Xiaolu Wang

A Simplified Approach to Realize Cellular Automata for UBot Modular Self-Reconfigurable Robots

Abstract—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.

Design and implementation of UBot: A modular Self-Reconfigurable Robot

The design and implementation of a novel modular Self-Reconfigurable Robot (SRR) called UBot is reviewed in this paper. Firstly, the philosophy of hardware design is presented. The module is designed with criteria such as cubic-shape, homogeneity, and strong connections to fulfill the requirements of complex three-dimensional reconfiguration and locomotion. Each robotic module has two degrees of freedom and four connecting surfaces with hook-type connecting mechanism. A group of modules can transform between different configurations by changing their local connections, achieve complicated modes of motion and accomplish a large variety of tasks. Secondly, a 3D dynamics simulator for UBot SRR is developed, where robot locomotion and transfiguration simulation could be done. A worm-like robot evolution is performed with results of a variety of high-performance locomotion patterns. Finally, Experiments are performed about autonomous docking, multi-mode locomotion and self-reconfiguration. The validity of docking method, CPG-network control and reconfiguration planning method is verified through locomotion and transformation tests of configurations such as snake-type, quadruped walking-type, omni-directional cross-type and loop-type.

Chaotic CPG Based Locomotion Control for Modular Self-reconfigurable Robot

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.

Automatic Locomotion Generation for a UBot Modular Robot - Towards Both High-speed and Multiple Patterns

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 problem 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 SRRs 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 diversity-keeping mechanism. A revised particle swarm optimization (PSO) algorithm, driven by behaviour sparseness, is implemented to evolve locomotion for a variety of configurations whose efficiency and flexibility is validated. The results show that this method can not only obtain an optimized robot controller, but also find various locomotion patterns.

Analysis and Implementation of Multiple Bionic Motion Patterns for Caterpillar Robot Driven by Sinusoidal Oscillator

Articulated caterpillar robot has various locomotion patterns—which make it adaptable to different tasks. Generally, the researchers have realized undulatory (transverse wave) and simple rolling locomotion. But many motion patterns are still unexplored. In this paper, peristaltic locomotion and various additional rolling patterns are achieved by employing sinusoidal oscillator with fixed phase difference as the joint controller. The usefulness of the proposed method is verified using simulation and experiment. The design parameters for different locomotion patterns have been calculated that they can be replicated in similar robots immediately.

Multisensor-based autonomous docking for UBot modular reconfigurable robot

Autonomous docking is still a major challenge for self-reconfigurable robots, it is an essential capability for the system to realize reconstruction, self-repairing and even for completing operational tasks in the complex environment. In this paper, we developed a new sensor module for the UBot self-reconfigurable robot system, and proposed a novel docking method for precise docking between module group with the sensor module and the target module. We describe this automated docking progress as three steps: The visual pre-positioning process; Precise positioning through hall sensors; Crawling and self-locking by the hook-type connecting systems. Finally, this method is proved to be effective by the S-4 module group automatic docking experiment.

A distributed and parallel control mechanism for self-reconfiguration of modular robots using L-systems and cellular automata

For distributed self-reconfiguration of Modular Self-Reconfigurable (MSR) robots, one of the main difficulties is the contradiction between limited information of decentralized modules and well-organized global structure. This paper presents a distributed and parallel mechanism for decentralized self-reconfiguration of MSR robots. This mechanism is hybrid by combining Lindenmayer systems (L-systems) describing the topological structure as configuration target and Cellular Automata (CA) for local motion planning of individual modules. Turtle interpretations are extended to modular robotics for generating module-level predictions from global description. According to local information, independent modules make motion planning by Cellular Automata in parallel. This distributed mechanism is robust to failure of modules, scalable to varying module numbers, and convergent to predefined reconfiguration targets. Simulations and statistical results are provided for validating the proposed algorithm. We propose a distributed and parallel mechanism for self-reconfiguration of modular robots.L-systems are introduced to the distributed self-reconfiguration for a parallel system.The Cellular Automata are simplified with only two rules.This approach is convergent to target structure, robust to failure of modules and scalable to module numbers.

A homogenous CPG-network for multimode locomotion control of modular self-reconfigurable robot

A self-reconfigurable (SR) robot is a cellular robot that is capable of adapting its shape and functions to changing environments and demands, and the “cellular” in which can rearrange their mutual mechanical connection to change the robots outward features. This paper first introduces a new self-reconfigurable robotic system UBot. For the variability characteristics of the self-reconfigurable system, combined with the biological central pattern generator (CPG), a locomotion control method has been proposed which can control multimode locomotion for different configurations through the self-excited oscillation via different coupled modes of CPG network. The oscillators in the CPG network are homogenous with the same models and parameters, only the connection relations change for different configurations. The simulation and experiment of quadruped walking and snake-type wiggling controlled by CPG network have been successfully completed on the platform of UBot modular self-reconfigurable system.

Research on Locomotive Evolution Based on Worm-Shaped Configuration of Self-reconfigurable Robot HitMSR II

In this paper, we constructed a three-dimensional dynamic simulator for HitMSR II which is a module self-reconfigurable robot system composed of single-rotational-freedom modules. Downhill Simplex Optimization Algorithm were used to evolve the locomotion of the robot ensuring that it is possible to get access to the relatively more optimized locomotion gaits parameters after large enough numbers of iterative calculations under a certain evaluation function. What’s more. We selected worm-shaped configuration to research on the validation of locomotive evolution both in simulation and experiment.

L-systems driven self-reconfiguration of modular robots

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.