ARGoS: a modular, parallel, multi-engine simulator for multi-robot systems

We present a novel multi-robot simulator named ARGoS. ARGoS is designed to simulate complex experiments involving large swarms of robots of different types. ARGoS is the first multi-robot simulator that is at the same time both efficient (fast performance with many robots) and flexible (highly customizable for specific experiments). Novel design choices in ARGoS have enabled this breakthrough. First, in ARGoS, it is possible to partition the simulated space into multiple sub-spaces, managed by different physics engines running in parallel. Second, ARGoS?? architecture is multi-threaded, thus designed to optimize the usage of modern multi-core CPUs. Finally, the architecture of ARGoS is highly modular, enabling easy addition of custom features and appropriate allocation of computational resources. We assess the efficiency of ARGoS and showcase its flexibility with targeted experiments. Experimental results demonstrate that simulation run-time increases linearly with the number of robots. A 2D-dynamics simulation of 10,000 e-puck robots can be performed in 60?% of the time taken by the corresponding real-world experiment. We show how ARGoS can be extended to suit the needs of an experiment in which custom functionality is necessary to achieve sufficient simulation accuracy. ARGoS is open source software licensed under GPL3 and is downloadable free of charge.     

Concept of Intelligent Mechanical Design for Autonomous Mobile Robots

The concept of Intelligent Mechanical Design (IMD) is presented to show how a mechanical structure can be designed to affect robot controllability, simplification and task performance. Exploring this concept produces landmarks in the territory of mechanical robot design in the form of seven design principles. The design principles, which we call the Mecha-Telligence Principles (MTP), provide guidance on how to design mechanics for autonomous mobile robots. These principles guide us to ask the right questions when investigating issues concerning self-controllable, reliable, feasible, and compatible mechanics for autonomous mobile robots. To show how MTP can be applied in the design process we propose a novel methodology, named as Mecha-Telligence Methodology (MTM). Mechanical design by the proposed methodology is based on preference classification of the robot specification described by interaction of the robot with its environment and the physical parameters of the robot mechatronics. After defining new terms, we investigate the feasibility of the proposed methodology to the mechanical design of an autonomous mobile sewer inspection robot. In this industrial project we show how a passive-active intelligent moving mechanism can be designed using the MTM and employed in the field.     

Bio-inspired design strategies for central pattern generator control in modular robotics

New findings in the nervous system of invertebrates have shown how a number of features of central pattern generator (CPG) circuits contribute to the generation of robust flexible rhythms. In this paper we consider recently revealed strategies that living CPGs follow to design CPG control paradigms for modular robots. To illustrate them, we divide the task of designing an example CPG for a modular robot into independent problems. We formulate each problem in a general way and provide a bio-inspired solution for each of them: locomotion information coding, individual module control and inter-module coordination. We analyse the stability of the CPG numerically, and then test it on a real robot. We analyse steady state locomotion and recovery after perturbations. In both cases, the robot is able to autonomously find a stable effective locomotion state. Finally, we discuss how these strategies can result in a more general design approach for CPG-based locomotion.     

Network design meets in silico evolutionary biology

Cell fate is programmed through gene regulatory networks that perform several calculations to take the appropriate decision. In silico evolutionary optimization mimics the way Nature has designed such gene regulatory networks. In this review we discuss the basic principles of these evolutionary approaches and how they can be applied to engineer synthetic networks. We summarize the basic guidelines to implement an in silico evolutionary design method, the operators for mutation and selection that iteratively drive the network architecture towards a specified dynamical behavior. Interestingly, as it happens in natural evolution, we show the existence of patterns of punctuated evolution. In addition, we highlight several examples of models that have been designed using automated procedures, together with different objective functions to select for the proper behavior. Finally, we briefly discuss the modular designability of gene regulatory networks and its potential application in biotechnology.     

Bending continuous structures with SMAs: a novel robotic fish design

In this paper, we describe our research on bio-inspired locomotion systems using deformable structures and smart materials, concretely shape memory alloys (SMAs). These types of materials allow us to explore the possibility of building motor-less and gear-less robots. A swimming underwater fish-like robot has been developed whose movements are generated using SMAs. These actuators are suitable for bending the continuous backbone of the fish, which in turn causes a change in the curvature of the body. This type of structural arrangement is inspired by fish red muscles, which are mainly recruited during steady swimming for the bending of a flexible but nearly incompressible structure such as the fishbone. This paper reviews the design process of these bio-inspired structures, from the motivations and physiological inspiration to the mechatronics design, control and simulations, leading to actual experimental trials and results. The focus of this work is to present the mechanisms by which standard swimming patterns can be reproduced with the proposed design. Moreover, the performance of the SMA-based actuators' control in terms of actuation speed and position accuracy is also addressed.     

Implementation of Complex Biological Logic Circuits Using Spatially Distributed Multicellular Consortia

Engineered synthetic biological devices have been designed to perform a variety of functions from sensing molecules and bioremediation to energy production and biomedicine. Notwithstanding, a major limitation of in vivo circuit implementation is the constraint associated to the use of standard methodologies for circuit design. Thus, future success of these devices depends on obtaining circuits with scalable complexity and reusable parts. Here we show how to build complex computational devices using multicellular consortia and space as key computational elements. This spatial modular design grants scalability since its general architecture is independent of the circuit’s complexity, minimizes wiring requirements and allows component reusability with minimal genetic engineering. The potential use of this approach is demonstrated by implementation of complex logical functions with up to six inputs, thus demonstrating the scalability and flexibility of this method. The potential implications of our results are outlined.     

Robot control and the evolution of modular neurodynamics

Summary A modular approach to neural behavior control of autonomous robots is presented. It is based on the assumption that complex internal dynamics of recurrent neural networks can efficiently solve complex behavior tasks. For the development of appropriate neural control structures an evolutionary algorithm is introduced, which is able to generate neuromodules with specific functional properties, as well as the connectivity structure for a modular synthesis of such modules. This so called ENS ³-algorithm does not use genetic coding. It is primarily designed to develop size and connectivity structure of neuro-controllers. But at the same time it optimizes also parameters of individual networks like synaptic weights and bias terms. For demonstration, evolved networks for the control of miniature Khepera robots are presented. The aim is to develop robust controllers in the sense that neuro-controllers evolved in a simulator show comparably good behavior when loaded to a real robot acting in a physical environment. Discussed examples of such controllers generate obstacle avoidance and phototropic behaviors in non-trivial environments.     

A versatile plasmid architecture for mammalian synthetic biology (VAMSyB)

Current molecular cloning strategies generally lack inter-compatibility, are not strictly modular, or are not applicable to engineer multi-gene expression vectors for transient and stable integration. A standardized molecular cloning platform would advance research, for example, by promoting exchange of vectors between groups. Here, we present a versatile plasmid architecture for mammalian synthetic biology, which we designate VAMSyB, consisting of a three-tier vector family. Tier-1 is designed for easy engineering of fusion constructs, as well as easy swapping of genes and modules to tune the functionality of the vector. Tier-2 is designed for transient multi-gene expression, and is constructed by directly transferring the engineered expression cassettes from tier-1 vectors. Tier-3 enables stable integration into a mammalian host cell through viral transduction, transposons, or homology-directed recombination via CRISPR. This VAMSyB architecture is expected to have broad applicability in the field of mammalian synthetic biology. The VAMSyB collection of plasmids will be available through Addgene.     

Ligand binding by repeat proteins: natural and designed

Repeat proteins contain tandem arrays of small structural motifs. As a consequence of this architecture, they adopt non-globular, extended structures that present large, highly specific surfaces for ligand binding. Here we discuss recent advances toward understanding the functional role of this unique modular architecture. We showcase specific examples of natural repeat proteins interacting with diverse ligands and also present examples of designed repeat protein-ligand interactions.     

Engineering of beta-propeller protein scaffolds by multiple gene duplication and fusion of an idealized WD repeat

The ability to design specific amino acid sequences that fold into desired structures is central to engineering novel proteins. Protein design is also a good method to assess our understanding of sequence-structure and structure-function relationships. While beta-sheet structures are important elements of protein architecture, it has traditionally been more difficult to design beta-proteins than alpha-helical proteins. Taking advantage of the tandem repeated sequences that form the structural building blocks in a group of beta-propeller proteins; we have used a consensus design approach to engineer modular and relatively large scaffolds. An idealized WD repeat was designed from a structure-based sequence alignment with a set of structural guidelines. Using a plasmid sequential ligation strategy, artificial concatemeric genes with up to 10 copies of this idealized repeat were then constructed. Corresponding proteins with 4 through to 10 WD repeats were soluble when over-expressed in Escherichia coli. Notably, they were sufficiently stable in vivo surviving attack from endogenous proteases, and maintained a homogeneous, non-aggregated form in vitro. The results show that the beta-propeller scaffold is an attractive platform for future engineering work, particularly in experiments in which directed evolution techniques might improve the stability of the molecules and/or tailor them for a specific function.     

Cellular fate of a modular DNA delivery system mediated by silica nanoparticles

Development of efficient molecular medicines, including gene therapeutics, RNA therapeutics, and DNA vaccines, depends on efficient means of transfer of DNA or RNA into the cell. Potential problems, including toxicity and immunogenicity, surrounding viral methods of DNA delivery have necessitated the use of nonviral, synthetic carriers. To better design synthetic carriers, or transfection reagents, the modular design of viruses has inspired a modular approach to DNA and RNA delivery. Each modular component can be designed to circumvent each of the many barriers. The modular approach will allow modification of individual components for a specific application. By utilizing a dense silica nanoparticle to form a ternary complex, transfection efficiency of a DNA-transfection reagent complex was increased by a factor of approximately 10 by concentrating the DNA at the surface of cells. Surface modification of the silica nanoparticles allowed determination of the cellular uptake mechanism with only minor alteration of transfection efficiency. Nanoparticles are internalized by an endosome-lysosomal route followed by perinuclear accumulation. The modification mechanism confirms that surface modification of the modular system can allow specific moieties to be incorporated into the modular system without significant alteration of the transfection efficiency. By showing that the modular system based upon concentration of DNA at the level of the cell can be used to increase transfection efficiency, we have shown that further modification of the system may better target DNA delivery and overcome other barriers of DNA expression.     

Tailoring RNA modular units on a common scaffold: A modular ribozyme with a catalytic unit for β-nicotinamide mononucleotide-activated RNA ligation

A novel ribozyme that accelerates the ligation of β-nicotinamide mononucleotide (β-NMN)-activated RNA fragments was isolated and characterized. This artificial ligase ribozyme (YFL ribozyme) was isolated by a “design and selection” strategy, in which a modular catalytic unit was generated on a rationally designed modular scaffold RNA. Biochemical analyses of the YFL ribozyme revealed that it catalyzes RNA ligation in a template-dependent manner, and its activity is highly dependent on its architecture, which consists of a modular scaffold and a catalytic unit. As the design and selection strategy was used for generation of DSL ribozyme, isolation of the YFL ribozyme indicated the versatility of this strategy for generation of functional RNAs with modular architectures. The catalytic unit of the YFL ribozyme accepts not only β-NMN but also inorganic pyrophosphate and adenosine monophosphate as leaving groups for RNA ligation. This versatility of the YFL ribozyme provides novel insight into the possible roles of β-NMN (or NADH) in the RNA world.     

A novel strategy to design binding molecules harnessing the modular nature of repeat proteins

Repeat proteins, such as ankyrin or leucine-rich repeat proteins, are ubiquitous binding molecules, which occur, unlike antibodies, intra- and extracellularly. Their unique modular architecture features repeating structural units (repeats), which stack together to form elongated repeat domains displaying variable and modular target-binding surfaces. Based on this modularity, we developed a novel strategy to generate combinatorial libraries of polypeptides with highly diversified binding specificities. This strategy includes the consensus design of self-compatible repeats displaying variable surface residues and their random assembly into repeat domains. We envision that such repeat protein libraries will be highly valuable sources for novel binding molecules especially suitable for intracellular applications.     

Control of a robot dinosaur

The Palaiomation Consortium, supported by the European Commission, is building a robot Iguanodon atherfieldensis for museum display that is much more sophisticated than existing animatronic exhibits. The current half-size (2.5 m) prototype is fully autonomous, carrying its own computer and batteries. It walks around the room, choosing its own path and avoiding obstacles. A bigger version with a larger repertoire of behaviours is planned. Many design problems have had to be overcome. A real dinosaur would have had hundreds of muscles, and we have had to devise means of achieving life-like movement with a much smaller number of motors; we have limited ourselves to 20, to keep the control problems manageable. Realistic stance requires a narrower trackway and a higher centre of mass than in previous (often spider-like) legged robots, making it more difficult to maintain stability. Other important differences from previous walking robots are that the forelegs have to be shorter than the hind, and the machinery has had to be designed to fit inside a realistically shaped body shell. Battery life is about one hour, but to achieve this we have had to design the robot to have very low power consumption. Currently, this limits it to unrealistically slow movement. The control system includes a high-level instructions processor, a gait generator, a motion-coordination generator, and a kinematic model.     

Computational design and analysis of modular cells for large libraries of exchangeable product synthesis modules

Microbial metabolism can be harnessed to produce a large library of useful chemicals from renewable resources such as plant biomass. However, it is laborious and expensive to create microbial biocatalysts to produce each new product. To tackle this challenge, we have recently developed modular cell (ModCell) design principles that enable rapid generation of production strains by assembling a modular (chassis) cell with exchangeable production modules to achieve overproduction of target molecules. Previous computational ModCell design methods are limited to analyze small libraries of around 20 products. In this study, we developed a new computational method, named ModCell-HPC, that can design modular cells for large libraries with hundreds of products with a highly-parallel and multi-objective evolutionary algorithm and enable us to elucidate modular design properties. We demonstrated ModCell-HPC to design Escherichia coli modular cells towards a library of 161 endogenous production modules. From these simulations, we identified E. coli modular cells with few genetic manipulations that can produce dozens of molecules in a growth-coupled manner with different types of fermentable sugars. These designs revealed key genetic manipulations at the chassis and module levels to accomplish versatile modular cells, involving not only in the removal of major by-products but also modification of branch points in the central metabolism. We further found that the effect of various sugar degradation on redox metabolism results in lower compatibility between a modular cell and production modules for growth on pentoses than hexoses. To better characterize the degree of compatibility, we developed a method to calculate the minimal set cover, identifying that only three modular cells are all needed to couple with all compatible production modules. By determining the unknown compatibility contribution metric, we further elucidated the design features that allow an existing modular cell to be re-purposed towards production of new molecules. Overall, ModCell-HPC is a useful tool for understanding modularity of biological systems and guiding more efficient and generalizable design of modular cells that help reduce research and development cost in biocatalysis.     

Hebbian learning in parallel and modular memories

Many cognitive and sensorimotor functions in the brain involve parallel and modular memory subsystems that are adapted by activity-dependent Hebbian synaptic plasticity. This is in contrast to the multilayer perceptron model of supervised learning where sensory information is presumed to be integrated by a common pool of hidden units through backpropagation learning. Here we show that Hebbian learning in parallel and modular memories is more advantageous than backpropagation learning in lumped memories in two respects: it is computationally much more efficient and structurally much simpler to implement with biological neurons. Accordingly, we propose a more biologically relevant neural network model, called a tree-like perceptron, which is a simple modification of the multilayer perceptron model to account for the general neural architecture, neuronal specificity, and synaptic learning rule in the brain. The model features a parallel and modular architecture in which adaptation of the input-to-hidden connection follows either a Hebbian or anti-Hebbian rule depending on whether the hidden units are excitatory or inhibitory, respectively. The proposed parallel and modular architecture and implicit interplay between the types of synaptic plasticity and neuronal specificity are exhibited by some neocortical and cerebellar systems. Received: 13 October 1996 / Accepted in revised form: 16 October 1997     

Production activity control: A practical approach to scheduling

There is a widely perceived gap within the domain of scheduling for manufacturing systems, namely, many of the methods employed by production supervisors are quite different from those developed by researchers. In a sense, this inconsistency highlights the important fact that much scheduling research has failed to win approval where it matters most, namely, within the manufacturing system. In this article, we argue for a practical approach to scheduling for manufacturing systems, one that we believe can narrow, and possibly bridge, the gap between theory and practice. This approach is based upon a well-defined and modular architecture for scheduling, termedproduction activity control. This architecture is the foundation of our proposed solution to scheduling, since it provides a coherent blueprint for the synthesis of information technology and scheduling strategies. The result of this synthesis is a design tool for production activity control, which allows for detailed and disciplined experimentation with a range of scheduling strategies in a controlled and simulated environment. Due to the unique modular property of the design tool, these strategies may then be implemented live in a flexible manufacturing facility, hence narrowing the gap between scheduling theory and manufacturing practice. Our overall approach is tested through an appropriate implementation in a modern electronics assembly plant.     

Modular Upgradability in Consumer Electronics: Economic and Environmental Implications

Modularly upgradable product designs have been advocated to offer environmental and economic advantages; however, they are not commonly used in the consumer electronics industry. In this article, we investigate the economic and environmental benefits and challenges of modular upgradability for consumer electronics. From an economic point of view, we posit that the limited adoption of modular upgradability in consumer electronics is owing to various demand‐, technology‐, and competition‐related issues. From an environmental point of view, we posit that modularly upgradable product designs may not necessarily lead to superior environmental outcomes. To reach meaningful conclusions regarding the environmental benefits of modular upgradability, one needs to understand how product architecture affects demand, production, and consumption patterns, which arise from endogenous consumer and manufacturer choices. It is also important to take into account that modular upgradability may have potentially differentiated effects in the production, consumption, and postuse phases of the lifecycle.     

Global architecture of metabolite distributions across species and its formation mechanisms

Living organisms produce metabolites of many types via metabolic reactions. Especially, flavonoids, a type of secondary metabolite, of plant species are interesting examples. Since plant species are believed to have specific flavonoids that were formed response to diverse environments, elucidation of the design principles of metabolite distributions across plant species is important for understanding metabolite diversity and plant evolution. In our previous study, we found heterogeneous connectivity in metabolite distribution, and proposed a simple model to explain the possible origin of heterogeneous connectivity. In this paper, we determine additional structural properties of metabolite distribution among families that are analogous analogy with plant–animal mutualistic networks: nested structure and modular structure. An earlier model representative of these structural properties in bipartite relationships was established on the basis of the traits of elements and external factors. However, we found that the architecture of metabolite distribution could be explained by simple evolutionary processes without trait-based mechanisms by comparing our model and the earlier model. Our model provided a better qualitative and quantitative prediction of nested and modular structures in addition to heterogeneous connectivity. This finding implies an alternative possible origin of these structural properties, and suggests the mechanisms underlying establishment metabolite distributions across plant species are simpler than expected.