Constructing living buildings: a review of relevant technologies for a novel application of biohybrid robotics

Biohybrid robotics takes an engineering approach to the expansion and exploitation of biological behaviours for application to automated tasks. Here, we identify the construction of living buildings and infrastructure as a high-potential application domain for biohybrid robotics, and review technological advances relevant to its future development. Construction, civil infrastructure maintenance and building occupancy in the last decades have comprised a major portion of economic production, energy consumption and carbon emissions. Integrating biological organisms into automated construction tasks and permanent building components therefore has high potential for impact. Live materials can provide several advantages over standard synthetic construction materials, including self-repair of damage, increase rather than degradation of structural performance over time, resilience to corrosive environments, support of biodiversity, and mitigation of urban heat islands. Here, we review relevant technologies, which are currently disparate. They span robotics, self-organizing systems, artificial life, construction automation, structural engineering, architecture, bioengineering, biomaterials, and molecular and cellular biology. In these disciplines, developments relevant to biohybrid construction and living buildings are in the early stages, and typically are not exchanged between disciplines. We, therefore, consider this review useful to the future development of biohybrid engineering for this highly interdisciplinary application.publishe

Biomorpher: interactive evolution for parametric design

Combining graph-based parametric design with metaheuristic solvers has to date focussed solely on performance based criteria and solving clearly defined objectives. In this paper, we outline a new method for combining a parametric modelling environment with an interactive Cluster-Orientated Genetic Algorithm (COGA). In addition to performance criteria, evolutionary design exploration can be guided through choice alone, with user motivation that cannot be easily defined. As well as numeric parameters forming a genotype, the evolution of whole parametric definitions is discussed through the use of genetic programming. Visualisation techniques that enable mixing small populations for interactive evolution with large populations for performance-based optimisation are discussed, with examples from both academia and industry showing a wide range of applications

TARTARUS AND FRACTAL GENE REGULATORY NETWORKS WITH INPUTS

Tartarus is a benchmark problem used to evaluate artificial intelligence techniques for solving problems in the field of non-Markovian agent motion planning. In this paper a fractal gene regulatory network with inputs is evolved to act as a virtual robot controller in the Tartarus environment. The proposed technique is compared and contrasted with other previously reported techniques and it is shown that the gene regulatory network that includes input information provides an excellent performance without using any explicit memory or environmental modeling. Detailed experimental studies are presented to illustrate the effectiveness and superiority of the proposed approach.Evolutionary computation, gene regulatory network, fractals, Tartarus, control

Evolving Reactive Controller for a Modular Robot: Benefits of the Property of State-Switching in Fractal Gene Regulatory Networks

Abstract. In this paper, we study Fractal Gene Regulatory Networks (FGRNs) evolved as local controllers for a modular robot in snake topol-ogy that reacts adaptively to environment. The task is to have the robot moving in a specific direction until it reaches a randomly placed target-zone and stays there. We point to a characteristic of FGRN model, namely “state-switching property ” and demonstrate it as a beneficial property in evolving reactive controllers

Using interactive evolution to design behaviors for non-deterministic self-organized construction

Self-organizing construction is an emerging subdomain for on-site construction robots. This not only presents new challenges for robotics, but due to the stochasticity involved in such systems, impacts the modeling and prediction of resulting built structures. Self-organizing models have been explored by architects for generative design and for optimization, but so far have infrequently been studied in the context of construction. Here we present a strategy for architects to design with non-deterministic self-organizing behaviors, using interactive evolution to incorporate user judgment. We introduce our "Integrated Growth Projection'' method, having implemented it into a software pipeline for early phase design. We test the software with an initial user group of architects, to see whether the method and pipeline helps them design a non-deterministic self-organizing behavior. The user group creates several hybrid controllers that reliably solve their chosen design tasks

An Interruptible Task Allocation Model : Application to a Honey Bee Colony Simulation

International audienceDivision of labour is a key aspect of distributed systems, such as swarm robotics or multi-agent systems. Inspired by social insects known for their task allocation capabilities, most of the models rely on two assumptions: 1) each task is associated with a stimulus, and 2) the execution of this task lowers that stimulus. In short, the stimulus is a representation of the amount of work needed on a task. When these assumptions are not true, we need a mechanism to guide the agent in its decision whether to pursue or to interrupt its current task, as there is no diminishing stimulus to rely on. In this article, we propose a model based on the Response Threshold Model and a mechanism based on the agent’s intrinsic motivation and internal states, allowing to take into account tasks dissociated from stimuli. Agents use their intrinsic motivation to emulate the priority of tasks not associated with any stimuli, and to decide whether to interrupt or pursue their current task. This model has been applied to simulate the division of labour within a simplified honey bee colony, associated with the constantly adapting physiology of honey bees. Preliminary results show that the task allocation is effective, robust and in some cases improved by the interruption mechanism