing CA on MSRR--which is a synergy of CA rules and modular design. Firstly, using the geometric expression of CA rules for SCM, a 2-DOF cube-shaped MSRR module (UBot system) is proposed, which lays the foundation for implementation of unified and highly effective modular locomotion criteria. Secondly, cellular rules are arranged according to the locomotion property of UBot module, and distributed control algorithm is designed for the robot to explore unknown environments. Simulations results verified this approach with reconfiguration locomotion of UBot robots in diverse unfamiliar environments. Hardware experiment with 16 modules also indicates the physical feasibility of the method.

Distributed control, represented by Cellular Automata (CA), has advantage over centralized control for Modular Self-Reconfigurable Robot (MSRR) in capability of extension, efficiency and robustness. But due to the fact that the modular constraints, e.g., the number of DOF and the physical geometry, etc., the ideal CA rules cannot be implemented on the real robot system easily. The goal of the research is on design and implementation of distributed control based on integrated module design and CA rules arrangement. 2-DOF cubic-shaped modular model is first proposed, that is the Ubot module. Cellular rules are arranged according to the locomotion property of UBot module, and distributed control algorithm is designed for the robot to cross the uneven terrain. In simulation and on physical robotic hardware platform, experiments are carried out for UBot to verify the validity of CA theory applied on reconfiguration locomotion of UBot MSRR systems in complex environment.

Self-reconfiguration of Modular Self-Reconfigurable (MSR) robots is a fundamental primitive that can be used as part of higher-lever functionality. The biological principle of self-organizing and growth in both animals and plants is learned to solve the automatic planning of configuration and control methods. A bioinspired approach is proposed for distributed self-reconfiguration. This method aims at utilizing the self-organization and pattern emergence of the MSR robotic system, which is made of large quantity of modules to promote the system to reconfigure in the direction of adapting to environments and tasks. This mechanism is hybrid by combining Lindenmayer systems (L-systems) and Cellular Automata (CA). L-systems are introduced for defining global structure and providing topological description. Cellular Automata (CA) handle motion planning of distributed modules. This method is absolutely distributed and convergent to self-adaptive structures with desired organizing principles. The convergence of proposed method is verified through simulations and experiments on UBot robots.

Self-reconfiguration of self-reconfigurable robots is a fundamental primitive that can be used as part of higher-lever functionality. A distributed control mechanism is proposed for self-reconfiguration, which uses the Lindenmayer systems (L-systems) for global configuration description and cellular automata for local motion planning. Turtle graphics are taken for structure interpretation of L-systems and provide moving targets for decentralized modules. The modules are led to their final positions using a gradient approach. The mechanism takes a split where distributed locomotion aspects of the control is managed by cellular automata, while global description in L-systems. Turtle interpretations can generate module-level predictions from global description. The convergence of proposed method is verified through simulations.

Modular self-reconfigurable robots (SRRs) have redundant degrees of freedom and various configurations. There are two hard problems imposed by SRR features: locomotion planning and the discovery of multiple locomotion patterns. Most of the current research focuses on solving the first problem, using evolutionary algorithms based on the philosophy of searching-for-the-best. The main prob‐ lem is that the search can fall into a local optimum in the case of a complex non-linear problem. Another drawback is that the searched result lacks diversity in the behaviour space, which is inappropriate in addressing the problem of discovering multiple locomotion patterns. In this paper, we present a new strategy that evolves an SRR’s controller by searching for behavioural diversity. Instead of converging on a single optimal solution, this strategy discovers a vast variety of different ways to realize robot locomotion. Optimal motion is sparse in the behaviour space, and this method can find it as a by-product through a diversitykeeping mechanism. A revised particle swarm optimiza‐ tion (PSO) algorithm, driven by behaviour sparseness, is implemented to evolve locomotion for a variety of config‐ urations whose efficiency and flexibility is validated. The results show that this method can not only obtain an optimized robot controller, but also find various locomo‐ tion patterns.

In the domain of modular self-reconfigurable robotic systems, self-reconfiguration is known to be a highly challenging task. This article presents a novel algorithm for distributed self-reconfiguration by combining cellular automata and L-systems. Cellular automata is used to handle the relative motion planning of decentralized modules. L-systems are introduced to provide a topological description for the target configuration. The turtle interpretation is extended to modular robotics to generate local predictions for distributed modules from global description. Local predictions spread out in the system through gradient propagation. Modules, using cellular automata rules managing local motion, climb gradient to the expanding fronts for constructing global configurations. Both simulations and experiments have demonstrated the practical effectiveness of the proposed algorithm.