Emergent adaptive behaviour of GRN-controlled simulated robots in a changing environment

We developed a bio-inspired robot controller combining an artificial genome with an agent-based control system. The genome encodes a gene regulatory network (GRN) that is switched on by environmental cues and, following the rules of transcriptional regulation, provides output signals to actuators. Whereas the genome represents the full encoding of the transcriptional network, the agent-based system mimics the active regulatory network and signal transduction system also present in naturally occurring biological systems. Using such a design that separates the static from the conditionally active part of the gene regulatory network contributes to a better general adaptive behaviour. Here, we have explored the potential of our platform with respect to the evolution of adaptive behaviour, such as preying when food becomes scarce, in a complex and changing environment and show through simulations of swarm robots in an A-life environment that evolution of collective behaviour likely can be attributed to bio-inspired evolutionary processes acting at different levels, from the gene and the genome to the individual robot and robot population

Applications of Biological Cell Models in Robotics

In this paper I present some of the most representative biological models applied to robotics. In particular, this work represents a survey of some models inspired, or making use of concepts, by gene regulatory networks (GRNs): these networks describe the complex interactions that affect gene expression and, consequently, cell behaviour

Is there an integrative center in the vertebrate brain-stem? A robotic evaluation of a model of the reticular formation viewed as an action selection device

Neurobehavioral data from intact, decerebrate, and neonatal rats, suggests that the reticular formation provides a brainstem substrate for action selection in the vertebrate central nervous system. In this article, Kilmer, McCulloch and Blum’s (1969, 1997) landmark reticular formation model is described and re-evaluated, both in simulation and, for the first time, as a mobile robot controller. Particular model configurations are found to provide effective action selection mechanisms in a robot survival task using either simulated or physical robots. The model’s competence is dependent on the organization of afferents from model sensory systems, and a genetic algorithm search identified a class of afferent configurations which have long survival times. The results support our proposal that the reticular formation evolved to provide effective arbitration between innate behaviors and, with the forebrain basal ganglia, may constitute the integrative, ’centrencephalic’ core of vertebrate brain architecture. Additionally, the results demonstrate that the Kilmer et al. model provides an alternative form of robot controller to those usually considered in the adaptive behavior literature

Hardware Architecture Review of Swarm Robotics System: Self-Reconfigurability, Self-Reassembly, and Self-Replication

Swarm robotics is one of the most fascinating and new research areas of recent decades, and one of the grand challenges of robotics is the design of swarm robots that are self-sufficient. This can be crucial for robots exposed to environments that are unstructured or not easily accessible for a human operator, such as the inside of a blood vessel, a collapsed building, the deep sea, or the surface of another planet. In this paper, we present a comprehensive study on hardware architecture and several other important aspects of modular swarm robots, such as self-reconfigurability, self-replication, and self-assembly. The key factors in designing and building a group of swarm robots are cost and miniaturization with robustness, flexibility, and scalability. In robotics intelligence, self-assembly and self-reconfigurability are among the most important characteristics as they can add additional capabilities and functionality to swarm robots. Simulation and model design for swarm robotics is highly complex and expensive, especially when attempting to model the behavior of large swarm robot groups.http://dx.doi.org/10.5402/2013/84960

A stochastic self-replicating robot capable of hierarchical assembly

This paper presents the development of a self-replicating mobile robot that functions by undergoing stochastic motions. The robot functions hierarchically. There are three stages in this hierarchy: (1) An initial pool of feed modules/parts together with one functional basic robot; (2) a collection of basic robots that are spontaneously formed out of these parts as a result of a chain reaction induced by stochastic motion of the initial seed robot at stage 1; (3) complex formations of joined basic robots from stage 2. In the first part of this paper we demonstrate basic stochastic self-replication in unstructured environments. A single functional robot moves around at random in a sea of stock modules and catalyzes the conversion of these modules into replicas. In the second part of the paper, the robots are upgraded with a layer that enables mechanical connections between robots. The replicas can then connect to each other and aggregate. Finally, self-reconfigurability is presented for two robotic aggregation

The Propulsion of Reconfigurable Modular Robots in Fluidic Environments

Reconfigurable modular robots promise to transform the way robotic systems are designed and operated. Fluidic or microgravity environments, which can be difficult or dangerous for humans to work in, are ideal domains for the use of modular systems. This thesis proposes that combining effective propulsion, large reconfiguration space and high scalability will increase the utility of modular robots. A novel concept for the propulsion of reconfigurable modular robots is developed. Termed Modular Fluidic Propulsion (MFP), this concept describes a system that propels by routing fluid though itself. This allows MFP robots to self-propel quickly and effectively in any configuration, while featuring a cubic lattice structure. A decentralized occlusion-based motion controller for the system is developed. The simplicity of the controller, which requires neither run-time memory nor computation via logic units, combined with the simple binary sensors and actuators of the robot, gives the system a high level of scalabilty. It is proven formally that 2-D MFP robots are able to complete a directed locomotion task under certain assumptions. Simulations in 3-D show that robots composed of 125 modules in a variety of configurations can complete the task. A hardware prototype that floats on the surface of water is developed. Experiments show that robots composed of four modules can complete the task in any configuration. This thesis also investigates the evo-bots, a self-reconfigurable modular system that floats in 2-D on an air table. The evo-bot system uses a stop-start propulsion mechanism to choose between moving randomly or not moving at all. This is demonstrated experimentally for the first time. In addition, the ability of the modules to detect, harvest and share energy, as well as self-assemble into simple structures, is demonstrated

Optimizing Associative Information Transfer within Content-addressable Memory

Original article can be found at: http://www.oldcitypublishing.com/IJUC/IJUC.htmlPeer reviewe

Self-deformable modular robot inspired by cellular structure

Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2008.Includes bibliographical references (leaves 15-16).In this paper, we present a modular robot design inspired by the creation of complex structures and functions in biology via deformation. Our design is based on the Tensegrity model of cellular structure, where active filaments within the cell contract and expand to control individual cell shape, and sheets of such cells undergo large-scale shape change through the cooperative action of connected cells. Such deformations play a role in many processes: early embryo shape change, heart and intestine function, and in lamprey locomotion. Modular robotic systems that replicate the basic deformable multicellular structure have the potential to quickly generate large-scale shape change and create time-varying shapes to achieve different global functions. We present a design and initial hardware implementation of this model. Our design includes four different modular components: (1) actuating links, (2) passive (compressive) links, (3) elastic surface membranes, and (4) universal connecting interfaces. In both hardware implementation and simulation, we show several self-deformable structures that can be generated from these four components, including the deformable surface, expandable cube, terrain-adaptive bridge from [1] and some examples inspired by biology. We argue that self-deformation is more appropriate for dynamic and sensing-adaptive shape change in a certain class of tasks.by Kristina M. Haller.S.B