Capability by Stacking: The Current Design Heuristic for Soft Robots

Soft robots are a new class of systems being developed and studied by robotics scientists. These systems have a diverse range of applications including sub-sea manipulation and rehabilitative robotics. In their current state of development, the prevalent paradigm for the control architecture in these systems is a one-to-one mapping of controller outputs to actuators. In this work, we define functional blocks as the physical implementation of some discrete behaviors, which are presented as a decomposition of the behavior of the soft robot. We also use the term ‘stacking’ as the ability to combine functional blocks to create a system that is more complex and has greater capability than the sum of its parts. By stacking functional blocks a system designer can increase the range of behaviors and the overall capability of the system. As the community continues to increase the capabilities of soft systems—by stacking more and more functional blocks—we will encounter a practical limit with the number of parallelized control lines. In this paper, we review 20 soft systems reported in the literature and we observe this trend of one-to-one mapping of control outputs to functional blocks. We also observe that stacking functional blocks results in systems that are increasingly capable of a diverse range of complex motions and behaviors, leading ultimately to systems that are capable of performing useful tasks. The design heuristic that we observe is one of increased capability by stacking simple units—a classic engineering approach. As we move towards more capability in soft robotic systems, and begin to reach practical limits in control, we predict that we will require increased amounts of autonomy in the system. The field of soft robotics is in its infancy, and as we move towards realizing the potential of this technology, we will need to develop design tools and control paradigms that allow us to handle the complexity in these stacked, non-linear systems

Context Aware Computing for The Internet of Things: A Survey

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201

Context Aware Middleware Architectures: Survey and Challenges

Abstract: Context aware applications, which can adapt their behaviors to changing environments, are attracting more and more attention. To simplify the complexity of developing applications, context aware middleware, which introduces context awareness into the traditional middleware, is highlighted to provide a homogeneous interface involving generic context management solutions. This paper provides a survey of state-of-the-art context aware middleware architectures proposed during the period from 2009 through 2015. First, a preliminary background, such as the principles of context, context awareness, context modelling, and context reasoning, is provided for a comprehensive understanding of context aware middleware. On this basis, an overview of eleven carefully selected middleware architectures is presented and their main features explained. Then, thorough comparisons and analysis of the presented middleware architectures are performed based on technical parameters including architectural style, context abstraction, context reasoning, scalability, fault tolerance, interoperability, service discovery, storage, security & privacy, context awareness level, and cloud-based big data analytics. The analysis shows that there is actually no context aware middleware architecture that complies with all requirements. Finally, challenges are pointed out as open issues for future work

Neural Control and Biomechanics of the Octopus Arm Muscular Hydrostat

openOctopus vulgaris is a cephalopod mollusk with outstanding motor capabilities, built upon the action of eight soft and exceptionally flexible appendages. In the absence of any rigid skeletal-like support, the octopus arm works as a “muscular hydrostat” and movement is generated from the antagonistic action of two main muscle groups (longitudinal, L, and transverse, T, muscles) under an isovolumetric constrain. This peculiar anatomical organization evolved along with novel morphological arrangements, biomechanical properties, and motor control strategies aimed at reducing the computational burden of controlling unconstrained appendages endowed with virtually infinite degrees of freedom of motion. Hence, the octopus offers the unique opportunity to study a motor system, different from those of skeletal animals, and capable of controlling complex and precise motor tasks of eight arms with theoretically infinite degrees of freedom. Here, we investigated the octopus arm motor system employing a bottom-up approach. We began by identifying the motor neuron population and characterizing their organization in the arm nervous system. We next performed an extensive biomechanical characterization of the arm muscles focusing on the morphofunctional properties that are likely to facilitate the dynamic deformations occurring during arm movement. We show that motor neurons cluster in specific regions of the arm ganglia following a topographical organization. In addition, T muscles exhibit biomechanical properties resembling those of vertebrate slow muscles whereas L muscles are closer to those of vertebrate fast muscles. This difference is enhanced by the hydrostatic pressure inherently present in the arm, which causes the two muscles to operate under different conditions. Interestingly, these features underlie the different use of arm muscles during specific tasks Thus, the octopus evolved several arm-embedded adaptations to reduce the motor control complexity and increase the energetic efficiency of arm motion. This study find relevance also in the blooming field of soft-robotics. Indeed, an increasing number of researchers are currently aiming to design and construct bio-inspired soft-robotic manipulators, more flexible and versatile than their “hard” counterparts and more suited to perform gentle tasks and to interact with biological tissues. In this context, the octopus emerged as a pivotal source of inspiration for motor control principles underlying motion in soft-bodied limbs.openXXXIV CICLO - NEUROSCIENZE - Neuroscienze e neurotecnologieDI CLEMENTE, Alessi