Ear-Bot: Locust Ear-on-a-Chip Bio-Hybrid Platform

During hundreds of millions of years of evolution, insects have evolved some of the most efficient and robust sensing organs, often far more sensitive than their man-made equivalents. In this study, we demonstrate a hybrid bio-technological approach, integrating a locust tympanic ear with a robotic platform. Using an Ear-on-a-Chip method, we manage to create a long-lasting miniature sensory device that operates as part of a bio-hybrid robot. The neural signals recorded from the ear in response to sound pulses, are processed and used to control the robot’s motion. This work is a proof of concept, demonstrating the use of biological ears for robotic sensing and control.

Insect-inspired neuromorphic computing

The steady improvement in the performance of computing systems seen for many decades is levelling off as the miniaturization of semiconducting technology approaches the atomic limit, facing severe physical and technological issues. Neuromorphic computing is an emerging solution which makes use of silicon technology in a different way, inline with the computational principles observed in animal nervous systems. In this article, we argue that the nervous systems of insects in particular offer themselves as an ideal starting point for incorporation into realistic neuromorphic systems and review research in developing insect-inspired neuromorphic systems. We conclude with an exciting yet tangible vision of a full neuromorphic sensory-motor system where a liquid state machine modelling the function of the insect mushroom body links input to output and allows for amalgamation of the work discussed in a hierarchical framework of a full system inspired by the concept of how information flows through insects.

Optic flow-based collision-free strategies: From insects to robots

Flying insects are able to fly smartly in an unpredictable environment. It has been found that flying insects have smart neurons inside their tiny brains that are sensitive to visual motion also called optic flow. Consequently, flying insects rely mainly on visual motion during their flight maneuvers such as: takeoff or landing, terrain following, tunnel crossing, lateral and frontal obstacle avoidance, and adjusting flight speed in a cluttered environment. Optic flow can be defined as the vector field of the apparent motion of objects, surfaces, and edges in a visual scene generated by the relative motion between an observer (an eye or a camera) and the scene. Translational optic flow is particularly interesting for short-range navigation because it depends on the ratio between (i) the relative linear speed of the visual scene with respect to the observer and (ii) the distance of the observer from obstacles in the surrounding environment without any direct measurement of either speed or distance. In flying insects, roll stabilization reflex and yaw saccades attenuate any rotation at the eye level in roll and yaw respectively (i.e. to cancel any rotational optic flow) in order to ensure pure translational optic flow between two successive saccades. Our survey focuses on feedback-loops which use the translational optic flow that insects employ for collision-free navigation. Optic flow is likely, over the next decade to be one of the most important visual cues that can explain flying insects' behaviors for short-range navigation maneuvers in complex tunnels. Conversely, the biorobotic approach can therefore help to develop innovative flight control systems for flying robots with the aim of mimicking flying insects' abilities and better understanding their flight.

Results on the convergence of Braitenberg vehicle 3a

Braitenberg vehicles are well-known models of animal behavior used as steering mechanisms in mobile robotics and artificial life. Because of their simplicity, they are mainly used for teaching robotics, while the lack of a quantitative theory has limited their use for research purposes. This article contributes to our formal understanding of Braitenberg vehicle 3a by presenting the convergence properties of its trajectories under parabolic-shaped stimuli. We show previously unreported features of the motion of the vehicle: the conditional stability, the oscillatory behavior, and the existence of periodic trajectories. The mathematical model used provides a theoretical relation between the environment, the internal control mechanism of the vehicle, and some morphological parameters, a link already found in experimental works. This work provides theoretical support for experimental research using Braitenberg vehicle 3a, and paves the way for further research in biology, robotics, and artificial life.

Cellular Nonlinear Networks for the emergence of perceptual states: application to robot navigation control

In this paper a new general purpose perceptual control architecture, based on nonlinear neural lattices, is presented and applied to solve robot navigation tasks. Insects show the ability to react to certain stimuli with simple reflexes, using direct sensory-motor pathways, which can be considered as basic behaviors, inherited and pre-wired. Relevant brain centres, known as Mushroom Bodies (MB) and Central Complex (CX) were recently identified in insects: though their functional details are not yet fully understood, it is known that they provide secondary pathways allowing the emergence of cognitive behaviors. These are gained through the coordination of the basic abilities to satisfy the insect's needs. Taking inspiration from this evidence, our architecture modulates, through a reinforcement learning, a set of competitive and concurrent basic behaviors in order to accomplish the task assigned through a reward function. The core of the architecture is constituted by the so-called Representation layer, used to create a concise picture of the current environment situation, fusing together different stimuli for the emergence of perceptual states. These perceptual states are steady state solutions of lattices of Reaction-Diffusion Cellular Nonlinear Networks (RD-CNN), designed to show Turing patterns. The exploitation of the dynamics of the multiple equilibria of the network is emphasized through the adaptive shaping of the basins of attraction for each emerged pattern. New experimental campaigns on standard robotic platforms are reported to demonstrate the potentiality and the effectiveness of the approach.

Application of chaotic dynamics in a recurrent neural network to control: hardware implementation into a novel autonomous roving robot

Originating from a viewpoint that complex/chaotic dynamics would play an important role in biological system including brains, chaotic dynamics introduced in a recurrent neural network was applied to control. The results of computer experiment was successfully implemented into a novel autonomous roving robot, which can only catch rough target information with uncertainty by a few sensors. It was employed to solve practical two-dimensional mazes using adaptive neural dynamics generated by the recurrent neural network in which four prototype simple motions are embedded. Adaptive switching of a system parameter in the neural network results in stationary motion or chaotic motion depending on dynamical situations. The results of hardware implementation and practical experiment using it show that, in given two-dimensional mazes, the robot can successfully avoid obstacles and reach the target. Therefore, we believe that chaotic dynamics has novel potential capability in controlling, and could be utilized to practical engineering application.

Phonotactic walking paths of field crickets in closed-loop conditions and their simulation using a stochastic model

Field cricket females localize one of many singing males in the field in closed-loop multi-source conditions. To understand this behaviour, field cricket phonotaxis was investigated in a closed-loop walking phonotaxis paradigm, in response to two simultaneously active speakers playing aphasic calling songs. Female phonotactic paths were oriented towards the louder sound sources, but showed great inter-individual variability. Decisions made in the initial phases were correlated with the overall directions of the paths. Interestingly, the sound pressure levels of stimuli did not greatly influence several features of phonotactic paths such as sinuosity, walking bout lengths and durations. In order to ascertain the extent of our understanding of walking phonotaxis, a stochastic model was used to simulate the behaviour observed in the experiment. The model incorporated data from the experiment and our current understanding of field cricket auditory physiology. This model, based on stochastic turning towards the louder side, successfully recaptured several qualitative and quantitative features of the observed phonotactic paths. The simulation also reproduced the paths observed in a separate outdoor field experiment. Virtual crickets that were unilaterally deafened or had poor ear directionality exhibited walking paths similar to those observed in previous experiments.

Validating biorobotic models

Some issues in neuroscience can be addressed by building robot models of biological sensorimotor systems. What we can conclude from building models or simulations, however, is determined by a number of factors in addition to the central hypothesis we intend to test. These include the way in which the hypothesis is represented and implemented in simulation, how the simulation output is interpreted, how it is compared to the behaviour of the biological system, and the conditions under which it is tested. These issues will be illustrated by discussing a series of robot models of cricket phonotaxis behaviour.