Adaptive Event-Triggered Synchronization of Reaction-Diffusion Neural Networks

This article focuses on the design of an adaptive event-triggered sampled-data control (ETSDC) mechanism for synchronization of reaction-diffusion neural networks (RDNNs) with random time-varying delays. Different from the existing ETSDC schemes with predetermined constant thresholds, an adaptive ETSDC mechanism is proposed for RDNNs. The adaptive ETSDC mechanism can be promptly adaptively adjusted since the threshold function is based on the current sampled and latest transmitted signals. Thus, the adaptive ETSDC mechanism can effectively save communication resources for RDNNs. By taking the influence of uncertain factors, the random time-varying delays are considered, which belongs to two intervals in a probabilistic way. Then, by constructing an appropriate Lyapunov-Krasovskii functional (LKF), new synchronization criteria are derived for RDNNs. By solving a set of linear matrix inequalities (LMIs), the desired adaptive ETSDC gain is obtained. Finally, the merits of the adaptive ETSDC mechanism and the effectiveness of the proposed results are verified by one numerical example.

Bio-Inspired Robotic Solutions for Landslide Monitoring

Bio-inspired solutions are often taken into account to solve problems that nature took millions of years to deal with. In the field of robotics, when we need to design systems able to perform in unstructured environments, bio-inspiration can be a useful instrument both for mechanical design and for the control architecture. In the proposed work the problem of landslide monitoring is addressed proposing a bio-inspired robotic structure developed to deploy a series of smart sensors on target locations with the aim of creating a sensor network capable of acquiring information on the status of the area of interest. The acquired data can be used both to create models and to generate alert signals when a landslide event is identified in the early stage. The design process of the robotic system, including dynamic simulations and robot experiments, will be presented here.

Decentralized control mechanism underlying interlimb coordination of millipedes

Legged animals exhibit adaptive and resilient locomotion through interlimb coordination. The long-term goal of this study is to clarify the relationship between the number of legs and the inherent decentralized control mechanism for interlimb coordination. As a preliminary step, the study focuses on millipedes as they represent the species with the greatest number of legs among various animal species. A decentralized control mechanism involving local force feedback was proposed based on the qualitative findings of behavioural experiments in which responses to the removal of part of the terrain and leg amputation were observed. The proposed mechanism was implemented in a developed millipede-like robot to demonstrate that the robot can adapt to the removal of the part of the terrain and leg amputation in a manner similar to that in behavioural experiments.

Motor-Skill Learning in an Insect Inspired Neuro-Computational Control System

In nature, insects show impressive adaptation and learning capabilities. The proposed computational model takes inspiration from specific structures of the insect brain: after proposing key hypotheses on the direct involvement of the mushroom bodies (MBs) and on their neural organization, we developed a new architecture for motor learning to be applied in insect-like walking robots. The proposed model is a nonlinear control system based on spiking neurons. MBs are modeled as a nonlinear recurrent spiking neural network (SNN) with novel characteristics, able to memorize time evolutions of key parameters of the neural motor controller, so that existing motor primitives can be improved. The adopted control scheme enables the structure to efficiently cope with goal-oriented behavioral motor tasks. Here, a six-legged structure, showing a steady-state exponentially stable locomotion pattern, is exposed to the need of learning new motor skills: moving through the environment, the structure is able to modulate motor commands and implements an obstacle climbing procedure. Experimental results on a simulated hexapod robot are reported; they are obtained in a dynamic simulation environment and the robot mimicks the structures of Drosophila melanogaster.

Cyclic synchronous patterns in coupled discontinuous maps

Cyclic collective behaviors are commonly observed in biological and neuronal systems, yet the dynamical origins remain unclear. Here, by models of coupled discontinuous map lattices, we investigate the cyclic collective behaviors by means of cluster synchronization. Specifically, we study the synchronization behaviors in lattices of coupled periodic piecewise-linear maps and find that in the nonsynchronous regime the maps can be synchronized into different clusters and, as the system evolves, the synchronous clusters compete with each other and present the recurring process of cluster expanding, shrinking, and switching, i.e., showing the cyclic synchronous patterns. The dynamical mechanisms of cyclic synchronous patterns are explored, and the crucial roles of basin distribution are revealed. Moreover, due to the discontinuity feature of the map, the cyclic patterns are found to be very sensitive to the system initial conditions and parameters, based on which we further propose an efficient method for controlling the cyclic synchronous patterns.

On exploration of geometrically constrained space by medicinal leeches Hirudo verbana

Leeches are fascinating creatures: they have simple modular nervous circuitry yet exhibit a rich spectrum of behavioural modes. Leeches could be ideal blue-prints for designing flexible soft robots which are modular, multi-functional, fault-tolerant, easy to control, capable for navigating using optical, mechanical and chemical sensorial inputs, have autonomous inter-segmental coordination and adaptive decision-making. With future designs of leech-robots in mind we study how leeches behave in geometrically constrained spaces. Core results of the paper deal with leeches exploring a row of rooms arranged along a narrow corridor. In laboratory experiments we find that rooms closer to ends of the corridor are explored by leeches more often than rooms in the middle of the corridor. Also, in series of scoping experiments, we evaluate leeches capabilities to navigating in mazes towards sources of vibration and chemo-attraction. We believe our results lay foundation for future developments of robots mimicking behaviour of leeches.

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.

Design and Control of an IPMC Wormlike Robot

This paper presents an innovative wormlike robot controlled by cellular neural networks (CNNs) and made of an ionic polymer-metal composite (IPMC) self-actuated skeleton. The IPMC actuators, from which it is made of, are new materials that behave similarly to biological muscles. The idea that inspired the work is the possibility of using IPMCs to design autonomous moving structures. CNNs have already demonstrated their powerfulness as new structures for bio-inspired locomotion generation and control. The control scheme for the proposed IPMC moving structure is based on CNNs. The wormlike robot is totally made of IPMCs, and each actuator has to carry its own weight. All the actuators are connected together without using any other additional part, thereby constituting the robot structure itself. Worm locomotion is performed by bending the actuators sequentially from "tail" to "head," imitating the traveling wave observed in real-world undulatory locomotion. The activation signals are generated by a CNN. In the authors' opinion, the proposed strategy represents a promising solution in the field of autonomous and light structures that are capable of reconfiguring and moving in line with spatial-temporal dynamics generated by CNNs.

Soft tissue modelling through autowaves for surgery simulation

Modelling of soft tissue deformation is of great importance to virtual reality based surgery simulation. This paper presents a new methodology for simulation of soft tissue deformation by drawing an analogy between autowaves and soft tissue deformation. The potential energy stored in a soft tissue as a result of a deformation caused by an external force is propagated among mass points of the soft tissue by non-linear autowaves. The novelty of the methodology is that (i) autowave techniques are established to describe the potential energy distribution of a deformation for extrapolating internal forces, and (ii) non-linear materials are modelled with non-linear autowaves other than geometric non-linearity. Integration with a haptic device has been achieved to simulate soft tissue deformation with force feedback. The proposed methodology not only deals with large-range deformations, but also accommodates isotropic, anisotropic and inhomogeneous materials by simply changing diffusion coefficients.

An Adaptive, Self-Organizing Dynamical System for Hierarchical Control of Bio-Inspired Locomotion

In this paper, dynamical systems made up of locally coupled nonlinear units are used to control the locomotion of bio-inspired robots and, in particular, a simulation of an insect-like hexapod robot. These controllers are inspired by the biological paradigm of central pattern generators and are responsible for generating a locomotion gait. A general structure, which is able to change the locomotion gait according to environmental conditions, is introduced. This structure is based on an adaptive system, implemented by motor maps, and is able to learn the correct locomotion gait on the basis of a reward function. The proposed control system is validated by a large number of simulations carried out in a dynamic environment for simulating legged robots.

Mode transitions and wave propagation in a driven-dissipative Toda-Rayleigh ring

A circular lattice (ring) of N electronic elements with Toda-type exponential interactions and Rayleigh-type dissipation is used to illustrate wave formation, propagation, and switching between wave modes. A methodology is provided to help controlling modes, thus allowing it to realize any of (N-1) different wave modes (including soliton-type modes) and the switching between them by means of a single control parameter.