Switching mechanism of sensor-motor coordination through an oscillator network model.

Insects have small brains, but their behavior is highly adaptive; this leads us to conclude that their brains possess a simple adaptation mechanism. This paper focuses on the pheromone processing of crickets, varying their aggression depending on their global neural connection, and proposes a behavior selection mechanism that can be controlled by network transformation. The controller is composed of an oscillator network, and its behavior is decided by the synchrony of organic oscillations. Furthermore, every network component corresponds to a certain brain module. A model is realized by using an analog circuit, and it is applied to a simple robot that displays the behavior of a real insect.     

Switching Mechanism of Sensor–Motor Coordination Through an Oscillator Network Model

Insects have small brains, but their behavior is highly adaptive; this leads us to conclude that their brains possess a simple adaptation mechanism. This paper focuses on the pheromone processing of crickets, varying their aggression depending on their global neural connection, and proposes a behavior selection mechanism that can be controlled by network transformation. The controller is composed of an oscillator network, and its behavior is decided by the synchrony of organic oscillations. Furthermore, every network component corresponds to a certain brain module. A model is realized by using an analog circuit, and it is applied to a simple robot that displays the behavior of a real insect.     

Development of a desktop swarm robot system based on pheromone communication

Complex and adaptive population behavior emerges in social insects. In ants, in particular, pheromone communication is the key to understanding their swarm intelligence. This article proposes a swarm robot system based on pheromone communication, and reports on the current status of the development of our robot system. We believe that the system could be used in swarm robotics and complex systems education.     

Robots, insects and swarm intelligence

The aim of this paper is to consider the relationships between robots and insects. To this end, an overview is provided of the two main areas in which insects have been implicated in robotics research. First, robots have been used to provide working models of mechanisms underlying insect behaviour. Second, there are developments in robotics that have been inspired by our understanding of insect behaviour; in particular the approach of swarm robotics. In the final section of the paper, the possibility of achieving “strong swarm intelligence” is discussed. Two possible interpretations of strong swarm intelligence are raised: (1) the emergence of a group mind from a natural, or robot swarm, and (2) that behaviours could emerge from a swarm of artificial robots in the same way as they emerge from a biological swarm. Both interpretations are dismissed as being unachievable in principle. It is concluded that bio-robotic modelling and biological inspiration have made important contributions to both insect and robot research, but insects and robots remain separated by the divide between the living and the purely mechanical.     

Cognitive neuroscience and robotics: Advancements and future research directions

In recent years, brain-based technologies that capitalise on human abilities to facilitate human–system/robot interactions have been actively explored, especially in brain robotics. Brain–computer interfaces, as applications of this conception, have set a path to convert neural activities recorded by sensors from the human scalp via electroencephalography into valid commands for robot control and task execution. Thanks to the advancement of sensor technologies, non-invasive and invasive sensor headsets have been designed and developed to achieve stable recording of brainwave signals. However, robust and accurate extraction and interpretation of brain signals in brain robotics are critical to reliable task-oriented and opportunistic applications such as brainwave-controlled robotic interactions. In response to this need, pervasive technologies and advanced analytical approaches to translating and merging critical brain functions, behaviours, tasks, and environmental information have been a focus in brain-controlled robotic applications. These methods are composed of signal processing, feature extraction, representation of neural activities, command conversion and robot control. Artificial intelligence algorithms, especially deep learning, are used for the classification, recognition, and identification of patterns and intent underlying brainwaves as a form of electroencephalography. Within the context, this paper provides a comprehensive review of the past and the current status at the intersection of robotics, neuroscience, and artificial intelligence and highlights future research directions.     

Construction of a brain–machine hybrid system to evaluate adaptability of an insect

Insects perform adaptive behavior according to changing environmental conditions using comparatively small brains. Because adaptability is generated through the relationship among brain, body and environment, it is necessary to examine how a brain works under these conditions. In this study, to understand neural processing involved in adaptive behavior, we constructed a brain–machine hybrid system using motor signals related to the steering behavior of the male silkworm moth for controlling a two-wheeled mobile robot. We developed this hybrid system according to the following steps. (1) We selected steering signals corresponding to walking direction that were activated during neck swinging induced by optic flow and pheromone stimuli. (2) To control a robot by neural activity, we implemented a spike-behavior conversion rule such that frequency of the left and right neck motor neurons’ spikes was linearly converted into rotation of the wheels. (3) For electrophysiological multi-unit recordings on a robot, we developed small amplifiers. Using this hybrid system, we could observe the programmed behavioral pattern and orientation toward a pheromone source. Moreover, we compared the orientation behavior of moths and that of the hybrid system at different pheromone stimulus frequencies. From these experiments, we concluded that we could reconstruct silkworm moth behavior on the hybrid system.     

From insects to machines

Insects and hummingbirds remain unmatched in their aerodynamic ability to hover in place in addition to other acrobatic feats such as flying backward and sideways by exploiting flapping-wing motion [1]. Although this remarkable ability is key to making small-scale aircraft, flapping-hovering behavior has been difficult to reproduce artificially because of the challenging stability, power, and aeroelastic phenomena involved. Recent interest in small-scale unmanned air vehicles, especially those capable of hovering like insects and hummingbirds, is driven by many potential applications. A number of flapping machines have been developed [2]-[8], but only two are capable of untethered hovering flight [9], [10]. A key challenge is to demonstrate a stable untethered flapping-hovering ability at a weight and power approximating that of insects and birds where flapping-hovering flight is observed in nature. Here we demonstrate, for the first time, a passively stable 24-g machine capable of flapping-hovering flight at a Reynolds number similar to insects (Re = 8 × 10³). This architecture, particularly the passive stability, may help in the design of insect-sized hovering vehicles as well as shed light on the aeroelastic dynamic principles underlying insect flight.     

Adaptive robotics in entertainment

Techniques developed in the fields of evolutionary computation, adaptive systems, agents, and artificial neural networks can be used in entertainment robotics in order to provide easy access to the robot technology. We have developed a number of user-guided approaches based on the techniques from these research fields. These techniques include user-guided behaviour-based systems, user-guided evolutionary robotics, user-guided co-evolutionary robotics, and morphological development. All these techniques are applied to allow children to develop their own robot behaviours in a very easy and fast manner. Here, I show examples with development of Khepera robots and LEGO MINDSTORMS robots, including the World Cup’98 stadium, the Co-evolutionary Robot Soccer Show, the Toybot Soccer Player, the LEGO Interactive Football, and RoboCup Junior Rescue.     

基于卷积神经网络的虫情图像分割和计数方法

为提高虫情图像的分割和计数的准确率,提出了一种基于卷积神经网络的虫情图像分割和计数方法。该方法基于U-Net模型构造了一种昆虫图像分割的模型Insect-Net,将完整的虫情图像和切割后的虫情图像分别输入模型后,提取两者特征进行融合。将融合后的特征输入1个1×1的卷积层得到最终分割结果,再将得到的结果二值化后,采用轮廓检测算法将昆虫目标与背景分离并计数。实验结果表明,该方法在虫情图像中取得了较高的分割正确率和计数正确率,分别为94.4%和89.2%。用深度学习和卷积神经网络的方法有效提高了虫情图像的计数精度,并且为昆虫识别分类提供了大量的无背景数据集。     

Intelligent fault-tolerant control using adaptive and learning methods

Stimulated by the growing demand for improving system performance and reliability, fault-tolerant system design has been receiving significant attention. This paper proposes a new fault-tolerant control methodology using adaptive estimation and control approaches based on the learning capabilities of neural networks or fuzzy systems. On-line approximation-based stable adaptive neural/fuzzy control is studied for a class of input–output feedback linearizable time-varying nonlinear systems. This class of systems is large enough so that it is not only of theoretical interest but also of practical applicability. Moreover, the fault-tolerance ability of the adaptive controller has been further improved by exploiting information estimated from a fault-diagnosis unit designed by interfacing multiple models with an expert supervisory scheme. Simulation examples for a fault-tolerant jet engine control problem are given to demonstrate the effectiveness of the proposed scheme.     

Teamwork in Self-Organized Robot Colonies

Swarm robotics draws inspiration from decentralized self-organizing biological systems in general and from the collective behavior of social insects in particular. In social insect colonies, many tasks are performed by higher order group or team entities, whose task-solving capacities transcend those of the individual participants. In this paper, we investigate the emergence of such higher order entities. We report on an experimental study in which a team of physical robots performs a foraging task. The robots are “identical” in hardware and control. They make little use of memory and take actions purely on the basis of local information.      

Evolution and development of neural controllers for locomotion,gradient-following, and obstacle-avoidance in artificial insects

This paper describes how the SGOCE paradigm has been used to evolve developmental programs capable of generating recurrent neural networks that control the behavior of simulated insects. This paradigm is characterized by an encoding scheme, an evolutionary algorithm, syntactic constraints, and an incremental strategy that are described in turn. The additional use of an insect model equipped with six legs and two antennae made it possible to generate control modules that allowed it to successively add gradient-following and obstacle-avoidance capacities to walking behavior. The advantages of this evolutionary approach, together with directions for future work, are discussed.     

Evolutionary design and behavior analysis of neuromodulatory neural networks for mobile robots control

Evolutionary Robotics (ER) is one of promising approaches to design robot controllers which essentially have complicated and/or complex properties. In most ER research, the sensory–motor mappings of robots are represented as artificial neural networks, and their connection weights (and sometimes the structure of the networks) can be optimized in the parameter spaces by using evolutionary computation. However, generally, the evolved neural controllers could be fragile in unexperienced environments, especially in real worlds, because the evolutionary optimization processes would be executed in idealized simulators. This is known as the gap problem between the simulated and real worlds. To overcome this, the author focused on evolving an on-line learning ability instead of weight parameters in a simulated environment. According to recent biological findings, actually, the kinds of on-line adaptation abilities can be found in real nervous systems of insects and crustaceans, and it is also known that a variety of neuromodulators (NMs) play crucial roles to regulate the network characteristics (i.e. activating/blocking/changing of synaptic connections). Based on this, a neuromodulatory neural network model was proposed and it was utilized as a mobile robot controller. In the paper, the detail behavior analysis of the evolved neuromodulatory neural network is also discussed.     

Shoe soles for the gripping robot: Searching for polymer-based materials maximising friction

The development of light-weight climbing robots capable of operating in various environments is a growing area in robotics. That is why there is a strong need for new tribologically-optimised materials at their gripping devices that may lead to the enhancement of the attachment force while reducing energy expenditure. Since insects are equipped with a set of very efficient attachment systems, enabling them to grip a variety of substrates, they may provide approaches for the innovation of climbing robots’ grippers. The goal of our study was a broad screening of polymeric materials to find out those with similar structure and functional principles to biological examples known from insect smooth attachment systems. Friction, adhesion and stiffness measurements were carried out. Rubber foamy materials covered with thin polymeric films and sandwich-like materials provided excellent compliant properties and the best performance in experiments (friction coefficients up to 3.2).     

Immuno-inspired robotic applications: A review

The ability of artificial immune systems to adapt to varying pathogens makes such systems a suitable choice for various robotic applications. Generally, immunity-based robotic applications map local instantaneous sensory information into either an antigen or a co-stimulatory signal, according to the choice of representation schema. Algorithms then use relevant immune functions to output either evolved antibodies or maturity of dendritic cells, in terms of actuation signals. It is observed that researchers do not try to replicate the biological immunity but select necessary immune functions instead, resulting in an ad-hoc manner these applications are reported. On the other hand, the paradigm shift in robotics research from reactive to probabilistic approaches is also not being reflected in these applications. Authors, therefore, present a detailed review of immuno-inspired robotic applications in an attempt to identify the possible areas to explore. Moreover, the literature has been categorized according to the underlying immuno-definitions. Implementation details have been critically reviewed in terms of corresponding mathematical expressions and their representation schema that include binary, real or hybrid approaches. Limitations of reported applications have also been identified in light of modern immunological interpretations including the danger theory. As a result of this study, authors suggest a renewed focus on innate immunity, action contextualization prior to B/T cell invocation and behavior evolution instead of arbitration. In this context, a multi-tier immunological framework for robotics research, combining innate and adaptive components together is also suggested and skeletonized.     

微扑翼飞行中的生物和仿生力传感器述评

随着昆虫飞行机理和仿生扑翼飞行研究的不断进展,理解昆虫的传感系统并研制适于微型扑翼飞行器的仿生传感器逐渐成为目前关注的焦点,而MEMS的快速发展使得研制新型的仿生微型传感器成为可能。简要介绍仿生扑翼飞行中传感系统研究现状,对昆虫飞行中采用的力传感器原理、分类进行了介绍;重点分析了几种典型的力传感器;归纳和总结了基于昆虫毛状传感器、钟状传感器和平衡棒仿生,采用各种MEMS技术研制的微型力传感器的生产工艺和应用特点;探索和展望了仿生力传感器的发展特点和今后的发展趋势。     

A novel soft computing architecture for the control of autonomous walking robots

An integration of concepts from neurobiology, applied psychology, insect physiology and behaviour based robotics has led us to propose a novel generic systems architecture for the intelligent control of mobile robots and in particular, autonomous walking machines. (We define what we mean by “autonomy”.) The control architecture is hierarchical and will be described from a top-down perspective. Level one consists of interpreting a motivation and translating this into high-level commands. Once a high-level command is generated, a range of internal representations or “cognitive maps” may be employed at level two to help provide body-centred motion. At level three of the hierarchy kinematic planning is performed. The fourth level – dynamic compensation – requires feedback from the actuators and compensates for errors in the target vectors provided by the kinematic level and caused by systematic dynamic uncertainties or environmental disturbances. This is implemented using adaptive neural controllers. The interfaces will be described and results from simulation and implementation of levels 2–4 on a hexapod robot will be presented. The hierarchy employs the following soft computing techniques: evolution strategies, cognitive maps, adaptive heuristic critics, temporal difference learning and adaptive neural control using linear-equivalent neural networks.     

Artificial neural networks for robotics coordinate transformation

Artificial neural networks with such characteristics as learning, graceful degradation, and speed inherent to parallel distributed architectures might provide a flexible and cost solution to the real time control of robotics systems. In this investigation artificial neural networks are presented for the coordinate transformation mapping of a two-axis robot modeled with Fischertechnik physical modeling components. The results indicate that artificial neural systems could be utilized for practical situations and that extended research in these neural structures could provide adaptive architectures for dynamic robotics control.     

Artificial neural network based robot control: An overview

The current thrust of research in robotics is to build robots which can operate in dynamic and/or partially known environments. The ability of learning endows the robot with a form of autonomous intelligence to handle such situations. This paper focuses on the intersection of the fields of robot control and learning methods as represented by artificial neural networks. An in-depth overview of the application of neural networks to the problem of robot control is presented. Some typical neural network architectures are discussed first. The important issues involved in the study of robotics are then highlighted. This paper concentrates on the neural network applications to the motion control of robots involved in both non-contact and contact tasks. The current state of research in this area is surveyed and the strengths and weakness of the present approaches are emphasized. The paper concludes by indentifying areas which need future research work.     

Evolving Intelligence in Humans and Machines: Integrative Evolving Connectionist Systems Approach

One of the main properties of human intelligence is that it is evolving (developing, revealing, unfolding) based on: (1) Genetically "wired" rules; (2) Experience and learning during life time. The paper argues that we need to understand how the brain operates at its different levels of information processing and then use some of these principles "when building intelligent machines. Without "drowning" into the sea of details, some main principles of information processing in the brain at cognitive-, neuronal-, genetic-, and particle field information levels are reviewed. The paper takes the approach towards understanding and building integrative connectionist systems, that integrate principles and rules from different hierarchical levels of information processing in their dynamic interaction, as an approach to develop intelligent machines. Examples given include: simple evolving connectionist systems; evolving spiking neural networks; integrative neurogenetic models; genetically defined robots; quantum evolutionary algorithms for exponentially faster optimization; integrative quantum neural networks. Some of the new integrative models are significantly faster in feature selection and learning and can be used to solve efficiently NP complete biological and engineering problems for adaptive, incremental learning in a large dimensional space-an important feature of the human intelligence. They can also help to better understand complex information processes in the brain, especially how information processes at different information levels interact to achieve a higher level intelligent human behavior. Open questions, challenges and directions for further research are presented.