Optimization of Heterogeneous UAV Communications Using the Multiobjective Quadratic Assignment Problem

The Air Force has placed a high priority on developing new and innovative ways to use Unmanned Aerial Vehicles (UAVs). The Defense Advanced Research Projects Agency (DARPA) currently funds many projects that deal with the advancement of UAV research. The ultimate goal of the Air Force is to use UAVs in operations that are highly dangerous to pilots, mainly the suppression of enemy air defenses (SEAD). With this goal in mind, formation structuring of autonomous or semi-autonomous UAVs is of future importance. This particular research investigates the optimization of heterogeneous UAV multi-channel communications in formation. The problem maps to the multiobjective Quadratic Assignment Problem (mQAP). Optimization of this problem is done through the use of a Multiobjective Evolutionary Algorithm (MOEA) called the Multiobjective Messy Genetic Algorithm - II (MOMGA-II). Experimentation validates the attainment of an acceptable Pareto Front for a variety of mQAP benchmarks. It was observed that building block size can affect the location vectors along the current Pareto Front. The competitive templates used during testing perform best when they are randomized before each building block size evaluation. This tuning of the MOMGA-II parameters creates a more effective algorithm for the variety of mQAP benchmarks, when compared to the initial experiments. Thus this algorithmic approach would be useful for Air Force decision makers in determining the placement of UAVs in formations

Creating an Objective Methodology for Human-Robot Team Configuration Selection

As technology has been advancing and designers have been looking to future applications, it has become increasingly evident that robotic technology can be used to supplement, augment, and improve human performance of tasks. Team members can be combined in various combinations to better utilize their capabilities and skills to create more efficient and diversified operational teams. A primary obstacle to integrating new robotic technology has been the inability to quantitatively compare overall team performance between very different team configurations without limiting the analysis to a few metrics. To-date, mission designers have arbitrarily assigned importance to mission parameters, subjectively limiting the search space. While this has been effective at evaluating individual mission plans, the arbitrary evaluation criteria has made a straightforward comparison between different research projects and ranking scales impossible. The question then becomes how to select an objective set of criteria for any given problem. It is this final question that this research sought to answer. A methodology was developed to facilitate performance comparison amongst heterogeneous human and robot teams. This methodology makes no assumptions about mission priorities or preferences. Instead, it provides an objective, generic, quantitative method to reduce the complexity of the mission designer's decision space. It employs an heuristic, greedy objective reduction algorithm to reduce problem complexity and a multi-objective genetic algorithm to explore the design space. The human-robot team configuration selection problem was utilized as the application that motivated this research. The methodology, however, will be applicable to a wider domain of research. It will provide a structure to enable broader search of the design space, exploration of the differences between performance metrics, and comparison of optimization models that facilitate evaluation of the design options

Optimisation-based verification process of obstacle avoidance systems for unmanned vehicles

This thesis deals with safety verification analysis of collision avoidance systems for unmanned vehicles. The safety of the vehicle is dependent on collision avoidance algorithms and associated control laws, and it must be proven that the collision avoidance algorithms and controllers are functioning correctly in all nominal conditions, various failure conditions and in the presence of possible variations in the vehicle and operational environment. The current widely used exhaustive search based approaches are not suitable for safety analysis of autonomous vehicles due to the large number of possible variations and the complexity of algorithms and the systems. To address this topic, a new optimisation-based verification method is developed to verify the safety of collision avoidance systems. The proposed verification method formulates the worst case analysis problem arising the verification of collision avoidance systems into an optimisation problem and employs optimisation algorithms to automatically search the worst cases. Minimum distance to the obstacle during the collision avoidance manoeuvre is defined as the objective function of the optimisation problem, and realistic simulation consisting of the detailed vehicle dynamics, the operational environment, the collision avoidance algorithm and low level control laws is embedded in the optimisation process. This enables the verification process to take into account the parameters variations in the vehicle, the change of the environment, the uncertainties in sensors, and in particular the mismatching between model used for developing the collision avoidance algorithms and the real vehicle. It is shown that the resultant simulation based optimisation problem is non-convex and there might be many local optima. To illustrate and investigate the proposed optimisation based verification process, the potential field method and decision making collision avoidance method are chosen as an obstacle avoidance candidate technique for verification study. Five benchmark case studies are investigated in this thesis: static obstacle avoidance system of a simple unicycle robot, moving obstacle avoidance system for a Pioneer 3DX robot, and a 6 Degrees of Freedom fixed wing Unmanned Aerial Vehicle with static and moving collision avoidance algorithms. It is proven that although a local optimisation method for nonlinear optimisation is quite efficient, it is not able to find the most dangerous situation. Results in this thesis show that, among all the global optimisation methods that have been investigated, the DIviding RECTangle method provides most promising performance for verification of collision avoidance functions in terms of guaranteed capability in searching worst scenarios

Hybrid Formation Control of Unmanned Helicopters

Ph.DDOCTOR OF PHILOSOPH

Climbing and Walking Robots

Nowadays robotics is one of the most dynamic fields of scientific researches. The shift of robotics researches from manufacturing to services applications is clear. During the last decades interest in studying climbing and walking robots has been increased. This increasing interest has been in many areas that most important ones of them are: mechanics, electronics, medical engineering, cybernetics, controls, and computers. Today’s climbing and walking robots are a combination of manipulative, perceptive, communicative, and cognitive abilities and they are capable of performing many tasks in industrial and non- industrial environments. Surveillance, planetary exploration, emergence rescue operations, reconnaissance, petrochemical applications, construction, entertainment, personal services, intervention in severe environments, transportation, medical and etc are some applications from a very diverse application fields of climbing and walking robots. By great progress in this area of robotics it is anticipated that next generation climbing and walking robots will enhance lives and will change the way the human works, thinks and makes decisions. This book presents the state of the art achievments, recent developments, applications and future challenges of climbing and walking robots. These are presented in 24 chapters by authors throughtot the world The book serves as a reference especially for the researchers who are interested in mobile robots. It also is useful for industrial engineers and graduate students in advanced study

ASEAN’S DEFENCE DIPLOMACY: THE ROAD TO SOUTHEAST ASIAN DEFENCE COMMUNITY?

The purpose of this study is to examine whether the practice of ASEAN member states’ defence diplomacy through practical defence cooperatives for handling security challenges in Southeast Asia contains the characteristics of a defence community which could pave the way for the formation of a future Southeast Asian defence community. The security challenges facing the countries of Southeast Asia today lie in creating a stable environment. The debate over the concept of security has broadened the discourse on regional security cooperation, which drives states to engage more deeply in multilateral diplomacy in order to defend better and promote their national interests, and particularly in handling problems in maintaining a stable peace in the region. As the unit of analysis, this study examined the forum of the ASEAN Defence Ministers’ Meeting (ADMM) and ASEAN Defence Ministers’ Meeting Plus (ADMM-Plus), and selects six ASEAN member states as the loci of the study, namely: Brunei, Indonesia, Malaysia, Singapore; Thailand and Vietnam. The different forms of government system became the reasons for selecting these six countries, i.e. a democratic state such as Indonesia, a semi-authoritarian system such as in Malaysia and Singapore, a monarchy as in Brunei and Thailand, socialist countries such as Vietnam. However, only four out of the six chosen countries were founding members of ASEAN. This study proposes two central questions. The first question focuses on the development of forms of threats found in Southeast Asia today, which encourages ASEAN member states to conduct defence diplomacy, while the second question focuses on whether the implementation of ASEAN defence diplomacy helps to shape an ASEAN defence community. In answering those questions, this study is applying qualitative methodology to address an explanatory aspect of these questions, and using the theory of regionalism through presenting empirical evidence of ASEAN member states practical defence diplomacy. It seeks to show how ASEAN member states develop their military role in handling new emerging non-traditional security threats contain the characteristics of a defence community which could pave the way for this regional organisation to turn into a collective defence grouping

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC