Dongyang Bie

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.

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.

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 Synthetic Algorithm for Tracking a Moving Object in a Multiple-Dynamic Obstacles Environment Based on Kinematically Planar Redundant Manipulators

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.

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.

Parametric L-systems-based modeling self-reconfiguration of modular robots in obstacle environments

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.

An Approach to the Bio-Inspired Control of Self-reconfigurable Robots

Self-reconfigurable robots are robots built by modules which can move in relationship to each other. This ability of changing its physical form provides the robots a high level of adaptability and robustness. Given an initial configuration and a goal configuration of the robot, the problem of self-regulation consists on finding a sequence of module moves that will reconfigure the robot from the initial configuration to the goal configuration. In this paper, we use a bio-inspired method for studying this problem 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. A promising 3D software simulation and a 2D hardware experiment are also presented.

Modeling the fractal development of modular robots

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.