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

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

Non-Linear Neuronal Responses as an Emergent Property of Afferent Networks: A Case Study of the Locust Lobula Giant Movement Detector

In principle it appears advantageous for single neurons to perform non-linear operations. Indeed it has been reported that some neurons show signatures of such operations in their electrophysiological response. A particular case in point is the Lobula Giant Movement Detector (LGMD) neuron of the locust, which is reported to locally perform a functional multiplication. Given the wide ramifications of this suggestion with respect to our understanding of neuronal computations, it is essential that this interpretation of the LGMD as a local multiplication unit is thoroughly tested. Here we evaluate an alternative model that tests the hypothesis that the non-linear responses of the LGMD neuron emerge from the interactions of many neurons in the opto-motor processing structure of the locust. We show, by exposing our model to standard LGMD stimulation protocols, that the properties of the LGMD that were seen as a hallmark of local non-linear operations can be explained as emerging from the dynamics of the pre-synaptic network. Moreover, we demonstrate that these properties strongly depend on the details of the synaptic projections from the medulla to the LGMD. From these observations we deduce a number of testable predictions. To assess the real-time properties of our model we applied it to a high-speed robot. These robot results show that our model of the locust opto-motor system is able to reliably stabilize the movement trajectory of the robot and can robustly support collision avoidance. In addition, these behavioural experiments suggest that the emergent non-linear responses of the LGMD neuron enhance the system's collision detection acuity. We show how all reported properties of this neuron are consistently reproduced by this alternative model, and how they emerge from the overall opto-motor processing structure of the locust. Hence, our results propose an alternative view on neuronal computation that emphasizes the network properties as opposed to the local transformations that can be performed by single neurons

Sensitive Search for a Permanent Muon Electric Dipole Moment

We are proposing a new method to carry out a dedicated search for a permanent electric dipole moment (EDM) of the muon with a sensitivity at a level of 10^{-24} e cm. The experimental design exploits the strong motional electric field sensed by relativistic particles in a magnetic storage ring. As a key feature, a novel technique has been invented in which the g-2 precession is compensated with radial electric field. This technique will benefit greatly when the intense muon sources advocated by the developers of the muon storage rings and the muon colliders become available.Comment: 16 pages, 3 figures. Submitted for publication in Proceedings of the International Workshop on High Intensity Muon Sources (HIMUS99), KEK, Japan, December 1-4 199

Improved Measurement of the Positive Muon Anomalous Magnetic Moment

A new measurement of the positive muon's anomalous magnetic moment has been made at the Brookhaven Alternating Gradient Synchrotron using the direct injection of polarized muons into the superferric storage ring. The angular frequency difference omega_{a} between the angular spin precession frequency omega_{s} and the angular orbital frequency omega_{c} is measured as well as the free proton NMR frequency omega_{p}. These determine R = omega_{a} / omega_{p} = 3.707~201(19) times 10^{-3}. With mu_{mu} / mu_{p} = 3.183~345~39(10) this gives a_{mu^+} = 11~659~191(59) times 10^{-10} (pm 5 ppm), in good agreement with the previous CERN and BNL measurements for mu^+ and mu^-, and with the standard model prediction.Comment: 4 pages, 4 figures. accepted for publication in Phys. Rev. D62 Rapid Communication

Search for Lorentz and CPT Violation Effects in Muon Spin Precession

The spin precession frequency of muons stored in the $(g-2)$ storage ring has been analyzed for evidence of Lorentz and CPT violation. Two Lorentz and CPT violation signatures were searched for: a nonzero $\Delta\omega_{a}$ (=$\omega_{a}^{\mu^{+}}-\omega_{a}^{\mu^{-}}$); and a sidereal variation of $\omega_{a}^{\mu^{\pm}}$. No significant effect is found, and the following limits on the standard-model extension parameters are obtained: $b_{Z} =-(1.0 \pm 1.1)\times 10^{-23}$ GeV; $(m_{\mu}d_{Z0}+H_{XY}) = (1.8 \pm 6.0 \times 10^{-23})$ GeV; and the 95% confidence level limits $\check{b}_{\perp}^{\mu^{+}}< 1.4 \times 10^{-24}$ GeV and $\check{b}_{\perp}^{\mu^{-}} < 2.6 \times 10^{-24}$ GeV.Comment: 5 pages, 3 figures, submitted to Physical Review Letters, Modified to answer the referees suggestion

Colias IV: The Affordable Micro Robot Platform with Bio-inspired Vision

Vision is one of the most important sensing modalities for robots and has been realized on mostly large platforms. However for micro robots which are commonly utilized in swarm robotic studies, the visual ability is seldom applied or with only limited functions and resolution, due to the challenging requirements on the computation power and high data volume to deal with. This research has proposed the low-cost micro ground robot Colias IV, which is particularly designed to meet the requirements to allow embedded vision based tasks onboard, such as bio-inspired collision detection neural networks. Numerous of successful approaches have demonstrated that the proposed micro robot Colias IV to be a feasible platform for introducing visual based algorithms into swarm robotics

An Improved Limit on the Muon Electric Dipole Moment

Three independent searches for an electric dipole moment (EDM) of the positive and negative muons have been performed, using spin precession data from the muon g-2 storage ring at Brookhaven National Laboratory. Details on the experimental apparatus and the three analyses are presented. Since the individual results on the positive and negative muon, as well as the combined result, d=-0.1(0.9)E-19 e-cm, are all consistent with zero, we set a new muon EDM limit, |d| < 1.9E-19 e-cm (95% C.L.). This represents a factor of 5 improvement over the previous best limit on the muon EDM.Comment: 19 pages, 15 figures, 7 table

Search for Lorentz and CPT Violation Effects in Muon Spin Precession

The spin precession frequency of muons stored in the $(g-2)$ storage ring has been analyzed for evidence of Lorentz and CPT violation. Two Lorentz and CPT violation signatures were searched for: a nonzero $\Delta\omega_{a}$ (=$\omega_{a}^{\mu^{+}}-\omega_{a}^{\mu^{-}}$); and a sidereal variation of $\omega_{a}^{\mu^{\pm}}$. No significant effect is found, and the following limits on the standard-model extension parameters are obtained: $b_{Z} =-(1.0 \pm 1.1)\times 10^{-23}$ GeV; $(m_{\mu}d_{Z0}+H_{XY}) = (1.8 \pm 6.0 \times 10^{-23})$ GeV; and the 95% confidence level limits $\check{b}_{\perp}^{\mu^{+}}< 1.4 \times 10^{-24}$ GeV and $\check{b}_{\perp}^{\mu^{-}} < 2.6 \times 10^{-24}$ GeV.Comment: 5 pages, 3 figures, submitted to Physical Review Letters, Modified to answer the referees suggestion