The body as a reservoir: locomotion and sensing with linear feedback

It is known that mass-spring nets have computational power and can be trained to reproduce oscillating patterns. In this work, we extend this idea to locomotion and sensing. We simulate systems made out of bars and springs and show that stable gaits can be maintained by these structures with only linear feedback. We then conduct a classification experiment in which the system has to distinguish terrains while maintaining an oscillatory pattern. These experiments indicate that the control of compliant robots can be simplified if one exploits the computational power of the body’s dynamics

Controlling Tensegrity Robots Through Evolution

Tensegrity structures (built from interconnected rods and cables) have the potential to offer a revolutionary new robotic design that is light-weight, energy-efficient, robust to failures, capable of unique modes of locomotion, impact tolerant, and compliant (reducing damage between the robot and its environment). Unfortunately robots built from tensegrity structures are difficult to control with traditional methods due to their oscillatory nature, nonlinear coupling between components and overall complexity. Fortunately this formidable control challenge can be overcome through the use of evolutionary algorithms. In this paper we show that evolutionary algorithms can be used to efficiently control a ball-shaped tensegrity robot. Experimental results performed with a variety of evolutionary algorithms in a detailed soft-body physics simulator show that a centralized evolutionary algorithm performs 400 percent better than a hand-coded solution, while the multi-agent evolution performs 800 percent better. In addition, evolution is able to discover diverse control solutions (both crawling and rolling) that are robust against structural failures and can be adapted to a wide range of energy and actuation constraints. These successful controls will form the basis for building high-performance tensegrity robots in the near future

Design of tensegrity structures using parametric analysis and stochastic search.

The final publication is available at Springer via http://dx.doi.org/10.1007/s00366-009-0154-1Tensegrity structures are lightweight structures composed of cables in tension and struts in compression. Since tensegrity systems exhibit geometrically nonlinear behavior, finding optimal structural designs is difficult. This paper focuses on the use of stochastic search for the design of tensegrity systems. A pedestrian bridge made of square hollow-rope tensegrity ring modules is studied. Two design methods are compared in this paper. Both methods aim to find the minimal cost solution. The first method approximates current practice in design offices. More specifically, parametric analysis that is similar to a gradient-based optimization is used to identify good designs. Parametric studies are executed for each system parameter in order to identify its influence on response. The second method uses a stochastic search strategy called probabilistic global search Lausanne. Both methods provide feasible configurations that meet civil engineering criteria of safety and serviceability. Parametric studies also help in defining search parameters such as appropriate penalty costs to enforce constraints while optimizing using stochastic search. Traditional design methods are useful to gain an understanding of structural behavior. However, due to the many local minima in the solution space, stochastic search strategies find better solutions than parametric studies.Swiss National Science Foundatio

Form Finding and structural Optimization of Tensile cable dome using Parametric Modelling Tools

In this paper, a new framework of form finding and structural optimizations for tensile domes was developed using a cutting-edge parametric modelling tool Grasshopper in Rhino. The detailed exploration of this new techniques is presented. It is found that the use of this parametric tool allows a more intuitive, rapid and flexible design. Structural optimisation of the member sizes, topology and surface can be explored easily at an initial design stage in a project. Therefore, the proposed new framework provides a more effective and efficient way for form finding and structural optimization. Based on the new method, a prototype Tensile dome which is to replicate the existing Tensile Dome Georgia dome is designed and analyzed. The structural behavior of the cable domes is investigated. Using this new framework, two ellipse shape Tensile domes with new geometrical configuration are developed. They exhibit enhanced load bearing capacity, therefore can be used the future long span structure projects

Behavior finding: Morphogenetic Designs Shaped by Function

Evolution has shaped an incredible diversity of multicellular living organisms, whose complex forms are self-made through a robust developmental process. This fundamental combination of biological evolution and development has served as an inspiration for novel engineering design methodologies, with the goal to overcome the scalability problems suffered by classical top-down approaches. Top-down methodologies are based on the manual decomposition of the design into modular, independent subunits. In contrast, recent computational morphogenetic techniques have shown that they were able to automatically generate truly complex innovative designs. Algorithms based on evolutionary computation and artificial development have been proposed to automatically design both the structures, within certain constraints, and the controllers that optimize their function. However, the driving force of biological evolution does not resemble an enumeration of design requirements, but much rather relies on the interaction of organisms within the environment. Similarly, controllers do not evolve nor develop separately, but are woven into the organism’s morphology. In this chapter, we discuss evolutionary morphogenetic algorithms inspired by these important aspects of biological evolution. The proposed methodologies could contribute to the automation of processes that design “organic” structures, whose morphologies and controllers are intended to solve a functional problem. The performance of the algorithms is tested on a class of optimization problems that we call behavior-finding. These challenges are not explicitly based on morphology or controller constraints, but only on the solving abilities and efficacy of the design. Our results show that morphogenetic algorithms are well suited to behavior-finding

Design of tensegrity structures using parametric analysis and stochastic search

Tensegrity structures are lightweight structures composed of cables in tension and struts in compression. Since tensegrity systems exhibit geometrically nonlinear behavior, finding optimal structural designs is difficult. This paper focuses on the use of stochastic search for the design of tensegrity systems. A pedestrian bridge made of square hollow-rope tensegrity ring modules is studied. Two design methods are compared in this paper. Both methods aim to find the minimal cost solution. The first method approximates current practice in design offices. More specifically, parametric analysis that is similar to a gradient-based optimization is used to identify good designs. Parametric studies are executed for each system parameter in order to identify its influence on response. The second method uses a stochastic search strategy called probabilistic global search Lausanne. Both methods provide feasible configurations that meet civil engineering criteria of safety and serviceability. Parametric studies also help in defining search parameters such as appropriate penalty costs to enforce constraints while optimizing using stochastic search. Traditional design methods are useful to gain an understanding of structural behavior. However, due to the many local minima in the solution space, stochastic search strategies find better solutions than parametric studies

Designing tensegrity modules for pedestrian bridges

Tensegrity systems are spatial structures composed of tensile and compression components in a self-equilibrated state of prestress. The tensegrity concept has already been studied by researchers in various fields over the past decades. A family of tensegrity modules that can offer promising solutions for civil engineering applications such as tensegrity domes, towers and bridges is analyzed. Research into tensegrity systems has resulted in reliable techniques for form finding and structural analysis. However, the tensegrity concept is not yet part of mainstream structural design. This paper presents a design study of a tensegrity-based pedestrian bridge. The structural performance of the bridge using three tensegrity modules is evaluated through parametric studies. Design requirements for pedestrian bridges and results of parametric studies are used to define a design procedure that optimizes section sizes for this type of structure. A structural efficiency indicator is proposed and used to compare proposals for feasible bridge configurations. Design results illustrate that the hollow-rope tensegrity bridge can efficiently meet typical design criteria

Design optimization and dynamic analysis of a tensegrity-based footbridge

Tensegrity structures are spatial structural systems composed of struts and cables with pin-jointed connections. Their stability is provided by the self-stress state in tensioned and compressed members. Although much progress has been made in advancing research into the tensegrity concept, a rapid survey of current activities in engineering practice shows that much of its potential has yet to be accomplished. A design optimization study for a tensegrity-based footbridge is presented in order to further advance the tensegrity concept in modern structural engineering. In the absence of specific design guidelines, design requirements for a tensegrity footbridge are stated. A genetic algorithm based optimization scheme is used to find a cost-effective design solution. The dynamic performance of the tensegrity footbridge is studied through parametric studies. Design results illustrate that the proposed tensegrity-based footbridge meets typical static and dynamic design criteria

INVERSE IDENTIFICATION OF TRANSIENT THERMAL PROPERTIES AND HEAT SOURCES USING GENETIC ALGORITHMS

This work investigates the solution to inverse problems in heat transfer using genetic algorithms. Genetic algorithms are robust, stochastic search techniques which also admit the ability to search highly nonlinear problems. In this work, computational techniques are developed for the simultaneous inverse identification the internal heat generation and the thermal diffusivity of early age concrete as functions of time, as well as constant convective coefficients. Through the use of several numerical examples it is shown that this methodology yields accurate results for the inverse heat transfer problem in finding several unknown conditions simultaneously

Topological Self-Organisation: Using a particle-spring system simulation to generate structural space-filling lattices

The problem being addressed relates to the filling of a certain volume with a structural space frame network lattice consisting of a given number of nodes. A method is proposed that comprises a generative algorithm including a physical dynamic simulation of particle-spring system. The algorithm is able to arrange nodes in space and establish connections among them through local rules of self-organisation, thus producing space frame topologies. In order to determine the appropriateness of the method, an experiment is conducted that involves testing the algorithm in the case of filling the volume of a cube with multiple numbers of nodes. The geometrical, topological and structural aspects of the generated lattices are analysed and discussed. The results indicate that the method is capable of generating efficient space frame topologies that fill spatial envelopes