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.National Natural Science Foundation of China No. 6167313

Self-repair ability of evolved self-assembling systems in cellular automata

Self-repairing systems are those that are able to reconfigure themselves following disruptions to bring them back into a defined normal state. In this paper we explore the self-repair ability of some cellular automata-like systems, which differ from classical cellular automata by the introduction of a local diffusion process inspired by chemical signalling processes in biological development. The update rules in these systems are evolved using genetic programming to self-assemble towards a target pattern. In particular, we demonstrate that once the update rules have been evolved for self-assembly, many of those update rules also provide a self-repair ability without any additional evolutionary process aimed specifically at self-repair

Continuum percolation theory of epimorphic regeneration

A biophysical model of epimorphic regeneration based on a continuum percolation process of fully penetrable disks in two dimensions is proposed. All cells within a randomly chosen disk of the regenerating organism are assumed to receive a signal in the form of a circular wave as a result of the action/reconfiguration of neoblasts and neoblast-derived mesenchymal cells in the blastema. These signals trigger the growth of the organism, whose cells read, on a faster time scale, the electric polarization state responsible for their differentiation and the resulting morphology. In the long time limit, the process leads to a morphological attractor that depends on experimentally accessible control parameters governing the blockage of cellular gap junctions and, therefore, the connectivity of the multicellular ensemble. When this connectivity is weakened, positional information is degraded leading to more symmetrical structures. This general theory is applied to the specifics of planaria regeneration. Computations and asymptotic analyses made with the model show that it correctly describes a significant subset of the most prominent experimental observations, notably anterior-posterior polarization (and its loss) or the formation of four-headed planaria.Comment: This author wish to retract the paper arXiv:1705.06720 because it began as part of a collaboration that later fell apart and it was published without the consent from the collaborators. Furthermore, the collaborators have managed to provide a better solution to this proble

Distributed Control of Microscopic Robots in Biomedical Applications

Current developments in molecular electronics, motors and chemical sensors could enable constructing large numbers of devices able to sense, compute and act in micron-scale environments. Such microscopic machines, of sizes comparable to bacteria, could simultaneously monitor entire populations of cells individually in vivo. This paper reviews plausible capabilities for microscopic robots and the physical constraints due to operation in fluids at low Reynolds number, diffusion-limited sensing and thermal noise from Brownian motion. Simple distributed controls are then presented in the context of prototypical biomedical tasks, which require control decisions on millisecond time scales. The resulting behaviors illustrate trade-offs among speed, accuracy and resource use. A specific example is monitoring for patterns of chemicals in a flowing fluid released at chemically distinctive sites. Information collected from a large number of such devices allows estimating properties of cell-sized chemical sources in a macroscopic volume. The microscopic devices moving with the fluid flow in small blood vessels can detect chemicals released by tissues in response to localized injury or infection. We find the devices can readily discriminate a single cell-sized chemical source from the background chemical concentration, providing high-resolution sensing in both time and space. By contrast, such a source would be difficult to distinguish from background when diluted throughout the blood volume as obtained with a blood sample

Emergent behaviors in a bio-inspired platform controlled by a physical cellular automata cluster

This work illustrates behavior patterns and trajectories of a bio-inspired artificial platform induced by a cellular automata (CA)-based control strategy. The platform embeds both CA control as physical electronic architecture and a distributed hardware layer as effectors. In this work, we test both the functionality of the novel hardware’s components as well as the device’s capabilities in locomotion tasks. We also observe the trajectories and patterns emerging from different initial states of the CA excitation and hardware configurations. Two main result sets emerge from this study: the first set illustrates different trajectories according to different initial excitation of the physical CA controller layer. The second set suggests the potential of the developed platform for generating complex patterns of control, as well as indicating emergent characteristics similar to those common to morphological computation approaches in generating localized perturbations without affecting or notifying the central controller