Robot control with biological cells

At present there exists a large gap in size, performance, adaptability and robustness between natural and artificial information processors for performing coherent perception-action tasks under real-time constraints. Even the simplest organisms have an enviable capability of coping with an unknown dynamic environment. Robots, in contrast, are still clumsy if confronted with such complexity. This paper presents a bio-hybrid architecture developed for exploring an alternate approach to the control of autonomous robots. Circuits prepared from amoeboid plasmodia of the slime mold Physarum polycephalum are interfaced with an omnidirectional hexapod robot. Sensory signals from the macro-physical environment of the robot are transduced to cellular scale and processed using the unique micro-physical features of intracellular information processing. Conversely, the response form the cellular computation is amplified to yield a macroscopic output action in the environment mediated through the robot’s actuators

Benchmarking Cerebellar Control

Cerebellar models have long been advocated as viable models for robot dynamics control. Building on an increasing insight in and knowledge of the biological cerebellum, many models have been greatly refined, of which some computational models have emerged with useful properties with respect to robot dynamics control. Looking at the application side, however, there is a totally different picture. Not only is there not one robot on the market which uses anything remotely connected with cerebellar control, but even in research labs most testbeds for cerebellar models are restricted to toy problems. Such applications hardly ever exceed the complexity of a 2 DoF simulated robot arm; a task which is hardly representative for the field of robotics, or relates to realistic applications. In order to bring the amalgamation of the two fields forwards, we advocate the use of a set of robotics benchmarks, on which existing and new computational cerebellar models can be comparatively tested. It is clear that the traditional approach to solve robotics dynamics loses ground with the advancing complexity of robotic structures; there is a desire for adaptive methods which can compete as traditional control methods do for traditional robots. In this paper we try to lay down the successes and problems in the fields of cerebellar modelling as well as robot dynamics control. By analyzing the common ground, a set of benchmarks is suggested which may serve as typical robot applications for cerebellar models

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

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

Using Surface-Motions for Locomotion of Microscopic Robots in Viscous Fluids

Microscopic robots could perform tasks with high spatial precision, such as acting in biological tissues on the scale of individual cells, provided they can reach precise locations. This paper evaluates the feasibility of in vivo locomotion for micron-size robots. Two appealing methods rely only on surface motions: steady tangential motion and small amplitude oscillations. These methods contrast with common microorganism propulsion based on flagella or cilia, which are more likely to damage nearby cells if used by robots made of stiff materials. The power potentially available to robots in tissue supports speeds ranging from one to hundreds of microns per second, over the range of viscosities found in biological tissue. We discuss design trade-offs among propulsion method, speed, power, shear forces and robot shape, and relate those choices to robot task requirements. This study shows that realizing such locomotion requires substantial improvements in fabrication capabilities and material properties over current technology.Comment: 14 figures and two Quicktime animations of the locomotion methods described in the paper, each showing one period of the motion over a time of 0.5 milliseconds; version 2 has minor clarifications and corrected typo

Controlling a mobile robot with a biological brain

The intelligent controlling mechanism of a typical mobile robot is usually a computer system. Some recent research is ongoing in which biological neurons are being cultured and trained to act as the brain of an interactive real world robot�thereby either completely replacing, or operating in a cooperative fashion with, a computer system. Studying such hybrid systems can provide distinct insights into the operation of biological neural structures, and therefore, such research has immediate medical implications as well as enormous potential in robotics. The main aim of the research is to assess the computational and learning capacity of dissociated cultured neuronal networks. A hybrid system incorporating closed-loop control of a mobile robot by a dissociated culture of neurons has been created. The system is flexible and allows for closed-loop operation, either with hardware robot or its software simulation. The paper provides an overview of the problem area, gives an idea of the breadth of present ongoing research, establises a new system architecture and, as an example, reports on the results of conducted experiments with real-life robots

Bio-inspired Tensegrity Soft Modular Robots

In this paper, we introduce a design principle to develop novel soft modular robots based on tensegrity structures and inspired by the cytoskeleton of living cells. We describe a novel strategy to realize tensegrity structures using planar manufacturing techniques, such as 3D printing. We use this strategy to develop icosahedron tensegrity structures with programmable variable stiffness that can deform in a three-dimensional space. We also describe a tendon-driven contraction mechanism to actively control the deformation of the tensegrity mod-ules. Finally, we validate the approach in a modular locomotory worm as a proof of concept.Comment: 12 pages, 7 figures, submitted to Living Machine conference 201

Information-Theoretic Aspects of Control in a Bio-Hybrid Robot Device

Information processing in natural systems radically differs from current information technology. This difference is particularly apparent in the area of robotics, where both organisms and artificial devices face a similar challenge: the need to act in real time in a complex environment and to do so with computing resources severely limited by their size and power consumption. The formidable gap between artificial and natural systems in terms of information processing capability motivates research into the biological modes of information processing. Such undertakings, however, are hampered by the fact that nature directly exploits the manifold physical characteristics of its computing substrates, while available theoretical tools in general ignore the underlying implementation. Here we sketch the concept of bounded computability in an attempt towards reconciling the information-theoretic perspective with the need to take the material basis of information processing into account. We do so in the context of Physarum polycephalum as a naturally evolved information processor and the use of this organism as an integral component of a robot controller

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