Minimizing vehicular traffic via optimized land use development for a sustainable and equitable future

Mixed-use developments and transit-oriented developments are becoming very common in urban areas in an effort to reduce sprawl. Numerous studies have shown that such programs would not be successful unless the mix of land uses and sizes is well-balanced and integrated with the surrounding neighborhood. Developers often ignore this aspect in favor of immediate financial gain and do not realize that there are sustainable financial benefits in land use optimization. In addition, professionals often work with limited logistics, resources, and technical knowledge and therefore struggle in setting goals and suggesting land uses that have less auto dependency based on travel demand characteristics. The current traffic impact assessment methodology (part of the environmental review process for approval of a project) is one-dimensional. It does not consider land use optimization based on the surrounding neighborhood characteristics that have a significant effect in reducing vehicular traffic. These surrounding neighborhood characteristics are often grouped into categories reflecting the “D’s of development”—Density, Diversity, Design, and Distance to transit—and would have significant benefits in minimizing auto dependence. The objectives of this research are to first develop a methodology to optimize the mix of land uses and sizes to minimize the number vehicular trips and maximize the person trips using a case study of a mixed-use development. This will help to further understand the travel demand and parking behavior. Secondly, this research will use the travel demand characteristics from other approved mixed-use developments from various boroughs of New York City with diverse neighborhood characteristics to validate the land use optimization methodology. The third and ultimate objective of this research is to develop a model that is practical and implementable on a regional level to optimize the mix of land uses and sizes based on localized travel behavior patterns and neighborhood characteristics to minimize vehicle trips. In this study, a genetic algorithm has been developed and tested on one development to demonstrate its application (objective 1) and is subsequently applied to additional developments (objective 2). A stepwise regression analysis is then performed to develop equations for the optimal number of vehicle trips as well as the percent split of individual land use types within a development, all based on the surrounding neighborhood characteristics (objective 3). The research results in a series of equations that can be used to optimize a development’s mix of land uses and sizes by minimizing vehicular trips and maximizing person trips. Although the equations vary from city to city, the methodology is adaptable enough such that planning agencies can generate their own equations for their own region and engineers can then use them to forecast trip

Analysis and simulation of emergent architectures for internet of things

The Internet of Things (IoT) promises a plethora of new services and applications supported by a wide range of devices that includes sensors and actuators. To reach its potential IoT must break down the silos that limit applications' interoperability and hinder their manageability. These silos' result from existing deployment techniques where each vendor set up its own infrastructure, duplicating the hardware and increasing the costs. Fog Computing can serve as the underlying platform to support IoT applications thus avoiding the silos'. Each application becomes a system formed by IoT devices (i.e. sensors, actuators), an edge infrastructure (i.e. Fog Computing) and the Cloud. In order to improve several aspects of human lives, different systems can interact to correlate data obtaining functionalities not achievable by any of the systems in isolation. Then, we can analyze the IoT as a whole system rather than a conjunction of isolated systems. Doing so leads to the building of Ultra-Large Scale Systems (ULSS), an extension of the concept of Systems of Systems (SoS), in several verticals including Autonomous Vehicles, Smart Cities, and Smart Grids. The scope of ULSS is large in the number of things and complex in the variety of applications, volume of data, and diversity of communication patterns. To handle this scale and complexity in this thesis we propose Hierarchical Emergent Behaviors (HEB), a paradigm that builds on the concepts of emergent behavior and hierarchical organization. Rather than explicitly program all possible situations in the vast space of ULSS scenarios, HEB relies on emergent behaviors induced by local rules that define the interactions of the "things" between themselves and also with their environment. We discuss the modifications to classical IoT architectures required by HEB, as well as the new challenges. Once these challenges such as scalability and manageability are addressed, we can illustrate HEB's usefulness dealing with an IoT-based ULSS through a case study based on Autonomous Vehicles (AVs). To this end we design and analyze well-though simulations that demonstrate its tremendous potential since small modifications to the basic set of rules induce different and interesting behaviors. Then we design a set of primitives to perform basic maneuver such as exiting a platoon formation and maneuvering in anticipation of obstacles beyond the range of on-board sensors. These simulations also evaluate the impact of a HEB deployment assisted by Fog nodes to enlarge the informational scope of vehicles. To conclude we develop a design methodology to build, evaluate, and run HEB-based solutions for AVs. We provide architectural foundations for the second level and its implications in major areas such as communications. These foundations are then validated through simulations that incorporate new rules, obtaining valuable experimental observations. The proposed architecture has a tremendous potential to solve the scalability issue found in ULSS, enabling IoT deployments to reach its true potential.El Internet de las Cosas (IoT) promete una plétora de nuevos servicios y aplicaciones habilitadas por una amplia gama de dispositivos que incluye sensores y actuadores. Para alcanzar su potencial, IoT debe superar los silos que limitan la interoperabilidad de las aplicaciones y dificultan su administración. Estos silos son el resultado de las técnicas de implementación existentes en las que cada proveedor instala su propia infraestructura y duplica el hardware, incrementando los costes. Fog Computing puede servir como la plataforma subyacente que soporte aplicaciones del IoT evitando así los silos. Cada aplicación se convierte en un sistema formado por dispositivos IoT (por ejemplo sensores y actuadores), una infraestructura (como Fog Computing) y la nube. Con el fin de mejorar varios aspectos de la vida humana, diferentes sistemas pueden interactuar para correlacionar datos obteniendo funcionalidades que no pueden lograrse por ninguno de los sistemas de forma aislada. Entonces, podemos analizar el IoT como un único sistema en lugar de una conjunción de sistemas aislados. Esta perspectiva conduce a la construcción de Ultra-Large Scale Systems (ULSS), una extensión del concepto de Systems of Systems (SoS), en varios verticales, incluidos los vehículos autónomos, Smart Cities y Smart Grids. El alcance de ULSS es vasto debido a la cantidad de dispositivos y complejo en la variedad de aplicaciones, volumen de datos y diversidad de patrones de comunicación. Para manejar esta escala y complejidad, en esta tesis proponemos Hierarchical Emergent Behaviors (HEB), un paradigma que se basa en los conceptos de comportamientos emergente y organización jerárquica. En lugar de programar explícitamente todas las situaciones posibles en el vasto espacio de escenarios presentes en los ULSS, HEB se basa en comportamientos emergentes inducidos por reglas locales que definen las interacciones de las "cosas" entre ellas y también con su entorno. Discutimos las modificaciones a las arquitecturas clásicas de IoT requeridas por HEB, así como los nuevos desafíos. Una vez que se abordan estos desafíos, como la escalabilidad y la capacidad de administración, podemos ilustrar la utilidad de HEB cuando se ocupa de un ULSS basado en IoT a través de un caso de estudio basado en Vehículos Autónomos (AV). Con este fin, diseñamos y analizamos simulaciones que demuestran su enorme potencial, ya que pequeñas modificaciones en el conjunto básico de reglas inducen comportamientos diferentes e interesantes. Luego, diseñamos un conjunto de primitivas para realizar una maniobra básica, como salir de un pelotón y maniobrar en anticipación de obstáculos más allá del alcance de los sensores de a bordo. Estas simulaciones también evalúan el impacto de una implementación de HEB asistida por nodos de Fog Computing para ampliar el alcance sensorial de los vehículos. Para concluir, desarrollamos una metodología de diseño para construir, evaluar y ejecutar soluciones basadas en HEB para AV. Brindamos fundamentos arquitectónicos para el segundo nivel de HEB y sus implicaciones en áreas importantes como las comunicaciones. Estas bases se validan a través de simulaciones que incorporan nuevas reglas, obteniendo valiosas observaciones experimentales. La arquitectura propuesta tiene un enorme potencial para resolver el problema de escalabilidad que presentan los ULSS, permitiendo que las implementaciones de IoT alcancen su verdadero potencial

Analysis and simulation of emergent architectures for internet of things

The Internet of Things (IoT) promises a plethora of new services and applications supported by a wide range of devices that includes sensors and actuators. To reach its potential IoT must break down the silos that limit applications' interoperability and hinder their manageability. These silos' result from existing deployment techniques where each vendor set up its own infrastructure, duplicating the hardware and increasing the costs. Fog Computing can serve as the underlying platform to support IoT applications thus avoiding the silos'. Each application becomes a system formed by IoT devices (i.e. sensors, actuators), an edge infrastructure (i.e. Fog Computing) and the Cloud. In order to improve several aspects of human lives, different systems can interact to correlate data obtaining functionalities not achievable by any of the systems in isolation. Then, we can analyze the IoT as a whole system rather than a conjunction of isolated systems. Doing so leads to the building of Ultra-Large Scale Systems (ULSS), an extension of the concept of Systems of Systems (SoS), in several verticals including Autonomous Vehicles, Smart Cities, and Smart Grids. The scope of ULSS is large in the number of things and complex in the variety of applications, volume of data, and diversity of communication patterns. To handle this scale and complexity in this thesis we propose Hierarchical Emergent Behaviors (HEB), a paradigm that builds on the concepts of emergent behavior and hierarchical organization. Rather than explicitly program all possible situations in the vast space of ULSS scenarios, HEB relies on emergent behaviors induced by local rules that define the interactions of the "things" between themselves and also with their environment. We discuss the modifications to classical IoT architectures required by HEB, as well as the new challenges. Once these challenges such as scalability and manageability are addressed, we can illustrate HEB's usefulness dealing with an IoT-based ULSS through a case study based on Autonomous Vehicles (AVs). To this end we design and analyze well-though simulations that demonstrate its tremendous potential since small modifications to the basic set of rules induce different and interesting behaviors. Then we design a set of primitives to perform basic maneuver such as exiting a platoon formation and maneuvering in anticipation of obstacles beyond the range of on-board sensors. These simulations also evaluate the impact of a HEB deployment assisted by Fog nodes to enlarge the informational scope of vehicles. To conclude we develop a design methodology to build, evaluate, and run HEB-based solutions for AVs. We provide architectural foundations for the second level and its implications in major areas such as communications. These foundations are then validated through simulations that incorporate new rules, obtaining valuable experimental observations. The proposed architecture has a tremendous potential to solve the scalability issue found in ULSS, enabling IoT deployments to reach its true potential.El Internet de las Cosas (IoT) promete una plétora de nuevos servicios y aplicaciones habilitadas por una amplia gama de dispositivos que incluye sensores y actuadores. Para alcanzar su potencial, IoT debe superar los silos que limitan la interoperabilidad de las aplicaciones y dificultan su administración. Estos silos son el resultado de las técnicas de implementación existentes en las que cada proveedor instala su propia infraestructura y duplica el hardware, incrementando los costes. Fog Computing puede servir como la plataforma subyacente que soporte aplicaciones del IoT evitando así los silos. Cada aplicación se convierte en un sistema formado por dispositivos IoT (por ejemplo sensores y actuadores), una infraestructura (como Fog Computing) y la nube. Con el fin de mejorar varios aspectos de la vida humana, diferentes sistemas pueden interactuar para correlacionar datos obteniendo funcionalidades que no pueden lograrse por ninguno de los sistemas de forma aislada. Entonces, podemos analizar el IoT como un único sistema en lugar de una conjunción de sistemas aislados. Esta perspectiva conduce a la construcción de Ultra-Large Scale Systems (ULSS), una extensión del concepto de Systems of Systems (SoS), en varios verticales, incluidos los vehículos autónomos, Smart Cities y Smart Grids. El alcance de ULSS es vasto debido a la cantidad de dispositivos y complejo en la variedad de aplicaciones, volumen de datos y diversidad de patrones de comunicación. Para manejar esta escala y complejidad, en esta tesis proponemos Hierarchical Emergent Behaviors (HEB), un paradigma que se basa en los conceptos de comportamientos emergente y organización jerárquica. En lugar de programar explícitamente todas las situaciones posibles en el vasto espacio de escenarios presentes en los ULSS, HEB se basa en comportamientos emergentes inducidos por reglas locales que definen las interacciones de las "cosas" entre ellas y también con su entorno. Discutimos las modificaciones a las arquitecturas clásicas de IoT requeridas por HEB, así como los nuevos desafíos. Una vez que se abordan estos desafíos, como la escalabilidad y la capacidad de administración, podemos ilustrar la utilidad de HEB cuando se ocupa de un ULSS basado en IoT a través de un caso de estudio basado en Vehículos Autónomos (AV). Con este fin, diseñamos y analizamos simulaciones que demuestran su enorme potencial, ya que pequeñas modificaciones en el conjunto básico de reglas inducen comportamientos diferentes e interesantes. Luego, diseñamos un conjunto de primitivas para realizar una maniobra básica, como salir de un pelotón y maniobrar en anticipación de obstáculos más allá del alcance de los sensores de a bordo. Estas simulaciones también evalúan el impacto de una implementación de HEB asistida por nodos de Fog Computing para ampliar el alcance sensorial de los vehículos. Para concluir, desarrollamos una metodología de diseño para construir, evaluar y ejecutar soluciones basadas en HEB para AV. Brindamos fundamentos arquitectónicos para el segundo nivel de HEB y sus implicaciones en áreas importantes como las comunicaciones. Estas bases se validan a través de simulaciones que incorporan nuevas reglas, obteniendo valiosas observaciones experimentales. La arquitectura propuesta tiene un enorme potencial para resolver el problema de escalabilidad que presentan los ULSS, permitiendo que las implementaciones de IoT alcancen su verdadero potencial.Postprint (published version

Bio-inspired Computing and Smart Mobility

Por último, se aborda la predicción de plazas libres de aparcamiento utilizando técnicas de aprendizaje automático, tales como series temporales, agrupamiento, etc., incluyendo un prototipo de aplicación web. La tercera parte de esta tesis doctoral se enfoca en el diseño y evaluación de un nuevo algoritmo inspirado en la epigénesis, el Algoritmo Epigenético. Luego de la descripción del modelo en el que se basa y de sus partes, se utiliza este nuevo algoritmo para la resolución del problema de la mochila multidimensional y se comparan sus resultados con los de otros algoritmos del estado de arte. Por último se emplea también el Algoritmo Epigenético para la optimización de la arquitectura Yellow Swarm, un problema de movilidad inteligente resuelto por un nuevo algoritmo bioinspirado. A lo largo de esta tesis doctoral se han descrito los problemas de movilidad inteligente y propuesto nuevas herramientas para su optimización. A partir de los experimentos realizados se concluye que estas herramientas, basadas en algoritmos bioinspirados, son eficientes para abordar estos problemas, obteniendo resultados competitivos comparados con los del estado del arte, los cuales han sido validados estadísticamente. Esto representa un aporte científico pero también una serie de mejoras para la sociedad toda, tanto en su salud como en el aprovechamiento de su tiempo libre. Fecha de lectura de Tesis: 01 octubre 2018.Esta tesis doctoral propone soluciones a problemas de movilidad inteligente, concretamente la reducción de los tiempos de viajes en las vías urbanas, las emisiones de gases de efecto invernadero y el consumo de combustible, mediante el diseño y uso de nuevos algoritmos bioinspirados. Estos algoritmos se utilizan para la optimización de escenarios realistas, cuyo trazado urbano se obtiene desde OpenStreetMap, y que son luego evaluados en el microsimulador SUMO. Primero se describen las bases científicas y tecnológicas, incluyendo la definición y estado del arte de los problemas a abordar, las metaheurísticas que se utilizarán durante el desarrollo de los experimentos, así como las correspondientes validaciones estadísticas. A continuación se describen los simuladores de movilidad como principal herramienta para construir y evaluar los escenarios urbanos. Por último se presenta una propuesta para generar tráfico vehicular realista a partir de datos de sensores que cuentan el número de vehículos en la ciudad, utilizando herramientas incluidas en SUMO combinadas con algoritmos evolutivos. En la segunda parte se modelan y resuelven problemas de movilidad inteligente utilizando las nuevas arquitecturas Red Swarm y Green Swarm para sugerir nuevas rutas a los vehículos utilizando nodos con conectividad Wi-Fi. Red Swarm se centra en la reducción de tiempos de viajes evitando la congestión de las calles, mientras que Green Swarm está enfocado en la reducción de emisiones y consumo de combustible. Luego se propone la arquitectura Yellow Swarm que utiliza una serie de paneles LED para indicar desvíos que los vehículos pueden seguir en lugar de nodos Wi-Fi haciendo esta propuesta más accesible. Además se propone un método para genera rutas alternativas para los navegadores GPS de modo que se aprovechen mejor las calles secundarias de las ciudades, reduciendo los atascos

Anomaly detection for IoT networks using machine learning

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe Internet of Things (IoT) is considered one of the trending technologies today. IoT affects various industries, including logistics tracking, healthcare, automotive and smart cities. A rising number of cyber-attacks and breaches are rapidly targeting networks equipped with IoT devices. This thesis aims to improve security in IoT networks by enhancing anomaly detection using machine learning. This thesis identified the challenges and gaps related to securing the Internet of Things networks. The challenges are network size, the number of devices, the human factor, and the complexity of IoT networks. The gaps identified include the lack of research on signature-based intrusion detection systems used for anomaly detection, in addition to the lack of modelling input parameters required for anomaly detection in IoT networks. Furthermore, there is a lack of comparison of the performance of machine learning algorithms on standard and real IoT datasets. This thesis creates a dataset to test the anomaly binary classification performance of the Neural Networks, Gaussian Naive Bayes, Support Vector Machine, and Decision Trees machine learning algorithms and compares their results with the KDDCUP99 dataset. The results show that Support Vector Machine and Gaussian Naive Bayes perform lower than the other models on the created IoT dataset. This thesis reduces the number of features required by machine learning algorithms for anomaly detection in the IoT networks to five features only, which resulted in reduced execution time by an average of 58%. This thesis tests CNNwGFC, which is an enhanced Convolutional Neural Network model, in detecting and classifying anomalies in IoT networks. This model achieves an increase of 15.34% in the accuracy for IoT anomaly classification in the UNSW-NB15 compared to the classic Convolutional Neural Network. The CNNwGFC multi-classification accuracy (96.24%) is higher by 7.16 than the highest from the literature

Analysis of Illegal Parking Behavior in Lisbon: Predicting and Analyzing Illegal Parking Incidents in Lisbon´s Top 10 Critical Streets

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceIllegal parking represents a costly and pervasive problem for most cities, as it not only leads to an increase in traffic congestion and the emission of air pollutants but also compromises pedestrian, biking, and driving safety. Moreover, it obstructs the flow of emergency vehicles, delivery services, and other essential functions, posing a significant risk to public safety and impeding the efficient operation of urban services. These detrimental effects ultimately diminish the cleanliness, security, and overall attractiveness of cities, impacting the well-being of both residents and visitors alike. Traditionally, decision-support systems utilized for addressing illegal parking have heavily relied on costly camera systems and complex video-processing algorithms to detect and monitor infractions in real time. However, the implementation of such systems is often challenging and expensive, particularly considering the diverse and dynamic road environment conditions. Alternatively, research studies focusing on spatiotemporal features for predicting parking infractions present a more efficient and cost-effective approach. This project focuses on the development of a machine learning model to accurately predict illegal parking incidents in the ten highly critical streets of Lisbon Municipality, taking into account the hour period and whether it is a weekend or holiday. A comprehensive evaluation of various machine learning algorithms was conducted, and the k-nearest neighbors (KNN) algorithm emerged as the top performing model. The KNN model exhibited robust predictive capabilities, effectively estimating the occurrence of illegal parking in the most critical streets, and together with the creation of an interactive and user-friendly dashboard, this project contributes valuable insights for urban planners, policymakers, and law enforcement agencies, empowering them to enhance public safety and security through informed decision-making

Estão os portugueses preparados para o futuro do turismo? Aplicação do modelo de aceitação tecnológica ao uso de robots em turismo

The tourism sector has been growing exponentially in Portugal over the last few years, becoming increasingly competitive. On the other hand, the use of machines, robots and artificial intelligence in this industry that is built by and for people, has also been increasing and diversifying. The objective of this investigation focuses on the study of variables that can affect the acceptance of robots by the Portuguese public. The Technology Acceptance Model (TAM) is applied to understand the influence of a set of sociodemographic variables, travel behavior, motivation, and attitude towards technology in general in the perceived ease of use and perceived usefulness of using robots in tourism. The results obtained demonstrate that the Portuguese case is similar to that of other Western countries, with gender, age, travel group, motivation and attitude towards technology having a significant impact on the dependent variables.O setor do turismo em Portugal tem vindo a crescer exponencialmente nos últimos anos, tornando-se cada vez mais competitivo. Por outro lado, o uso de máquinas, robots e inteligência artificial nesta que é uma indústria construída por e para pessoas, tem também vindo a aumentar e a diversificar-se. O objetivo desta investigação centra-se no estudo das variáveis que podem afetar a aceitação dos robots por parte do público português. É aplicado o Modelo de Aceitação Tecnológica para perceber a influência de um conjunto de variáveis sociodemográficas, de comportamento em viagem, de motivação e de atitude face à tecnologia em geral na facilidade de utilização percebida e utilidade percebida do uso de robots em turismo. Os resultados obtidos permitem concluir que o caso português se assemelha ao de outros países ocidentais, tendo o género, idade, grupo de viagem, motivação e atitude face à tecnologia um impacto significativo nas variáveis dependentes.Mestrado em Gestão e Planeamento em Turism

Path planning, modelling and simulation for energy optimised mobile robotics

This thesis is concerned with an investigation of a solution for mobile robotic platforms to minimize the usage of scarce energy that is available and is not wasted following traditionally planned paths for complex terrain environments. This therefore addresses the need to reduce the total energy cost during a field task or mission. A path planning algorithm is designed by creating a new approach of artificial potential field method that generates a planned path, utilising terrain map. The new approach has the capability of avoiding the local minimum problems which is one of the major problems of traditional potential field method. By solving such problems gives a reliable solution to establish a required path. Therefore the approach results in an energy efficient path of the terrain identified, instead obvious straight line of the terrain. A literature review is conducted which reviews the mainstream path planning algorithms with the applications in mobile robotic platforms was analysed. These path planning algorithms are compared for the purpose of energy optimized planning, which concludes the method of artificial potential field as the path planning algorithm which has the most potential and will be further investigated and improved in this research. The methodology of designing, modelling and simulating a mobile robotic platform is defined and presented for the purpose of energy optimized path planning requirement. The research is to clarify the needs, requirements, and specifications of the design. A complete set of models which include mechanical and electrical modelling, functional concept modelling, modelling of the system are established. Based on these models, an energy optimized path planning algorithm is designed. The modelling of force and the kinematics is established to validate and evaluate the result of the algorithm through simulations. Moreover a simulation environment is established which is constructed for multi perspective simulation. This also enables collaborative simulation using Simulink and ADAMS to for simulating a path generated by the path planning algorithm and assess the energy consumption of the driven and steering mechanism of an exemplar system called AgriRover. This simulation environment allows the capture of simulated result of the total energy consumption, therefore outlines the energy cost behaviour of the AgriRover. A total of two sets of paths was tested in the fields for validation, one being generated by the energy optimized path planning algorithm and the other following a straight path. During the field tests the total cost of energy was captured . Two sets of results are compared with each other and compared with the simulation. The comparison shows a 21.34% of the energy saving by deploying the path generated with the energy optimized path planning algorithm in the field test. This research made the following contribution to knowledge. A comparison and grading of mainstream path planning algorithms from energy optimisation perspective is undertaken using detailed evaluation criteria, including computational power required, extendibility, flexibility and more criteria that is relevant for the energy optimized planning purpose. These algorithms have not been compared from energy optimisation angle before, and the research for energy optimised planning under complex terrain environments have not been investigated. Addressing these knowledge gaps, a methodology of designing, modelling and simulating a mobile platform system is proposed to facilitate an energy optimized path planning. This , leads to a new approach of path planning algorithm that reduces unnecessary energy spend for climbing of the terrain, using the terrain data available. Such a methodology derives several novel methods: Namely, a method for avoiding local minimum problem for artificial potential field path planning using the approach of approximation; A method of achieving high expendability of the path planning algorithm, where this method is capable of generate a path through a large map in a short time; A novel method of multi perspective dynamic simulation, which is capable of simulating the behaviour of internal mechanism and the overall robotic mobile platform with the fully integrated control, The dynamic simulation enables prediction of energy consumption; Finally, a novel method of mathematically modelling and simplifying a steering mechanism for the wheel based mobile vehicle was further investigated.This thesis is concerned with an investigation of a solution for mobile robotic platforms to minimize the usage of scarce energy that is available and is not wasted following traditionally planned paths for complex terrain environments. This therefore addresses the need to reduce the total energy cost during a field task or mission. A path planning algorithm is designed by creating a new approach of artificial potential field method that generates a planned path, utilising terrain map. The new approach has the capability of avoiding the local minimum problems which is one of the major problems of traditional potential field method. By solving such problems gives a reliable solution to establish a required path. Therefore the approach results in an energy efficient path of the terrain identified, instead obvious straight line of the terrain. A literature review is conducted which reviews the mainstream path planning algorithms with the applications in mobile robotic platforms was analysed. These path planning algorithms are compared for the purpose of energy optimized planning, which concludes the method of artificial potential field as the path planning algorithm which has the most potential and will be further investigated and improved in this research. The methodology of designing, modelling and simulating a mobile robotic platform is defined and presented for the purpose of energy optimized path planning requirement. The research is to clarify the needs, requirements, and specifications of the design. A complete set of models which include mechanical and electrical modelling, functional concept modelling, modelling of the system are established. Based on these models, an energy optimized path planning algorithm is designed. The modelling of force and the kinematics is established to validate and evaluate the result of the algorithm through simulations. Moreover a simulation environment is established which is constructed for multi perspective simulation. This also enables collaborative simulation using Simulink and ADAMS to for simulating a path generated by the path planning algorithm and assess the energy consumption of the driven and steering mechanism of an exemplar system called AgriRover. This simulation environment allows the capture of simulated result of the total energy consumption, therefore outlines the energy cost behaviour of the AgriRover. A total of two sets of paths was tested in the fields for validation, one being generated by the energy optimized path planning algorithm and the other following a straight path. During the field tests the total cost of energy was captured . Two sets of results are compared with each other and compared with the simulation. The comparison shows a 21.34% of the energy saving by deploying the path generated with the energy optimized path planning algorithm in the field test. This research made the following contribution to knowledge. A comparison and grading of mainstream path planning algorithms from energy optimisation perspective is undertaken using detailed evaluation criteria, including computational power required, extendibility, flexibility and more criteria that is relevant for the energy optimized planning purpose. These algorithms have not been compared from energy optimisation angle before, and the research for energy optimised planning under complex terrain environments have not been investigated. Addressing these knowledge gaps, a methodology of designing, modelling and simulating a mobile platform system is proposed to facilitate an energy optimized path planning. This , leads to a new approach of path planning algorithm that reduces unnecessary energy spend for climbing of the terrain, using the terrain data available. Such a methodology derives several novel methods: Namely, a method for avoiding local minimum problem for artificial potential field path planning using the approach of approximation; A method of achieving high expendability of the path planning algorithm, where this method is capable of generate a path through a large map in a short time; A novel method of multi perspective dynamic simulation, which is capable of simulating the behaviour of internal mechanism and the overall robotic mobile platform with the fully integrated control, The dynamic simulation enables prediction of energy consumption; Finally, a novel method of mathematically modelling and simplifying a steering mechanism for the wheel based mobile vehicle was further investigated