On Optimal Mission Planning for Vehicles over Long-distance Trips

This thesis proposes a mission planner for vehicles over long-distance trips, for finding the optimal trade-off between trip time, energy efficiency, anddriver comfort, subject to road information, traffic situations, and weather conditions. The mission planner consists of three components, i.e. logisticsplanner, eco-driving supervisor, and thermal and charging supervisor. The logistics planner aims at optimising the mission start and/or finish time byminimising energy consumption and trip time. The eco-driving supervisor computes the velocity profile of the driving vehicle, by optimising the energyconsumption and penalising driver discomfort. To do so, an online-capable algorithm has been formulated in a model predictive control framework, subject to road and traffic information, and the pre-optimised mission start and/or finish time. This algorithm is computationally efficient and enables the driving vehicle to adapt and optimally respond to predicted disturbances within a short amount of time. Eco-driving has also been achieved for a vehicleconfronted with wind, by applying stochastic dynamic programming method. The thermal and charging supervisor regulates battery temperature and state of charge by coordinating the energy use of different thermal components. Within the thermal and charging supervisor design, a heat pump has been included for waste heat recovery purposes. Also, the charging stops have been optimally planned, in favour of energy efficiency and trip time. The performance of the proposed algorithms over a road with a hilly terrain is assessed using simulations. According to the simulation results, it is observed that total travel time is reduced up to 5.5 % by optimising the mission start time, when keeping an average cruising speed of about 75 km/h. Also, compared to standard cruise control, the energy savings of using this algorithm is up to 11.6 %. Furthermore, total charging time and energy consumption are reduced by up to 19.4 % and 30.6 %, respectively by developing the thermal and charging supervisor, compared to a case without the heat pump activated and without charge point optimisation

INTELLIGENT VISION-BASED NAVIGATION SYSTEM

This thesis presents a complete vision-based navigation system that can plan and follow an obstacle-avoiding path to a desired destination on the basis of an internal map updated with information gathered from its visual sensor. For vision-based self-localization, the system uses new floor-edges-specific filters for detecting floor edges and their pose, a new algorithm for determining the orientation of the robot, and a new procedure for selecting the initial positions in the self-localization procedure. Self-localization is based on matching visually detected features with those stored in a prior map. For planning, the system demonstrates for the first time a real-world application of the neural-resistive grid method to robot navigation. The neural-resistive grid is modified with a new connectivity scheme that allows the representation of the collision-free space of a robot with finite dimensions via divergent connections between the spatial memory layer and the neuro-resistive grid layer. A new control system is proposed. It uses a Smith Predictor architecture that has been modified for navigation applications and for intermittent delayed feedback typical of artificial vision. A receding horizon control strategy is implemented using Normalised Radial Basis Function nets as path encoders, to ensure continuous motion during the delay between measurements. The system is tested in a simplified environment where an obstacle placed anywhere is detected visually and is integrated in the path planning process. The results show the validity of the control concept and the crucial importance of a robust vision-based self-localization process

On optimal mission planning for conventional and electric heavy duty vehicles

Ever-growing energy consumption and CO2 emissions due to the increase in road transport are major challenges that attract international attention, especially policy makers, logistic service providers and customers considering environmental, ecological and economic issues. Other negative side-effects caused by the growth of the road transport are the extensive economic and social costs because of traffic congestion. Thus, there is a strong motivation to investigate possible ways of improving transport efficiency aiming at achieving a sustainable transport, e.g. by finding the best compromise between resource consumption and logistics performance. The transport efficiency can be improved by optimal planning of the transport mission, which can be interpreted as optimising mission start and/or finish time, and velocity profile of the driving vehicle. This thesis proposes a bi-layer mission planner for long look-ahead horizons stretched up to hundreds of kilometers. The mission planner consists of logistics planner as its top level and eco-driving supervisor as its bottom level. The logistics planner aims at optimising the mission start and/or finish time by optimising energy consumption and travel time, subject to road and traffic information, e.g. legal and dynamic speed limits. The eco-driving supervisor computes the velocity profile of the driving vehicle by optimising the energy consumption and penalising driver discomfort. To do so, an online-capable algorithm has been formulated in MPC framework, subject to road and traffic information, and the pre-optimised mission start and/or finish time. This algorithm is computationally efficient and enables the driving vehicle to adapt and optimally respond to predicted disturbances within a short amount of time. The mission planner has been applied to conventional and fully-electric powertrains. It is observed that total travel timeis reduced up to 5.5 % by optimising the mission start time, when keeping anaverage cruising speed of about 75 km/h. Also, compared to standard cruise control, the energy savings of using this algorithm is up to 11.6 %

SLAM research for port AGV based on 2D LIDAR

With the increase in international trade, the transshipment of goods at international container ports is very busy. The AGV (Automated Guided Vehicle) has been used as a new generation of automated container horizontal transport equipment. The AGV is an automated unmanned vehicle that can work 24 hours a day, increasing productivity and reducing labor costs compared to using container trucks. The ability to obtain information about the surrounding environment is a prerequisite for the AGV to automatically complete tasks in the port area. At present, the method of AGV based on RFID tag positioning and navigation has a problem of excessive cost. This dissertation has carried out a research on applying light detection and ranging (LIDAR) simultaneous localization and mapping (SLAM) technology to port AGV. In this master's thesis, a mobile test platform based on a laser range finder is developed to scan 360-degree environmental information (distance and angle) centered on the LIDAR and upload the information to a real-time database to generate surrounding environmental maps, and the obstacle avoidance strategy was developed based on the acquired information. The effectiveness of the platform was verified by the experiments from multiple scenarios. Then based on the first platform, another experimental platform with encoder and IMU sensor was developed. In this platform, the functionality of SLAM is enabled by the GMapping algorithm and the installation of the encoder and IMU sensor. Based on the established environment SLAM map, the path planning and obstacle avoidance functions of the platform were realized.Com o aumento do comércio internacional, o transbordo de mercadorias em portos internacionais de contentores é muito movimentado. O AGV (“Automated Guided Vehicle”) foi usado como uma nova geração de equipamentos para transporte horizontal de contentores de forma automatizada. O AGV é um veículo não tripulado automatizado que pode funcionar 24 horas por dia, aumentando a produtividade e reduzindo os custos de mão-de-obra em comparação com o uso de camiões porta-contentores. A capacidade de obter informações sobre o ambiente circundante é um pré-requisito para o AGV concluir automaticamente tarefas na área portuária. Atualmente, o método de AGV baseado no posicionamento e navegação de etiquetas RFID apresenta um problema de custo excessivo. Nesta dissertação foi realizada uma pesquisa sobre a aplicação da tecnologia LIDAR de localização e mapeamento simultâneo (SLAM) num AGV. Uma plataforma de teste móvel baseada num telémetro a laser é desenvolvida para examinar o ambiente em redor em 360 graus (distância e ângulo), centrado no LIDAR, e fazer upload da informação para uma base de dados em tempo real para gerar um mapa do ambiente em redor. Uma estratégia de prevenção de obstáculos foi também desenvolvida com base nas informações adquiridas. A eficácia da plataforma foi verificada através da realização de testes com vários cenários e obstáculos. Por fim, com base na primeira plataforma, uma outra plataforma experimental com codificador e sensor IMU foi também desenvolvida. Nesta plataforma, a funcionalidade do SLAM é ativada pelo algoritmo GMapping e pela instalação do codificador e do sensor IMU. Com base no estabelecimento do ambiente circundante SLAM, foram realizadas as funções de planeamento de trajetória e prevenção de obstáculos pela plataforma

Autonomous mobile robot navigation using fuzzy logic control

Traditionally the type of robot used in the workplace consisted mainly o f the fixed arm variety. Any mobile robots that were commercially available required that the environment be altered to accommodate them. This involved the installation of guide lanes or some form of sensor units placed at various locations around the workplace to facilitate the robot in determining its position within the environment. Such approaches are costly and limit the use of robots to environments where these methods are feasible. The inadequacies in this technology has led to research into autonomous mobile robots that offer greater flexibility and do not require changes in the enviromnent. There are many technical issues to be addressed in designing such a robot. These stem from the necessity that the robot must be able to navigate through an environment unaided. Other problems such as the cost of the vehicle must be considered so that prospective customers will not be put off. This thesis discusses the strategies taken in addressing the problems associated with navigation in an obstacle strewn environment. Such issues include position estimation, path planning, obstacle avoidance and the acquisition and interpretation of sensor information. It also discusses the suitability of fuzzy logic for controlling a robot. A graphical user interface runs on the PC which communicates with the robot over a radio link. The robot uses a fuzzy logic controller to follow a planned path and avoid unknown obstacles by controlling the velocity and steering angle o f the drive unit. It is a tracked vehicle which is suitable for indoor use only. The results of path planning and the robots attempts at following the paths and avoiding obstacles are illustrated and discussed

Multi Vehicle Trajectory Planning On Road Networks

When multiple autonomous vehicles work in a shared space, such as in a surface mine or warehouse, they often travel along specified paths through a static road network. Although these vehicles’ actions and performance are coupled, their motion is often planned myopically or omits cooperation beyond avoiding collisions reactively. More desirable solutions could be achieved by coordinating and planning actions ahead of time. To make multi-vehicle systems more productive and efficient, the thesis introduces planning methods that can optimise for travel time, energy consumption, and trajectory smoothness. Vehicle motion is coordinated by using motion models that combine all trajectories, and avoid collisions. Mathematical programming is then used to find optimised solutions. The proposed methods are shown to significantly reduce solution costs compared to an approach based on common driving practices. As the number of vehicles and interactions between them increases, the number of solutions grows exponentially, making finding a solution computationally challenging. A major aim here was to find high quality solutions within practical computation times. To achieve this, techniques were developed that exploit the structure of the problems. This includes a heuristic algorithm that scales better with problem size, and is combined with the mathematical programming techniques to reduce their complexity. These were found to significantly reduce computation times, trading off marginal solution quality

Energy Academic Group Compilation of Abstracts 2012-2016

This report highlights the breadth of energy-related student research at NPS and reinforces the importance of energy as an integral aspect of today's Naval enterprise. The abstracts provided are from theses and a capstone project report completed by December 2012-March 2016 graduates.http://archive.org/details/energyacademicgr109454991

Underwater Vehicles

For the latest twenty to thirty years, a significant number of AUVs has been created for the solving of wide spectrum of scientific and applied tasks of ocean development and research. For the short time period the AUVs have shown the efficiency at performance of complex search and inspection works and opened a number of new important applications. Initially the information about AUVs had mainly review-advertising character but now more attention is paid to practical achievements, problems and systems technologies. AUVs are losing their prototype status and have become a fully operational, reliable and effective tool and modern multi-purpose AUVs represent the new class of underwater robotic objects with inherent tasks and practical applications, particular features of technology, systems structure and functional properties