Colias-Φ: an autonomous micro robot for artificial pheromone communication

Ants pheromone communication is an efficient mechanism which took inspiration from nature. It has been used in various artificial intelligence and multi robotics researches. This paper presents the development of an autonomous micro robot to be used in swarm robotic researches especially in pheromone based communication systems. The robot is an extended version of Colias micro robot with capability of decoding and following artificial pheromone trails. We utilize a low-cost experimental setup to implement pheromone-based scenarios using a flat LCD screen and a USB camera. The results of the performed experiments with group of robots demonstrated the feasibility of Colias-Φ to be used in pheromone based experiments

Mechanical implementation of kinematic synergy for continual grasping generation of anthropomorphic hand

The synergy-based motion generation of current anthropomorphic hands generally employ the static posture synergy, which is extracted from quantities of joint trajectory, to design the mechanism or control strategy. Under this framework, the temporal weight sequences of each synergy from pregrasp phase to grasp phase are required for reproducing any grasping task. Moreover, the zero-offset posture has to be preset before starting any grasp. Thus, the whole grasp phase appears to be unlike natural human grasp. Up until now, no work in the literature addresses these issues toward simplifying the continual grasp by only inputting the grasp pattern. In this paper, the kinematic synergies observed in angular velocity profile are employed to design the motion generation mechanism. The kinematic synergy extracted from quantities of grasp tasks is implemented by the proposed eigen cam group in tendon space. The completely continual grasp from the fully extending posture only require averagely rotating the two eigen cam groups one cycle. The change of grasp pattern only depends on respecifying transmission ratio pair for the two eigen cam groups. An illustrated hand prototype is developed based on the proposed design principle and the grasping experiments demonstrate the feasibility of the design method. The potential applications include the prosthetic hand that is controlled by the classified pattern from the bio-signal

Signaling role of iron in NF-kappa B activation in hepatic macrophages

Iron is both essential and toxic for cells and impaired iron homeostasis has been shown to cause or potentiate various forms of liver injury. Research in our laboratory suggests that iron also plays a pivotal role in intracellular signaling for NF-kappa B activation in hepatic macrophages (HM). Our results showed: 1) HM from alcohol-fed rats had a increase in the nonheme iron content accompanied by NF-kappa B activation; 2) iron chelation normalized nonheme iron concentration and blocked enhanced NF-kappa B activation and TNF-alpha expression in these cells; 3) LPS-induced NF-kappa B activation was also blocked by iron chelator; 4) iron directly induced TNF-alpha expression via IKK and NF-kappa B activation in normal HM. We propose that iron acts as an independent proinflammatory molecule via induction of the intracellular signaling for NF-kappa B activation in HM and primes the liver for chronic inflammation and injury

A Bioinspired Airfoil Optimization Technique Using Nash Genetic Algorithm

Natural fliers glide and minimize wing articulation to conserve energy for endured and long range flights. Elucidating the underlying physiology of such capability could potentially address numerous challenging problems in flight engineering. However, primitive nature of the bioinspired research impedes such achievements, hence to bypass these limitations, this study introduces a bioinspired non-cooperative multiple objective optimization methodology based on a novel fusion of PARSEC, Nash strategy, and genetic algorithms to achieve insect-level aerodynamic efficiencies. The proposed technique is validated on a conventional airfoil as well as the wing crosssection of a desert locust (Schistocerca gregaria) at low Reynolds number, and we have recorded a 77% improvement in its gliding ratio

Aerodynamic Analysis and Optimization of Gliding Locust Wing Using Nash Genetic Algorithm

Natural fliers glide and minimize wing articulation to conserve energy for endured and long range flights. Elucidating the underlying physiology of such capability could potentially address numerous challenging problems in flight engineering. This study investigates the aerodynamic characteristics of an insect species called desert locust (Schistocerca gregaria) with an extraordinary gliding skills at low Reynolds number. Here, locust tandem wings are subjected to a computational fluid dynamics (CFD) simulation using 2D and 3D Navier-Stokes equations revealing fore-hindwing interactions, and the influence of their corrugations on the aerodynamic performance. Furthermore, the obtained CFD results are mathematically parameterized using PARSEC method and optimized based on a novel fusion of Genetic Algorithms and Nash game theory to achieve Nash equilibrium being the optimized wings. It was concluded that the lift-drag (gliding) ratio of the optimized profiles were improved by at least 77% and 150% compared to the original wing and the published literature, respectively. Ultimately, the profiles are integrated and analyzed using 3D CFD simulations that demonstrated a 14% performance improvement validating the proposed wing models for further fabrication and rapid prototyping presented in the future study

Coping With Multiple Visual Motion Cues Under Extremely Constrained Computation Power of Micro Autonomous Robots

The perception of different visual motion cues is crucial for autonomous mobile robots to react to or interact with the dynamic visual world. It is still a great challenge for a micro mobile robot to cope with dynamic environments due to the restricted computational resources and the limited functionalities of its visual systems. In this study, we propose a compound visual neural system to automatically extract and fuse different visual motion cues in real-time using the extremely constrained computation power of micro mobile robots. The proposed visual system contains multiple bio-inspired visual motion perceptive neurons each with a unique role, for example to extract collision visual cues, darker collision cue and directional motion cues. In the embedded system, these multiple visual neurons share a similar presynaptic network to minimise the consumption of computation resources. In the postsynaptic part of the system, visual cues pass results to corresponding action neurons using lateral inhibition mechanism. The translational motion cues, which are identified by comparing pairs of directional cues, are given the highest priority, followed by the darker colliding cues and approaching cues. Systematic experiments with both virtual visual stimuli and real-world scenarios have been carried out to validate the system's functionality and reliability. The proposed methods have demonstrated that (1) with extremely limited computation power, it is still possible for a micro mobile robot to extract multiple visual motion cues robustly in a complex dynamic environment; (2) the cues extracted can be fused with a lateral inhibited postsynaptic network, thus enabling the micro robots to respond effectively with different actions, accordingly to different states, in real-time. The proposed embedded visual system has been modularised and can be easily implemented in other autonomous mobile platforms for real-time applications. The system could also be used by neurophysiologists to test new hypotheses pertaining to biological visual neural systems

Design of the offline test electronics for the nozzle system of proton therapy

A set of nozzle equipment for proton therapy is now being developed at China Institute of Atomic Energy. To facilitate the off-line commissioning of the whole equipment, a set of ionization chamber signal generation system, the test electronics, is designed. The system uses ZYNQ SoC as the main control unit and outputs the beam dose analog signal through DAC8532. The dual SPDT analog switch, DG636, is used to simulate the beam position signals according to Gaussian distribution. The results show that the system can simulate the beam position, dose, and other related analog signals generated by the proton beam when passing through the ionization chamber. Moreover, the accuracy of the simulated beam position is within +/-0.33mm, and the accuracy of the simulated dose signal is within +/-1%. At the same time, it can output analog signals representing environmental parameters. The test electronics meets the design requirements, which can be used to commission the nozzle system as well as the treatment control system without the proton beam

Bio-Inspired Embedded Vision System for Autonomous Micro-Robots: The LGMD Case

In this paper, we present a new bio-inspired vision system embedded for micro-robots. The vision system takes inspiration from locusts in detecting fast approaching objects. Neurophysiological research suggested that locusts use a wide-field visual neuron called lobula giant movement detector (LGMD) to respond to imminent collisions. In this paper, we present the implementation of the selected neuron model by a low-cost ARM processor as part of a composite vision module. As the first embedded LGMD vision module fits to a micro-robot, the developed system performs all image acquisition and processing independently. The vision module is placed on top of a micro-robot to initiate obstacle avoidance behavior autonomously. Both simulation and real-world experiments were carried out to test the reliability and robustness of the vision system. The results of the experiments with different scenarios demonstrated the potential of the bio-inspired vision system as a low-cost embedded module for autonomous robots