An Inexpensive Flying Robot Design for Embodied Robotics Research

Flying insects are capable of a wide-range of flight and cognitive behaviors which are not currently understood. The replication of these capabilities is of interest to miniaturized robotics, because they share similar size, weight, and energy constraints. Currently, embodiment of insect behavior is primarily done on ground robots which utilize simplistic sensors and have different constraints to flying insects. This limits how much progress can be made on understanding how biological systems fundamentally work. To address this gap, we have developed an inexpensive robotic solution in the form of a quadcopter aptly named BeeBot. Our work shows that BeeBot can support the necessary payload to replicate the sensing capabilities which are vital to bees' flight navigation, including chemical sensing and a wide visual field-of-view. BeeBot is controlled wirelessly in order to process this sensor data off-board; for example, in neural networks. Our results demonstrate the suitability of the proposed approach for further study of the development of navigation algorithms and of embodiment of insect cognition

A lightweight, inexpensive robotic system for insect vision

Designing hardware for miniaturized robotics which mimics the capabilities of flying insects is of interest, because they share similar constraints (i.e. small size, low weight, and low energy consumption). Research in this area aims to enable robots with similarly efficient flight and cognitive abilities. Visual processing is important to flying insects' impressive flight capabilities, but currently, embodiment of insect-like visual systems is limited by the hardware systems available. Suitable hardware is either prohibitively expensive, difficult to reproduce, cannot accurately simulate insect vision characteristics, and/or is too heavy for small robotic platforms. These limitations hamper the development of platforms for embodiment which in turn hampers the progress on understanding of how biological systems fundamentally works. To address this gap, this paper proposes an inexpensive, lightweight robotic system for modelling insect vision. The system is mounted and tested on a robotic platform for mobile applications, and then the camera and insect vision models are evaluated. We analyse the potential of the system for use in embodiment of higher-level visual processes (i.e. motion detection) and also for development of navigation based on vision for robotics in general. Optic flow from sample camera data is calculated and compared to a perfect, simulated bee world showing an excellent resemblance

GeNN: a code generation framework for accelerated brain simulations

Large-scale numerical simulations of detailed brain circuit models are important for identifying hypotheses on brain functions and testing their consistency and plausibility. An ongoing challenge for simulating realistic models is, however, computational speed. In this paper, we present the GeNN (GPU-enhanced Neuronal Networks) framework, which aims to facilitate the use of graphics accelerators for computational models of large-scale neuronal networks to address this challenge. GeNN is an open source library that generates code to accelerate the execution of network simulations on NVIDIA GPUs, through a flexible and extensible interface, which does not require in-depth technical knowledge from the users. We present performance benchmarks showing that 200-fold speedup compared to a single core of a CPU can be achieved for a network of one million conductance based Hodgkin-Huxley neurons but that for other models the speedup can differ. GeNN is available for Linux, Mac OS X and Windows platforms. The source code, user manual, tutorials, Wiki, in-depth example projects and all other related information can be found on the project website http://genn-team.github.io/genn/

Exploring the potential of brain-inspired computing

The gap between brains and computers regarding both their cognitive capability and power efficiency is remarkably huge. Brains process information massively in parallel and its constituents are intrinsically self-organizing, while in digital computers the execution of instructions is deterministic and rather serial. The recent progress in the development of dedicated hardware systems implementing physical models of neurons and synapses enables to efficiently emulate spiking neural networks. In this work, we verify the design and explore the potential for brain-inspired computing of such an analog neuromorphic system, called Spikey. We demonstrate the versatility of this highly configurable substrate by the implementation of a rich repertoire of network models, including models for signal propagation and enhancement, general purpose classifiers, cortical models and decorrelating feedback systems. Network emulations on Spikey are highly accelerated and consume less than 1 nJ per synaptic transmission. The Spikey system, hence, outperforms modern desktop computers in terms of fast and efficient network simulations closing the gap to brains. During this thesis the stability, performance and user-friendliness of the Spikey system was improved integrating it into the neuroscientific tool chain and making it available for the community. The implementation of networks suitable to solve everyday tasks, like object or speech recognition, qualifies this technology to be an alternative to conventional computers. Considering the compactness, computational capability and power efficiency, neuromorphic systems may qualify as a valuable complement to classical computation