Multi-vehicle planning using RRT-connect

The problem of planning multiple vehicles deals with the design of an effective algorithm that can cause multiple autonomous vehicles on the road to communicate and generate a collaborative optimal travel plan. Our modelling of the problem considers vehicles to vary greatly in terms of both size and speed, which makes it suboptimal to have a faster vehicle follow a slower vehicle or for vehicles to drive with predefined speed lanes. It is essential to have a fast planning algorithm whilst still being probabilistically complete. The Rapidly Exploring Random Trees (RRT) algorithm developed and reported on here uses a problem specific coordination axis, a local optimization algorithm, priority based coordination, and a module for deciding travel speeds. Vehicles are assumed to remain in their current relative position laterally on the road unless otherwise instructed. Experimental results presented here show regular driving behaviours, namely vehicle following, overtaking, and complex obstacle avoidance. The ability to showcase complex behaviours in the absence of speed lanes is characteristic of the solution developed.

Experimental analysis of the Reynolds flocking model

The classic Reynolds flocking model is formally analysed, with results presented and discussed. Flocking behaviour was investigated through the development of two measurements of flocking, flock area and polarisation, with a view to applying the findings to robotic applications. Experiments varying the flocking simulation parameters individually and simultaneously provide new insight into the control of flock behaviour.

Software-based study of a non-sorting method for median calculation on a set of integer numbers

This paper presents a software-based study of a hardware-based non-sorting median calculation method on a set of integer numbers. The method divides the binary representation of each integer element in the set into bit slices in order to find the element located in the middle position. The method exhibits a linear complexity order and our analysis shows that the best performance in execution time is obtained when slices of 4-bit in size are used for 8-bit and 16-bit integers, in mostly any data set size. Results suggest that software implementation of bit slice method for median calculation outperforms sorting-based methods with increasing improvement for larger data set size. For data set sizes of N > 5, our simulations show an improvement of at least 40%.

Estimation of magnet separation for magnetic suspension applications

This thesis describes a form of non-contact measurement using two dimensional hall effect sensing to resolve the location of a moving magnet which is part of a ‘magnetic spring’ type suspension system. This work was inspired by the field of Space Robotics, which currently relies on solid link suspension techniques for rover stability. This thesis details the design, development and testing of a novel magnetic suspension system with a possible application in space and terrestrial based robotics, especially when the robot needs to traverse rough terrain. A number of algorithms were developed, to utilize experimental data from testing, that can approximate the separation between magnets in the suspension module through observation of the magnetic fields. Experimental hardware was also developed to demonstrate how two dimensional hall effect sensor arrays could provide accurate feedback, with respects to the magnetic suspension modules operation, so that future work can include the sensor array in a real-time control system to produce dynamic ride control for space robots. The research performed has proven that two dimensional hall effect sensing with respects to magnetic suspension is accurate, effective and suitable for future testing.

Proposal of a floating mechanism based on magnetic levitation method toward EEG-driven robotic system

We discussed a floating mechanism based on quasi-magnetic levitation method that can be attached at the endpoint of a robot arm in order to construct a novel redundant robot arm for producing compliant motions. The floating mechanism can be composed of magnets and a constraint mechanism such that the repelling force of the magnets floats the endpoint part of the mechanism stable for the guided motions. The analytical and experimental results show that the proposed floating mechanism can produce stable floating motions with small inertia and viscosity. The results also show that the proposed mechanism can detect small force applied to the endpoint part because the friction force of the mechanism is very small.

Zombie mouse in a Chinese Room

John Searle’s Chinese Room Argument (CRA) purports to demonstrate that syntax is not sufficient for semantics, and, hence, because computation cannot yield understanding, the computational theory of mind, which equates the mind to an information processing system based on formal computations, fails. In this paper, we use the CRA, and the debate that emerged from it, to develop a philosophical critique of recent advances in robotics and neuroscience. We describe results from a body of work that contributes to blurring the divide between biological and artificial systems; so-called animats, autonomous robots that are controlled by biological neural tissue and what may be described as remote-controlled rodents, living animals endowed with augmented abilities provided by external controllers. We argue that, even though at first sight, these chimeric systems may seem to escape the CRA, on closer analysis, they do not. We conclude by discussing the role of the body–brain dynamics in the processes that give rise to genuine understanding of the world, in line with recent proposals from enactive cognitive science.

Development of a wearable assistive soft robotic device for elbow rehabilitation

The loss of motor function at the elbow joint can result as a consequence of stroke. Stroke is a clinical illness resulting in long lasting neurological deficits often affecting somatosensory and motor cortices. More than half of those that recover from a stroke survive with disability in their upper arm and need rehabilitation therapy to help in regaining functions of daily living. In this paper, we demonstrated a prototype of a low-cost, ultra-light and wearable soft robotic assistive device that could aid administration of elbow motion therapies to stroke patients. In order to assist the rotation of the elbow joint, the soft modules which consist of soft wedge-like cellular units was inflated by air to produce torque at the elbow joint. Highly compliant rotation can be naturally realised by the elastic property of soft silicone and pneumatic control of air. Based on the direct visual-actuation control, a higher control loop utilised visual processing to apply positional control, the lower control loop was implemented by an electronic circuit to achieve the desired pressure of the soft modules by Pulse Width Modulation. To examine the functionality of the proposed soft modular system, we used an anatomical model of the upper limb and performed the experiments with healthy participants.

Feasibility study on EEG driven robotic system to realize efficient stroke rehabilitation

We aim to develop an efficient robotic system for stroke rehabilitation, in which a robotic arm moves the hemiplegic upper limb when the patient tries to move it. In order to achieve this goal we have considered a method to detect the patient's intended motion using EEG (Electroencephalogram), and have designed a rehabilitation robot based on a Redundant Drive Method. In this paper, we propose an EEG driven rehabilitation robot system and present initial results evaluating the feasibility of the proposed system.

Analysis and modeling of Diacamma workers’ behavior

It is well-known that social insects such as ants show interesting collective behaviors. How do they organize such behaviors? To expand understanding of collective behaviors of social insects, we focused on ants, Diacamma, and analyzed the behavior of a few individuals. In an experimental set-up, ants are placed in hemisphere without a nest and food and the trajectory of ants is recorded. From this bottom-up approach, we found following characteristics: 1. Activity of individuals increases and decreases periodically. 2. Spontaneous meeting process is observed between two ants and meeting spot of two ants is localized in the experimental field.

Science drives horticulture's progress and profit

Horticultural science linked with basic studies in biology, chemistry, physics and engineering has laid the foundation for advances in applied knowledge which are at the heart of commercial, environmental and social horticulture. In few disciplines is science more rapidly translated into applicable technologies than in the huge range of man’s activities embraced within horticulture which are discussed in this Trilogy. This chapter surveys the origins of horticultural science developing as an integral part of the 16th century “Scientific Revolution”. It identifies early discoveries during the latter part of the 19th and early 20th centuries which rationalized the control of plant growth, flowering and fruiting and the media in which crops could be cultivated. The products of these discoveries formed the basis on which huge current industries of worldwide significance are founded in fruit, vegetable and ornamental production. More recent examples of the application of horticultural science are used in an explanation of how the integration of plant breeding, crop selection and astute marketing highlighted by the New Zealand industry have retained and expanded the viability of production which supplies huge volumes of fruit into the world’s markets. This is followed by an examination of science applied to tissue and cell culture as an example of technologies which have already produced massive industrial applications but hold the prospect for generating even greater advances in the future. Finally, examples are given of nascent scientific discoveries which hold the prospect for generating horticultural industries with considerable future impact. These include systems modeling and biology, nanotechnology, robotics, automation and electronics, genetics and plant breeding, and more efficient and effective use of resources and the employment of benign microbes. In conclusion there is an estimation of the value of horticultural science to society.

A Floating-point Extended Kalman Filter Implementation for Autonomous Mobile Robots

Localization and Mapping are two of the most important capabilities for autonomous mobile robots and have been receiving considerable attention from the scientific computing community over the last 10 years. One of the most efficient methods to address these problems is based on the use of the Extended Kalman Filter (EKF). The EKF simultaneously estimates a model of the environment (map) and the position of the robot based on odometric and exteroceptive sensor information. As this algorithm demands a considerable amount of computation, it is usually executed on high end PCs coupled to the robot. In this work we present an FPGA-based architecture for the EKF algorithm that is capable of processing two-dimensional maps containing up to 1.8 k features at real time (14 Hz), a three-fold improvement over a Pentium M 1.6 GHz, and a 13-fold improvement over an ARM920T 200 MHz. The proposed architecture also consumes only 1.3% of the Pentium and 12.3% of the ARM energy per feature.

A Parallel Hardware Architecture for Scale and Rotation Invariant Feature Detection

This paper proposes a parallel hardware architecture for image feature detection based on the Scale Invariant Feature Transform algorithm and applied to the Simultaneous Localization And Mapping problem. The work also proposes specific hardware optimizations considered fundamental to embed such a robotic control system on-a-chip. The proposed architecture is completely stand-alone; it reads the input data directly from a CMOS image sensor and provides the results via a field-programmable gate array coupled to an embedded processor. The results may either be used directly in an on-chip application or accessed through an Ethernet connection. The system is able to detect features up to 30 frames per second (320 x 240 pixels) and has accuracy similar to a PC-based implementation. The achieved system performance is at least one order of magnitude better than a PC-based solution, a result achieved by investigating the impact of several hardware-orientated optimizations oil performance, area and accuracy.

Semantic mapping using mobile robots

Robotic mapping is the process of automatically constructing an environment representation using mobile robots. We address the problem of semantic mapping, which consists of using mobile robots to create maps that represent not only metric occupancy but also other properties of the environment. Specifically, we develop techniques to build maps that represent activity and navigability of the environment. Our approach to semantic mapping is to combine machine learning techniques with standard mapping algorithms. Supervised learning methods are used to automatically associate properties of space to the desired classification patterns. We present two methods, the first based on hidden Markov models and the second on support vector machines. Both approaches have been tested and experimentally validated in two problem domains: terrain mapping and activity-based mapping.

A Road Following Approach Using Artificial Neural Networks Combinations

Navigation is a broad topic that has been receiving considerable attention from the mobile robotic community over the years. In order to execute autonomous driving in outdoor urban environments it is necessary to identify parts of the terrain that can be traversed and parts that should be avoided. This paper describes an analyses of terrain identification based on different visual information using a MLP artificial neural network and combining responses of many classifiers. Experimental tests using a vehicle and a video camera have been conducted in real scenarios to evaluate the proposed approach.

Cross-situational learning of object-word mapping using Neural Modeling Fields

The issue of how children learn the meaning of words is fundamental to developmental psychology. The recent attempts to develop or evolve efficient communication protocols among interacting robots or Virtual agents have brought that issue to a central place in more applied research fields, such as computational linguistics and neural networks, as well. An attractive approach to learning an object-word mapping is the so-called cross-situational learning. This learning scenario is based on the intuitive notion that a learner can determine the meaning of a word by finding something in common across all observed uses of that word. Here we show how the deterministic Neural Modeling Fields (NMF) categorization mechanism can be used by the learner as an efficient algorithm to infer the correct object-word mapping. To achieve that we first reduce the original on-line learning problem to a batch learning problem where the inputs to the NMF mechanism are all possible object-word associations that Could be inferred from the cross-situational learning scenario. Since many of those associations are incorrect, they are considered as clutter or noise and discarded automatically by a clutter detector model included in our NMF implementation. With these two key ingredients - batch learning and clutter detection - the NMF mechanism was capable to infer perfectly the correct object-word mapping. (C) 2009 Elsevier Ltd. All rights reserved.