Robust terminal attitude navigation for autonomous vehicles

Here we define the terminal attitude of the pursuer with respect to a target and present a LQR and H¿ control approach to solving the problem of pursuer achieving a desired terminal attack/approach angle. The intercept or engagement criteria is defined in terms of both minimizing the miss distance and controlling the pursuer's body attitude with respect to the target at the terminal point. This approach in comparison to previous approaches consider the relativistic approach of the pursuer with respect to the target as opposed the absolute velocities of the two dynamic bodies, and have possible applications ranging from autonomous vehicle entry in to a mother craft to nossle engagements in on-flight refuelling or even in precision missile guidance. Here we also suitably formulate the H¿ control ideas directly applicable to the underlying problem and presents both state feedback and output feed back results for the case of finite horizon and non-zero initial conditions together with a optimal parameter value to achieve a desired terminal characteristic in terms of the original weighting parameters.

Distributed protocol for communications among underwater vehicles

Underwater surveying by swarms of autonomous underwater vehicles presents problems in communication among the robots. These problems involve the bandwidth, power consumption, timing, processing power, and other issues. This paper presents a novel approach to communicate and coordinate effectively among underwater vehicles to accomplish this task successfully. The proposed approach solves issues by reducing the number of hops to conserve power, while reducing computation time and bandwidth, effectively utilizing resources to reduce the load on each node. Finally, the simulation results are presented, in order to prove that the proposed approach improves efficiency and effectiveness in communicating among underwater vehicles.

Visual tracking of vehicles using multiresolution analysis and neural network

This paper describes the procedure for detection and tracking of a vehicle from an on-road image sequence taken by a monocular video capturing device in real time. The main objective of such a visual tracking system is to closely follow objects in each frame of a video stream, such that the object position as well as other geometric information are always known. In the tracking system described, the video capturing device is also moving. It is a challenge to detect and track a moving vehicle under a constantly changing environment coupled to real time video processing. The system suggested is robust to implement under different illuminating conditions by using the monocular video capturing device. The vehicle tracking algorithm is one of the most important modules in an autonomous vehicle system, not only it should be very accurate but also must have the safety of other vehicles, pedestrians, and the moving vehicle itself. In order to achieve this an algorithm of multi resolution technique based on Haar basis functions were used for the wavelet transform, where a combination of classification was carried out with the multilayer feed forward neural network. The classification is done in a reduced dimensional space, where principle component analysis (PCA) dimensional reduction technique has been applied to make the classification process much more efficient. The results show the effectiveness of the proposed methodology.

The mundiad : sic transit gloria mundi

The Mundiad is a mock epic poem in heroic couplets. Modelled on structures of classical epics such as Homer's Iliad, Virgil's Aeneid and Milton's Paradise Lost, yet set in the contemporary world of globalisation, the poem celebrates the detritus of everyday life Kylie Minogue, pornography, new ageism, genetic engineering, IVF, screen culture, among many others. Reviving the ancient poetic ambition to speak differently about the things of this world, The Mundiad is startlingly original and is destined to be a cult classic.

The emergence of battery electric vehicles : A NZ manufacturing opportunity?

Personal passenger transport faces several challenges in the coming decades: depletion of cheap oil reserves, increasing congestion, localised pollution, the need for reduced carbon emissions and the long term goal of sustainability. One way of solving some of these problems could be to introduce comfortable, energy efficient, battery electric vehicles.

Currently, hybrid vehicles have been presented as a means to reducing the transportation related oil demand. New developments in materials and technologies have made them, cleaner and safer as well as more fuel efficient. However, hybrids will only prolong the use of oil until alternatively fuelled vehicles are developed.

One long term alternative is the battery electric vehicle (BEV). A BEV designed to be light, aerodynamic with high efficiency drive train and latest battery technology would have a performance comparable to a typical internal combustion engine vehicle (ICEV). Recent developments in virtual engineering, rapid prototyping and advanced manufacturing might enable low-cost development of niche market BEV’s designed and built in New Zealand for export markets.

This work examines the collaborative development of a twin seat BEV using new materials and latest technologies by the University of Waikato’s Engineering Department and a group of NZ and foreign companies. The car will be used to research the potential of BEVs and will also compete in the Commuter Class of the World Solar Challenge in 2007.

Detection of vehicles with monolithic classifier vis-à-vis a boosted cascaded classifier

This paper describes the comparison of accuracy and performance of two machine learning approaches for visual object detection and tracking vehicles, from an on-road image sequence. The first is a neural network based approach. Where an algorithm of multi resolution technique based on Haar basis functions was used to obtain an image with different scales. Thereafter a classification was carried out with the multilayer feed forward neural network. Principle Component Analysis (PCA) technique was used as a dimension reduction technique to make the classification process much more efficient. The second approach is based on boosting which also yields very good detection rates. In general, boosting is one of the most important developments in classification methodology. It works by sequentially applying a classification algorithm to reweighed versions of the training data, followed by taking a weighted majority vote of the sequence of classifiers thus produced. For this work, a strong classifier was trained by the adaboost algorithm. The results of comparing the two methodologies visà-vis shows the effectiveness of the methods that have been used.

Intelligent energy management in hybrid electric vehicles

The modelling and simulation approach is employed to develop an intelligent energy management system for hybrid electric vehicles. The aim is to optimize fuel consumption and reduce emissions. An analysis of the role of drivetrain, energy management control strategy and the associated impacts on the fuel consumption with combined wind/drag, slope, rolling, and accessories loads are included.

A study on plug-in hybrid electic vehicles

Plug-in hybrid electric vehicle (PHEV), which is a hybrid vehicle whose batteries can be recharged by plugging into an electric power source, is creating many interests due to its significant potential to improve fuel efficiency and reduce pollution. PHEVs would be the next generation of vehicles that are expected to replace conventional hybrid electric vehicles. This paper presents a study on PHEV. It gives a review of different drivetrain architectures associated with PHEVs. In addition, different control strategies that could bring about realization of advantages of PHEV capabilities are discussed and compared.

A study on look-ahead control and energy management strategies in hybrid electric vehicles

Fuel efficiency in a hybrid electric vehicle requires a fine balance between usage of combustion engine and battery power. Information about the geometry of the road and traffic ahead can have a great impact on optimized control and the power split between the main parts of a hybrid electric vehicle. This paper provides a survey on the existing methods of control and energy management emphasizing on those that consider the look-ahead road situation and trajectory information. Then it presents the future trends in the control and energy management of hybrid electric vehicles.