Distributed intelligent robotics : research & development in fault-tolerant control and size/position identification : a thesis presented in partial fulfilment of the requirements for the degree of Master of Engineering in Computer Systems Engineering at Massey University

This thesis presents research conducted on aspects of intelligent robotic systems. In the past two decades, robotics has become one of the most rapidly expanding and developing fields of science. Robotics can be considered as the science of using artificial intelligence in the physical world. Many areas of study exist in robotics. Among these, two fields that are of paramount importance in real world applications are fault tolerance, and sensory systems. Fault tolerance is necessary since a robot in the real world could encounter internal faults, and may also have to continue functioning under adverse conditions. Sensory mechanisms are essential since a robot will possess little intelligence if it does not have methods of acquiring information about its environment. Both these fields are researched in this thesis. In particular, emphasis is placed on distributed intelligent autonomous systems. Experiments and simulations have been conducted to investigate design for fault tolerance. A suitable platform was also chosen for an implementation of a visual system, as an example of a working sensory mechanism

Illusory self motion and simulator sickness

Presented here is a discussion of simulator sickness (with applications to motion sickness and space sickness) based on the notion of senses as perceptual systems, and the sensory conflict theory. Most forms of the sensory conflict theory unnecessarily propose the existence of a neural store. The neural store is thought to consist of a record of previous perceptual experiences against which currently experienced patterns of stimulation are compared. The authors seek to establish that in its most parsimonious form the sensory conflict theory does not require a construct such as the neural store. In its simpler form, the sensory conflict theory complements and extends Gibson's view of the senses as perceptual systems

How do we approach intrinsic motivation computationally? : a commentary on: What is intrinsic motivation? A typology of computational approaches. by Pierre-Yves Oudeyer and Frederic Kaplan

What is the energy function guiding behavior and learningµ Representationbased approaches like maximum entropy, generative models, sparse coding, or slowness principles can account for unsupervised learning of biologically observed structure in sensory systems from raw sensory data. However, they do not relate to behavior. Behavior-based approaches like reinforcement learning explain animal behavior in well-described situations. However, they rely on high-level representations which they cannot extract from raw sensory data. Combinations of multiple goal functions seems the methodology of choice to understand the complexity of the brain. But what is the set of possible goals. ..

Random sensory networks: a delay in analysis

A fundamental function performed by a sensory network is the retrieval of data gathered collectively by sensor nodes. The metrics that measure the efficiency of this data collection process are time and energy. In this paper, we study via simple discrete mathematical models, the statistics of the data collection time in sensory networks. Specifically, we analyze the average minimum delay in collecting randomly located/distributed sensors data for networks of various topologies when the number of nodes becomes large. Furthermore, we analyze the impact of various parameters such as size of packet, transmission range, and channel erasure probability on the optimal time performance. Our analysis applies to directional antenna systems as well as omnidirectional ones. This paper focuses on directional antenna systems and briefly presents results on omnidirectional antenna systems. Finally, a simple comparative analysis shows the respective advantages of the two systems

Thermodynamic limits to information harvesting by sensory systems

In view of the relation between information and thermodynamics we investigate how much information about an external protocol can be stored in the memory of a stochastic measurement device given an energy budget. We consider a layered device with a memory component storing information about the external environment by monitoring the history of a sensory part coupled to the environment. We derive an integral fluctuation theorem for the entropy production and a measure of the information accumulated in the memory device. Its most immediate consequence is that the amount of information is bounded by the average thermodynamic entropy produced by the process. At equilibrium no entropy is produced and therefore the memory device does not add any information about the environment to the sensory component. Consequently, if the system operates at equilibrium the addition of a memory component is superfluous. Such device can be used to model the sensing process of a cell measuring the external concentration of a chemical compound and encoding the measurement in the amount of phosphorylated cytoplasmic proteins.Comment: Revised version: 18 pages, 5 figure

Live calcium imaging of Aedes aegypti neuronal tissues reveals differential importance of chemosensory systems for life-history-specific foraging strategies.

BackgroundThe mosquito Aedes aegypti has a wide variety of sensory pathways that have supported its success as a species as well as a highly competent vector of numerous debilitating infectious pathogens. Investigations into mosquito sensory systems and their effects on behavior are valuable resources for the advancement of mosquito control strategies. Numerous studies have elucidated key aspects of mosquito sensory systems, however there remains critical gaps within the field. In particular, compared to that of the adult form, there has been a lack of studies directed towards the immature life stages. Additionally, although numerous studies have pinpointed specific sensory receptors as well as responding motor outputs, there has been a lack of studies able to monitor both concurrently.ResultsTo begin filling aforementioned gaps, here we engineered Ae. aegypti to ubiquitously express a genetically encoded calcium indicator, GCaMP6s. Using this strain, combined with advanced microscopy, we simultaneously measured live stimulus-evoked calcium responses in both neuronal and muscle cells with a wide spatial range and resolution.ConclusionsBy coupling in vivo live calcium imaging with behavioral assays we were able to gain functional insights into how stimulus-evoked neural and muscle activities are represented, modulated, and transformed in mosquito larvae enabling us to elucidate mosquito sensorimotor properties important for life-history-specific foraging strategies

Deciphering the brain's codes

The two sensory systems discussed use similar algorithms for the synthesis of the neuronal selectivity for the stimulus that releases a particular behavior, although the neural circuits, the brain sites involved, and even the species are different. This stimulus selectivity emerges gradually in a neural network organized according to parallel and hierarchical design principles. The parallel channels contain lower order stations with special circuits for the creation of neuronal selectivities for different features of the stimulus. Convergence of the parallel pathways brings these selectivities together at a higher order station for the eventual synthesis of the selectivity for the whole stimulus pattern. The neurons that are selective for the stimulus are at the top of the hierarchy, and they form the interface between the sensory and motor systems or between sensory systems of different modalities. The similarities of these two systems at the level of algorithms suggest the existence of rules of signal processing that transcend different sensory systems and species of animals

Sensory capacity: an information theoretical measure of the performance of a sensor

For a general sensory system following an external stochastic signal, we introduce the sensory capacity. This quantity characterizes the performance of a sensor: sensory capacity is maximal if the instantaneous state of the sensor has as much information about a signal as the whole time-series of the sensor. We show that adding a memory to the sensor increases the sensory capacity. This increase quantifies the improvement of the sensor with the addition of the memory. Our results are obtained with the framework of stochastic thermodynamics of bipartite systems, which allows for the definition of an efficiency that relates the rate with which the sensor learns about the signal with the energy dissipated by the sensor, which is given by the thermodynamic entropy production. We demonstrate a general tradeoff between sensory capacity and efficiency: if the sensory capacity is equal to its maximum 1, then the efficiency must be less than 1/2. As a physical realization of a sensor we consider a two component cellular network estimating a fluctuating external ligand concentration as signal. This model leads to coupled linear Langevin equations that allow us to obtain explicit analytical results.Comment: 15 pages, 7 figure

Sensory memory for odors is encoded in spontaneous correlated activity between olfactory glomeruli

Sensory memory is a short-lived persistence of a sensory stimulus in the nervous system, such as iconic memory in the visual system. However, little is known about the mechanisms underlying olfactory sensory memory. We have therefore analyzed the effect of odor stimuli on the first odor-processing network in the honeybee brain, the antennal lobe, which corresponds to the vertebrate olfactory bulb. We stained output neurons with a calcium-sensitive dye and measured across-glomerular patterns of spontaneous activity before and after a stimulus. Such a single-odor presentation changed the relative timing of spontaneous activity across glomeruli in accordance with Hebb's theory of learning. Moreover, during the first few minutes after odor presentation, correlations between the spontaneous activity fluctuations suffice to reconstruct the stimulus. As spontaneous activity is ubiquitous in the brain, modifiable fluctuations could provide an ideal substrate for Hebbian reverberations and sensory memory in other neural systems