Fabricate 2020

Fabricate 2020 is the fourth title in the FABRICATE series on the theme of digital fabrication and published in conjunction with a triennial conference (London, April 2020). The book features cutting-edge built projects and work-in-progress from both academia and practice. It brings together pioneers in design and making from across the fields of architecture, construction, engineering, manufacturing, materials technology and computation. Fabricate 2020 includes 32 illustrated articles punctuated by four conversations between world-leading experts from design to engineering, discussing themes such as drawing-to-production, behavioural composites, robotic assembly, and digital craft

On Neuromechanical Approaches for the Study of Biological Grasp and Manipulation

Biological and robotic grasp and manipulation are undeniably similar at the level of mechanical task performance. However, their underlying fundamental biological vs. engineering mechanisms are, by definition, dramatically different and can even be antithetical. Even our approach to each is diametrically opposite: inductive science for the study of biological systems vs. engineering synthesis for the design and construction of robotic systems. The past 20 years have seen several conceptual advances in both fields and the quest to unify them. Chief among them is the reluctant recognition that their underlying fundamental mechanisms may actually share limited common ground, while exhibiting many fundamental differences. This recognition is particularly liberating because it allows us to resolve and move beyond multiple paradoxes and contradictions that arose from the initial reasonable assumption of a large common ground. Here, we begin by introducing the perspective of neuromechanics, which emphasizes that real-world behavior emerges from the intimate interactions among the physical structure of the system, the mechanical requirements of a task, the feasible neural control actions to produce it, and the ability of the neuromuscular system to adapt through interactions with the environment. This allows us to articulate a succinct overview of a few salient conceptual paradoxes and contradictions regarding under-determined vs. over-determined mechanics, under- vs. over-actuated control, prescribed vs. emergent function, learning vs. implementation vs. adaptation, prescriptive vs. descriptive synergies, and optimal vs. habitual performance. We conclude by presenting open questions and suggesting directions for future research. We hope this frank assessment of the state-of-the-art will encourage and guide these communities to continue to interact and make progress in these important areas

Computational Tools and Facilities for the Next-Generation Analysis and Design Environment

This document contains presentations from the joint UVA/NASA Workshop on Computational Tools and Facilities for the Next-Generation Analysis and Design Environment held at the Virginia Consortium of Engineering and Science Universities in Hampton, Virginia on September 17-18, 1996. The presentations focused on the computational tools and facilities for analysis and design of engineering systems, including, real-time simulations, immersive systems, collaborative engineering environment, Web-based tools and interactive media for technical training. Workshop attendees represented NASA, commercial software developers, the aerospace industry, government labs, and academia. The workshop objectives were to assess the level of maturity of a number of computational tools and facilities and their potential for application to the next-generation integrated design environment

Turing Learning: Advances and Applications

Turing Learning is the family of algorithms where models and discriminators are generated in a competitive setting. This thesis concerns the coevolutionary framework of Turing Learning and investigates the advances for improved model accuracy and the applications in robotic systems. Advances proposed in this thesis are as follows: an interactive approach to enable the discriminator to genuinely influence the data sampling process; a hybrid formulation to combine the benefits of the interactive discriminator in improving model accuracy and the advantages of the passive discriminator for reducing training cost; an exclusiveness reward mechanism to promote candidates with the exclusive performance during the coevolutionary process. Applications presented in this thesis are as follows: an approach for a mobile robotic agent to automatically infer its sensor configuration; an approach for the robot agent to automatically calibrate its sensor reading; a novel approach to infer swarm behaviours from their effects on the environment. The interactive approach has been validated in the inference of sensor configuration and calibration model, leading to the self-modelling/self-discovery process of robotic agents. Results suggest an improved model accuracy with the interactive approach in both cases, compared with the passive approach. The hybrid formulation and the exclusiveness reward mechanism have been demonstrated in the inference of the calibration model. Results show that almost half of the training cost can be reduced without a decrease in model accuracy by applying the hybrid formulation. The novel reward mechanism can accelerate the convergence without a decrease in model accuracy. The indirect way of inferring swarm behaviours requires a small amount of training and reveals novel behavioural controllers for individual robots

Opinions and Outlooks on Morphological Computation

Morphological Computation is based on the observation that biological systems seem to carry out relevant computations with their morphology (physical body) in order to successfully interact with their environments. This can be observed in a whole range of systems and at many different scales. It has been studied in animals – e.g., while running, the functionality of coping with impact and slight unevenness in the ground is "delivered" by the shape of the legs and the damped elasticity of the muscle-tendon system – and plants, but it has also been observed at the cellular and even at the molecular level – as seen, for example, in spontaneous self-assembly. The concept of morphological computation has served as an inspirational resource to build bio-inspired robots, design novel approaches for support systems in health care, implement computation with natural systems, but also in art and architecture. As a consequence, the field is highly interdisciplinary, which is also nicely reflected in the wide range of authors that are featured in this e-book. We have contributions from robotics, mechanical engineering, health, architecture, biology, philosophy, and others

Cyber-Human Systems, Space Technologies, and Threats

CYBER-HUMAN SYSTEMS, SPACE TECHNOLOGIES, AND THREATS is our eighth textbook in a series covering the world of UASs / CUAS/ UUVs / SPACE. Other textbooks in our series are Space Systems Emerging Technologies and Operations; Drone Delivery of CBNRECy – DEW Weapons: Emerging Threats of Mini-Weapons of Mass Destruction and Disruption (WMDD); Disruptive Technologies with applications in Airline, Marine, Defense Industries; Unmanned Vehicle Systems & Operations On Air, Sea, Land; Counter Unmanned Aircraft Systems Technologies and Operations; Unmanned Aircraft Systems in the Cyber Domain: Protecting USA’s Advanced Air Assets, 2nd edition; and Unmanned Aircraft Systems (UAS) in the Cyber Domain Protecting USA’s Advanced Air Assets, 1st edition. Our previous seven titles have received considerable global recognition in the field. (Nichols & Carter, 2022) (Nichols, et al., 2021) (Nichols R. K., et al., 2020) (Nichols R. , et al., 2020) (Nichols R. , et al., 2019) (Nichols R. K., 2018) (Nichols R. K., et al., 2022)https://newprairiepress.org/ebooks/1052/thumbnail.jp

Additive Manufacturing Of Functional Constructs Under Process Uncertainty

In recent years, development of novel material-sets has enabled additive manufacturing (AM) to transform from being used purely for model-making applications, to production of functional constructs. What was once only a rapid prototyping technology is now being used to print functional components, including batteries, actuators, transistors, and for tissue engineering applications, living tissue (Chapter 1). Associated with these new printing inks, however, is a drastic increase in the complexity of AM materials, and consequently, in the process uncertainty related to deposition of these materials. New applications enabled by functional printing capabilities, in particular in situ AM, also have associated process uncertainties, including situational and environmental uncertainties. That is, uncertainty in the shape of the substrate and in environmental parameters, such as temperature and humidity. As additive manufacturing makes the transition from a prototyping technology to more of a functional-object fabrication platform, these new associated process uncertainties must be addressed to yield sufficient geometric fidelity. Existing control schemes largely relied upon open-loop control and did not handle uncertainty through control algorithms, but instead avoided them by limiting their material-sets, printing on trivially shaped substrates, and restricting environmental conditions. A few techniques used geometric feedback to handle materials uncertainty, but these techniques did so on a process-parameter-level, and did not monitor/manipulate on the whole-part level. As a result, these techniques could not detect high-level errors such as whole-part deformation. The technique proposed herein, Greedy Geometric Feedback (GGF), closes the loop on the whole-part level and therefore can detect/correct types of errors that were previously un-addressable. Simulations and physical experiments were employed to validate and study the GGF algorithm. Not only was GGF effective at handling materials uncertainties, but it also has potential for situational and environmental uncertainties. Additional work focused on situational uncertainty and alternative control schemes were developed that effectively handled this type of uncertainty with less computational and data collection overhead. A novel differencebased planning approach was employed to explore in situ AM repair of osteochondral defects, and repair/adaptation of a four-legged robot. These proof-of-concept prints are the only known examples of generalized in situ AM, to date, in which the AM system was not provided a priori hard-coded substrate geometric information. The contributions of the work presented herein fall into three categories: 1) development of functional printing materials, 2) development of novel methodologies for quantitatively optimizing the printing qualities of functional printing inks, and 3) development of novel generalized control schemes for handling AM process uncertainty

On neuromechanical approaches for the study of biological and robotic grasp and manipulation

abstract: Biological and robotic grasp and manipulation are undeniably similar at the level of mechanical task performance. However, their underlying fundamental biological vs. engineering mechanisms are, by definition, dramatically different and can even be antithetical. Even our approach to each is diametrically opposite: inductive science for the study of biological systems vs. engineering synthesis for the design and construction of robotic systems. The past 20 years have seen several conceptual advances in both fields and the quest to unify them. Chief among them is the reluctant recognition that their underlying fundamental mechanisms may actually share limited common ground, while exhibiting many fundamental differences. This recognition is particularly liberating because it allows us to resolve and move beyond multiple paradoxes and contradictions that arose from the initial reasonable assumption of a large common ground. Here, we begin by introducing the perspective of neuromechanics, which emphasizes that real-world behavior emerges from the intimate interactions among the physical structure of the system, the mechanical requirements of a task, the feasible neural control actions to produce it, and the ability of the neuromuscular system to adapt through interactions with the environment. This allows us to articulate a succinct overview of a few salient conceptual paradoxes and contradictions regarding under-determined vs. over-determined mechanics, under- vs. over-actuated control, prescribed vs. emergent function, learning vs. implementation vs. adaptation, prescriptive vs. descriptive synergies, and optimal vs. habitual performance. We conclude by presenting open questions and suggesting directions for future research. We hope this frank and open-minded assessment of the state-of-the-art will encourage and guide these communities to continue to interact and make progress in these important areas at the interface of neuromechanics, neuroscience, rehabilitation and robotics.The electronic version of this article is the complete one and can be found online at: https://jneuroengrehab.biomedcentral.com/articles/10.1186/s12984-017-0305-

White Paper 5: Brain, Mind & Behaviour

© CSICThe study of the brain will tell us what makes us humans and how our social behavior generates. Increasing our understanding of how the brain functions and interacts with the ecosystem to interpret the world will not only help to find effective means to treat and/or cure neurological and psychiatric disorders but will also change our vision on questions pertaining to philosophy and humanities and transform other fields such as economy and law. Neurosciences research at the CSIC is already valuable and should be intensified mainly focused on the eight major challenges described in this volume