Controlling underwater robots with electronic nervous systems

We are developing robot controllers based on biomimetic design principles. The goal is to realise the adaptive capabilities of the animal models in natural environments. We report feasibility studies of a hybrid architecture that instantiates a command and coordinating level with computed discrete-time map-based (DTM) neuronal networks and the central pattern generators with analogue VLSI (Very Large Scale Integration) electronic neuron (aVLSI) networks. DTM networks are realised using neurons based on a 1-D or 2-D Map with two additional parameters that define silent, spiking and bursting regimes. Electronic neurons (ENs) based on Hindmarsh-Rose (HR) dynamics can be instantiated in analogue VLSI and exhibit similar behaviour to those based on discrete components. We have constructed locomotor central pattern generators (CPGs) with aVLSI networks that can be modulated to select different behaviours on the basis of selective command input. The two technologies can be fused by interfacing the signals from the DTM circuits directly to the aVLSI CPGs. Using DTMs, we have been able to simulate complex sensory fusion for rheotaxic behaviour based on both hydrodynamic and optical flow senses. We will illustrate aspects of controllers for ambulatory biomimetic robots. These studies indicate that it is feasible to fabricate an electronic nervous system controller integrating both aVLSI CPGs and layered DTM exteroceptive reflexes

Real-time biomimetic Central Pattern Generators in an FPGA for hybrid experiments

This investigation of the leech heartbeat neural network system led to the development of a low resources, real-time, biomimetic digital hardware for use in hybrid experiments. The leech heartbeat neural network is one of the simplest central pattern generators (CPG). In biology, CPG provide the rhythmic bursts of spikes that form the basis for all muscle contraction orders (heartbeat) and locomotion (walking, running, etc.). The leech neural network system was previously investigated and this CPG formalized in the Hodgkin–Huxley neural model (HH), the most complex devised to date. However, the resources required for a neural model are proportional to its complexity. In response to this issue, this article describes a biomimetic implementation of a network of 240 CPGs in an FPGA (Field Programmable Gate Array), using a simple model (Izhikevich) and proposes a new synapse model: activity-dependent depression synapse. The network implementation architecture operates on a single computation core. This digital system works in real-time, requires few resources, and has the same bursting activity behavior as the complex model. The implementation of this CPG was initially validated by comparing it with a simulation of the complex model. Its activity was then matched with pharmacological data from the rat spinal cord activity. This digital system opens the way for future hybrid experiments and represents an important step toward hybridization of biological tissue and artificial neural networks. This CPG network is also likely to be useful for mimicking the locomotion activity of various animals and developing hybrid experiments for neuroprosthesis development

Neuromorphic electronics for real-time biomedical image processing

Published versio

Biomimetic Based Applications

The interaction between cells, tissues and biomaterial surfaces are the highlights of the book "Biomimetic Based Applications". In this regard the effect of nanostructures and nanotopographies and their effect on the development of a new generation of biomaterials including advanced multifunctional scaffolds for tissue engineering are discussed. The 2 volumes contain articles that cover a wide spectrum of subject matter such as different aspects of the development of scaffolds and coatings with enhanced performance and bioactivity, including investigations of material surface-cell interactions

Bio-Inspired Robotics

Modern robotic technologies have enabled robots to operate in a variety of unstructured and dynamically-changing environments, in addition to traditional structured environments. Robots have, thus, become an important element in our everyday lives. One key approach to develop such intelligent and autonomous robots is to draw inspiration from biological systems. Biological structure, mechanisms, and underlying principles have the potential to provide new ideas to support the improvement of conventional robotic designs and control. Such biological principles usually originate from animal or even plant models, for robots, which can sense, think, walk, swim, crawl, jump or even fly. Thus, it is believed that these bio-inspired methods are becoming increasingly important in the face of complex applications. Bio-inspired robotics is leading to the study of innovative structures and computing with sensory–motor coordination and learning to achieve intelligence, flexibility, stability, and adaptation for emergent robotic applications, such as manipulation, learning, and control. This Special Issue invites original papers of innovative ideas and concepts, new discoveries and improvements, and novel applications and business models relevant to the selected topics of ``Bio-Inspired Robotics''. Bio-Inspired Robotics is a broad topic and an ongoing expanding field. This Special Issue collates 30 papers that address some of the important challenges and opportunities in this broad and expanding field

Neuroinspired control strategies with applications to flapping flight

This dissertation is centered on a theoretical, simulation, and experimental study of control strategies which are inspired by biological systems. Biological systems, along with sufficiently complicated engineered systems, often have many interacting degrees of freedom and need to excite large-displacement oscillations in order to locomote. Combining these factors can make high-level control design difficult. This thesis revolves around three different levels of abstraction, providing tools for analysis and design. First, we consider central pattern generators (CPGs) to control flapping-flight dynamics. The key idea here is dimensional reduction - we want to convert complicated interactions of many degrees of freedom into a handful of parameters which have intuitive connections to the overall system behavior, leaving the control designer unconcerned with the details of particular motions. A rigorous mathematical and control theoretic framework to design complex three-dimensional wing motions is presented based on phase synchronization of nonlinear oscillators. In particular, we show that flapping-flying dynamics without a tail or traditional aerodynamic control surfaces can be effectively controlled by a reduced set of central pattern generator parameters that generate phase-synchronized or symmetry-breaking oscillatory motions of two main wings. Furthermore, by using a Hopf bifurcation, we show that tailless aircraft (inspired by bats) alternating between flapping and gliding can be effectively stabilized by smooth wing motions driven by the central pattern generator network. Results of numerical simulation with a full six-degree-of-freedom flight dynamic model validate the effectiveness of the proposed neurobiologically inspired control approach. Further, we present experimental micro aerial vehicle (MAV) research with low-frequency flapping and articulated wing gliding. The importance of phase difference control via an abstract mathematical model of central pattern generators is confirmed with a robotic bat on a 3-DOF pendulum platform. An aerodynamic model for the robotic bat based on the complex wing kinematics is presented. Closed loop experiments show that control dimension reduction is achievable - unstable longitudinal modes are stabilized and controlled using only two control parameters. A transition of flight modes, from flapping to gliding and vice-versa, is demonstrated within the CPG control scheme. The second major thrust is inspired by this idea that mode switching is useful. Many bats and birds adopt a mixed strategy of flapping and gliding to provide agility when necessary and to increase overall efficiency. This work explores dwell time constraints on switched systems with multiple, possibly disparate invariant limit sets. We show that, under suitable conditions, trajectories globally converge to a superset of the limit sets and then remain in a second, larger superset. We show the effectiveness of the dwell-time conditions by using examples of nonlinear switching limit cycles from our work on flapping flight. This level of abstraction has been found to be useful in many ways, but it also produces its own challenges. For example, we discuss death of oscillation which can occur for many limit-cycle controllers and the difficulty in incorporating fast, high-displacement reflex feedback. This leads us to our third major thrust - considering biologically realistic neuron circuits instead of a limit cycle abstraction. Biological neuron circuits are incredibly diverse in practice, giving us a convincing rationale that they can aid us in our quest for flexibility. Nevertheless, that flexibility provides its own challenges. It is not currently known how most biological neuron circuits work, and little work exists that connects the principles of a neuron circuit to the principles of control theory. We begin the process of trying to bridge this gap by considering the simplest of classical controllers, PD control. We propose a simple two-neuron, two-synapse circuit based on the concept that synapses provide attenuation and a delay. We present a simulation-based method of analysis, including a smoothing algorithm, a steady-state response curve, and a system identification procedure for capturing differentiation. There will never be One True Control Method that will solve all problems. Nature's solution to a diversity of systems and situations is equally diverse. This will inspire many strategies and require a multitude of analysis tools. This thesis is my contribution of a few

Neuromorphic auditory computing: towards a digital, event-based implementation of the hearing sense for robotics

In this work, it is intended to advance on the development of the neuromorphic audio processing systems in robots through the implementation of an open-source neuromorphic cochlea, event-based models of primary auditory nuclei, and their potential use for real-time robotics applications. First, the main gaps when working with neuromorphic cochleae were identified. Among them, the accessibility and usability of such sensors can be considered as a critical aspect. Silicon cochleae could not be as flexible as desired for some applications. However, FPGA-based sensors can be considered as an alternative for fast prototyping and proof-of-concept applications. Therefore, a software tool was implemented for generating open-source, user-configurable Neuromorphic Auditory Sensor models that can be deployed in any FPGA, removing the aforementioned barriers for the neuromorphic research community. Next, the biological principles of the animals' auditory system were studied with the aim of continuing the development of the Neuromorphic Auditory Sensor. More specifically, the principles of binaural hearing were deeply studied for implementing event-based models to perform real-time sound source localization tasks. Two different approaches were followed to extract inter-aural time differences from event-based auditory signals. On the one hand, a digital, event-based design of the Jeffress model was implemented. On the other hand, a novel digital implementation of the Time Difference Encoder model was designed and implemented on FPGA. Finally, three different robotic platforms were used for evaluating the performance of the proposed real-time neuromorphic audio processing architectures. An audio-guided central pattern generator was used to control a hexapod robot in real-time using spiking neural networks on SpiNNaker. Then, a sensory integration application was implemented combining sound source localization and obstacle avoidance for autonomous robots navigation. Lastly, the Neuromorphic Auditory Sensor was integrated within the iCub robotic platform, being the first time that an event-based cochlea is used in a humanoid robot. Then, the conclusions obtained are presented and new features and improvements are proposed for future works.En este trabajo se pretende avanzar en el desarrollo de los sistemas de procesamiento de audio neuromórficos en robots a través de la implementación de una cóclea neuromórfica de código abierto, modelos basados en eventos de los núcleos auditivos primarios, y su potencial uso para aplicaciones de robótica en tiempo real. En primer lugar, se identificaron los principales problemas a la hora de trabajar con cócleas neuromórficas. Entre ellos, la accesibilidad y usabilidad de dichos sensores puede considerarse un aspecto crítico. Los circuitos integrados analógicos que implementan modelos cocleares pueden no pueden ser tan flexibles como se desea para algunas aplicaciones específicas. Sin embargo, los sensores basados en FPGA pueden considerarse una alternativa para el desarrollo rápido y flexible de prototipos y aplicaciones de prueba de concepto. Por lo tanto, en este trabajo se implementó una herramienta de software para generar modelos de sensores auditivos neuromórficos de código abierto y configurables por el usuario, que pueden desplegarse en cualquier FPGA, eliminando las barreras mencionadas para la comunidad de investigación neuromórfica. A continuación, se estudiaron los principios biológicos del sistema auditivo de los animales con el objetivo de continuar con el desarrollo del Sensor Auditivo Neuromórfico (NAS). Más concretamente, se estudiaron en profundidad los principios de la audición binaural con el fin de implementar modelos basados en eventos para realizar tareas de localización de fuentes sonoras en tiempo real. Se siguieron dos enfoques diferentes para extraer las diferencias temporales interaurales de las señales auditivas basadas en eventos. Por un lado, se implementó un diseño digital basado en eventos del modelo Jeffress. Por otro lado, se diseñó una novedosa implementación digital del modelo de codificador de diferencias temporales y se implementó en FPGA. Por último, se utilizaron tres plataformas robóticas diferentes para evaluar el rendimiento de las arquitecturas de procesamiento de audio neuromórfico en tiempo real propuestas. Se utilizó un generador central de patrones guiado por audio para controlar un robot hexápodo en tiempo real utilizando redes neuronales pulsantes en SpiNNaker. A continuación, se implementó una aplicación de integración sensorial que combina la localización de fuentes de sonido y la evitación de obstáculos para la navegación de robots autónomos. Por último, se integró el Sensor Auditivo Neuromórfico dentro de la plataforma robótica iCub, siendo la primera vez que se utiliza una cóclea basada en eventos en un robot humanoide. Por último, en este trabajo se presentan las conclusiones obtenidas y se proponen nuevas funcionalidades y mejoras para futuros trabajos