Insect inspired behaviours for the autonomous control of mobile robots

Animals navigate through various uncontrolled environments with seemingly little effort. Flying insects, especially, are quite adept at manoeuvring in complex, unpredictable and possibly hostile environments. Through both simulation and real-world experiments, we demonstrate the feasibility of equipping a mobile robot with the ability to navigate a corridor environment, in real time, using principles based on insect-based visual guidance. In particular we have used the bees&rsquo; navigational strategy of measuring object range in terms of image velocity. We have also shown the viability and usefulness of various other insect behaviours: (i) keeping walls equidistant, (ii) slowing down when approaching an object, (iii) regulating speed according to tunnel width, and (iv) using visual motion as a measure of distance travelled.<br /

Reactive direction control for a mobile robot: A locust-like control of escape direction emerges when a bilateral pair of model locust visual neurons are integrated

Locusts possess a bilateral pair of uniquely identifiable visual neurons that respond vigorously to the image of an approaching object. These neurons are called the lobula giant movement detectors (LGMDs). The locust LGMDs have been extensively studied and this has lead to the development of an LGMD model for use as an artificial collision detector in robotic applications. To date, robots have been equipped with only a single, central artificial LGMD sensor, and this triggers a non-directional stop or rotation when a potentially colliding object is detected. Clearly, for a robot to behave autonomously, it must react differently to stimuli approaching from different directions. In this study, we implement a bilateral pair of LGMD models in Khepera robots equipped with normal and panoramic cameras. We integrate the responses of these LGMD models using methodologies inspired by research on escape direction control in cockroaches. Using ‘randomised winner-take-all’ or ‘steering wheel’ algorithms for LGMD model integration, the khepera robots could escape an approaching threat in real time and with a similar distribution of escape directions as real locusts. We also found that by optimising these algorithms, we could use them to integrate the left and right DCMD responses of real jumping locusts offline and reproduce the actual escape directions that the locusts took in a particular trial. Our results significantly advance the development of an artificial collision detection and evasion system based on the locust LGMD by allowing it reactive control over robot behaviour. The success of this approach may also indicate some important areas to be pursued in future biological research

Constructing living buildings: a review of relevant technologies for a novel application of biohybrid robotics

Biohybrid robotics takes an engineering approach to the expansion and exploitation of biological behaviours for application to automated tasks. Here, we identify the construction of living buildings and infrastructure as a high-potential application domain for biohybrid robotics, and review technological advances relevant to its future development. Construction, civil infrastructure maintenance and building occupancy in the last decades have comprised a major portion of economic production, energy consumption and carbon emissions. Integrating biological organisms into automated construction tasks and permanent building components therefore has high potential for impact. Live materials can provide several advantages over standard synthetic construction materials, including self-repair of damage, increase rather than degradation of structural performance over time, resilience to corrosive environments, support of biodiversity, and mitigation of urban heat islands. Here, we review relevant technologies, which are currently disparate. They span robotics, self-organizing systems, artificial life, construction automation, structural engineering, architecture, bioengineering, biomaterials, and molecular and cellular biology. In these disciplines, developments relevant to biohybrid construction and living buildings are in the early stages, and typically are not exchanged between disciplines. We, therefore, consider this review useful to the future development of biohybrid engineering for this highly interdisciplinary application.publishe

Towards Computational Models and Applications of Insect Visual Systems for Motion Perception: A Review

Motion perception is a critical capability determining a variety of aspects of insects' life, including avoiding predators, foraging and so forth. A good number of motion detectors have been identified in the insects' visual pathways. Computational modelling of these motion detectors has not only been providing effective solutions to artificial intelligence, but also benefiting the understanding of complicated biological visual systems. These biological mechanisms through millions of years of evolutionary development will have formed solid modules for constructing dynamic vision systems for future intelligent machines. This article reviews the computational motion perception models originating from biological research of insects' visual systems in the literature. These motion perception models or neural networks comprise the looming sensitive neuronal models of lobula giant movement detectors (LGMDs) in locusts, the translation sensitive neural systems of direction selective neurons (DSNs) in fruit flies, bees and locusts, as well as the small target motion detectors (STMDs) in dragonflies and hover flies. We also review the applications of these models to robots and vehicles. Through these modelling studies, we summarise the methodologies that generate different direction and size selectivity in motion perception. At last, we discuss about multiple systems integration and hardware realisation of these bio-inspired motion perception models

Multiple chaotic central pattern generators with learning for legged locomotion and malfunction compensation

An originally chaotic system can be controlled into various periodic dynamics. When it is implemented into a legged robot's locomotion control as a central pattern generator (CPG), sophisticated gait patterns arise so that the robot can perform various walking behaviors. However, such a single chaotic CPG controller has difficulties dealing with leg malfunction. Specifically, in the scenarios presented here, its movement permanently deviates from the desired trajectory. To address this problem, we extend the single chaotic CPG to multiple CPGs with learning. The learning mechanism is based on a simulated annealing algorithm. In a normal situation, the CPGs synchronize and their dynamics are identical. With leg malfunction or disability, the CPGs lose synchronization leading to independent dynamics. In this case, the learning mechanism is applied to automatically adjust the remaining legs' oscillation frequencies so that the robot adapts its locomotion to deal with the malfunction. As a consequence, the trajectory produced by the multiple chaotic CPGs resembles the original trajectory far better than the one produced by only a single CPG. The performance of the system is evaluated first in a physical simulation of a quadruped as well as a hexapod robot and finally in a real six-legged walking machine called AMOSII. The experimental results presented here reveal that using multiple CPGs with learning is an effective approach for adaptive locomotion generation where, for instance, different body parts have to perform independent movements for malfunction compensation.Comment: 48 pages, 16 figures, Information Sciences 201

A general architecture for robotic swarms

Swarms are large groups of simplistic individuals that collectively solve disproportionately complex tasks. Individual swarm agents are limited in perception, mechanically simple, have no global knowledge and are cheap, disposable and fallible. They rely exclusively on local observations and local communications. A swarm has no centralised control. These features are typifed by eusocial insects such as ants and termites, who construct nests, forage and build complex societies comprised of primitive agents. This project created the basis of a general swarm architecture for the control of insect-like robots. The Swarm Architecture is inspired by threshold models of insect behaviour and attempts to capture the salient features of the hive in a closely defined computer program that is hardware agnostic, swarm size indifferent and intended to be applicable to a wide range of swarm tasks. This was achieved by exploiting the inherent limitations of swarm agents. Individual insects were modelled as a machine capable only of perception, locomotion and manipulation. This approximation reduced behaviour primitives to a fixed tractable number and abstracted sensor interpretation. Cooperation was achieved through stigmergy and decisions made via a behaviour threshold model. The Architecture represents an advance on previous robotic swarms in its generality - swarm control software has often been tied to one task and robot configuration. The Architecture's exclusive focus on swarms, sets it apart from existing general cooperative systems, which are not usually explicitly swarm orientated. The Architecture was implemented successfully on both simulated and real-world swarms

Biomimetic visual navigation in a corridor: to centre or not to centre?

International audienceAs a first step toward an Automatic Flight Control System (AFCS) for Micro-Air Vehicle (MAV) obstacle avoidance, we introduce a vision based autopilot (LORA: Lateral Optic flow Regulation Autopilot), which is able to make a hovercraft automatically follow a wall or centre between the two walls of a corridor. A hovercraft is endowed with natural stabilization in pitch and roll while keeping two translational degrees of freedom (X and Y) and one rotational degree of freedom (yaw Ψ). We show the feasibility of an OF regulator that maintains the lateral Optic Flow (OF) on one wall equal to an OF set-point. The OF sensors used are Elementary Motion Detectors (EMDs), whose working was directly inspired by the housefly motion detecting neurons. The properties of these neurons were previously analysed at our laboratory by performing electrophysiological recordings while applying optical microstimuli to single photoreceptor cells of the compound eye. The simulation results show that depending on the OF set-point, the hovercraft either centres along the midline of the corridor or follows one of the two walls, even with local lack of optical texture on one wall, such as caused, for instance, by an open door or a T-junction. All these navigational tasks are performed with one and the same feedback loop, which consists of a lateral OF regulation loop that permits relatively high-speed navigation (1m/s, i.e 3 body-lengths per second). The passive visual sensors and the simple processing system are suitable for use with MAVs with an avionic payload of only a few grams. The goal is to achieve MAV automatic guidance or to relieve a remote operator from guiding it in challenging environments such as urban canyons or indoor environments

Neuromimetic Robots inspired by Insect Vision

International audienceEquipped with a less-than-one-milligram brain, insects fly autonomously in complex environments without resorting to any Radars, Ladars, Sonars or GPS. The knowledge gained during the last decades on insects' sensory-motor abilities and the neuronal substrates involved provides us with a rich source of inspiration for designing tomorrow's self-guided vehicles and micro-vehicles, which are to cope with unforeseen events on the ground, in the air, under water or in space. Insects have been in the business of sensory-motor integration for several 100 millions years and can therefore teach us useful tricks for designing agile autonomous vehicles at various scales. Constructing a "biorobot" first requires exactly formulating the signal processing principles at work in the animal. It gives us, in return, a unique opportunity of checking the soundness and robustness of those principles by bringing them face to face with the real physical world. Here we describe some of the visually-guided terrestrial and aerial robots we have developed on the basis of our biological findings. These robots (Robot Fly, SCANIA, FANIA, OSCAR, OCTAVE and LORA) all react to the optic flow (i.e., the angular speed of the retinal image). Optic flow is sensed onboard the robots by miniature vision sensors called Elementary Motion Detectors (EMDs). The principle of these electro-optical velocity sensors was derived from optical/electrophysiological studies where we recorded the responses of single neurons to optical microstimulation of single photoreceptor cells in a model visual system: the fly's compound eye. Optic flow based sensors rely solely on contrast provided by reflected (or scattered) sunlight from any kind of celestial bodies in a given spectral range. These nonemissive, powerlean sensors offer potential applications to manned or unmanned aircraft. Applications can also be envisaged to spacecraft, from robotic landers and rovers to asteroid explorers or space station dockers, with interesting prospects as regards reduction in weight and consumption