Learning the dynamics of the external world: Brain inspired learning for robotic applications

Humans have exceptional abilities to learn new skills, manipulate tools and objects, and interact with our environment. In order to be successful at these tasks, our brain has become exceptionally well adapted to learning to deal not only with the complex dynamics of our own limbs but also with novel dynamics in the external world. While learning of these dynamics includes learning the complex time-varying forces at the end of limbs through the updating of internal models, it must also include learning the appropriate mechanical impedance in order to stabilize both the limb and any objects contacted in the environment. This article reviews the field of human learning by examining recent experimental evidence about adaptation to novel unstable dynamics and explores how this knowledge about the brain and neuro-muscular system can expand the learning capabilities of robotics and prosthetics. © 2006.

Monte Carlo optimization strategies for air-traffic control

The safety of the flights, and in particular conflict resolution for separation assurance, is one of the main tasks of Air Traffic Control. Conflict resolution requires decision making in the face of the considerable levels of uncertainty inherent in the motion of aircraft. We present a Monte Carlo framework for conflict resolution which allows one to take into account such levels of uncertainty through the use of a stochastic simulator. A simulation example inspired by current air traffic control practice illustrates the proposed conflict resolution strategy. Copyright © 2005 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.

Biologically-inspired object recognition system with features from complex wavelets

In this paper, a novel cortex-inspired feed-forward hierarchical object recognition system based on complex wavelets is proposed and tested. Complex wavelets contain three key properties for object representation: shift invariance, which enables the extraction of stable local features; good directional selectivity, which simplifies the determination of image orientations; and limited redundancy, which allows for efficient signal analysis using the multi-resolution decomposition offered by complex wavelets. In this paper, we propose a complete cortex-inspired object recognition system based on complex wavelets. We find that the implementation of the HMAX model for object recognition in [1, 2] is rather over-complete and includes too much redundant information and processing. We have optimized the structure of the model to make it more efficient. Specifically, we have used the Caltech 5 standard dataset to compare with Serre's model in [2] (which employs Gabor filter bands). Results demonstrate that the complex wavelet model achieves a speed improvement of about 4 times over the Serre model and gives comparable recognition performance. © 2011 IEEE.

Nature-inspired microfluidic propulsion using magnetic actuation.

In this work we mimic the efficient propulsion mechanism of natural cilia by magnetically actuating thin films in a cyclic but non-reciprocating manner. By simultaneously solving the elastodynamic, magnetostatic, and fluid mechanics equations, we show that the amount of fluid propelled is proportional to the area swept by the cilia. By using the intricate interplay between film magnetization and applied field we are able to generate a pronounced asymmetry and associated flow. We delineate the functional response of the system in terms of three dimensionless parameters that capture the relative contribution of elastic, inertial, viscous, and magnetic forces.

Adhesive properties of gecko-inspired mimetic via micropatterned carbon nanotube forests

The adhesive properties of the gecko foot have inspired designs of advanced micropatterned surfaces with increased contact areas. We have fabricated micropatterned pillars of vertically aligned carbon nanotube forests with a range of pillar diameters, heights, and spacings (or pitch). We used nanoindentation to measure their elastic and orthogonal adhesion properties and derive their scaling behavior. The patterning of nanotube forests into pillar arrays allows a reduction of the effective modulus from 10 to 15 MPa to 0.1-1 MPa which is useful for developing maximum conformal adhesion. © 2012 American Chemical Society.

A neuro-inspired method for data rate limited feedback control

The nervous system implements a networked control system in which the plants take the form of limbs, the controller is the brain, and neurons form the communication channels. Unlike standard networked control architectures, there is no periodic sampling, and the fundamental units of communication contain little numerical information. This paper describes a novel communication channel, modeled after spiking neurons, in which the transmitter integrates an input signal and sends out a spike when the integral reaches a threshold value. The reciever then filters the sequence of spikes to approximately reconstruct the input signal. It is shown that for appropriate choices of channel parameters, stable feedback control over these spiking channels is possible. Furthermore, good tracking performance can be achieved. The data rate of the channel increases linearly with the size of the inputs. Thus, when placed in a feedback loop, small loop gains imply a low data rate. ©2010 IEEE.

A numerical study into the effects of Bio-synthetic Paraffinic Kerosine blends with Jet-A fuel for civil aircraft engine

Growing concerns regarding fluctuating fuel costs and pollution targets for gas emissions, have led the aviation industry to seek alternative technologies to reduce its dependency on crude oil, and its net emissions. Recently blends of bio-fuel with kerosine, have become an alternative solution as they offer "greener" aircraft and reduce demand on crude oil. Interestingly, this technique is able to be implemented in current aircraft as it does not require any modification to the engine. Therefore, the present study investigates the effect of blends of bio-synthetic paraffinic kerosine with Jet-A in a civil aircraft engine, focusing on its performance and exhaust emissions. Two bio-fuels are considered: Jatropha Bio-synthetic Paraffinic Kerosine (JSPK) and Camelina Bio-synthetic Paraffinic Kerosine (CSPK); there are evaluated as pure fuels, and as 10% and 50% blend with Jet-A. Results obtained show improvement in thrust, fuel flow and SFC as composition of bio-fuel in the blend increases. At design point condition, results on engine emissions show reduction in NO x, and CO, but increases of CO is observed at fixed fuel condition, as the composition of bio-fuel in the mixture increases. Copyright © 2012 by ASME.

A dragline-forming mobile robot inspired by spiders.

Mobility of wheeled or legged machines can be significantly increased if they are able to move from a solid surface into a three-dimensional space. Although that may be achieved by addition of flying mechanisms, the payload fraction will be the limiting factor in such hybrid mobile machines for many applications. Inspired by spiders producing draglines to assist locomotion, the paper proposes an alternative mobile technology where a robot achieves locomotion from a solid surface into a free space. The technology resembles the dragline production pathway in spiders to a technically feasible degree and enables robots to move with thermoplastic spinning of draglines. As an implementation, a mobile robot has been prototyped with thermoplastic adhesives as source material of the draglines. Experimental results show that a dragline diameter range of 1.17-5.27 mm was achievable by the 185 g mobile robot in descending locomotion from the solid surface of a hanging structure with a power consumption of 4.8 W and an average speed of 5.13 cm min(-1). With an open-loop controller consisting of sequences of discrete events, the robot has demonstrated repeatable dragline formation with a relative deviation within -4% and a length close to the metre scale.