Integrated Stability and Tracking Control System for Autonomous Vehicle-Trailer Systems

The addition of a trailer to a vehicle significantly changes the dynamics of a single-vehicle and generates new instability modes including jackknifing and snaking. Understanding of the dynamics of these modes leads to the development of more effective control strategies for improved stability and handling. In addition, vehicle-trailer systems suffer from the off-tracking problem meaning that the path of the trailer rear end differs from that of the vehicle front end. Off-tracking makes these vehicles less maneuverable and increases swept path of the vehicle, especially in urban areas and tight spaces. Vehicle active safety systems have been extensively developed over the past decades to improve vehicle handling and assist the driver to keep the vehicle under control in unfavorable driving situations. Such active safety systems, however, are not well developed for vehicle-trailer systems specialty to control the above-mentioned instability modes. In addition to the above active safety systems, off-tracking of vehicle-trailers is another aspect that is essential in path planning and path tracking of autonomous vehicle-trailers. In this thesis, first, phase portraits are used to study the non-linear dynamics of the system through state trajectories and equilibrium point locations of a vehicle-trailer system, with respect to the inputs and also the loading conditions of the trailer. By the study of the phase portraits, the foundation of the vehicle-trailer yaw instability is recognized and a two-dimensional stable region as the stability envelope is defined. This stability envelope is utilized to prevent unnecessary control interventions while the vehicle is operating in the safe stability region whereas the controller is allowed to effectively interfere when the vehicle crosses the stability envelope. To handle multiple control actions in the vehicle and trailer units, a control structure with two hierarchical layers is proposed. In the upper layer, a model predictive controller is formulated as a quadratic optimization problem with a virtual control action for each degree of freedom of the vehicle dynamic model. In this formulation, the developed stability envelope is constructed as state constraints along with control action constraints. In the lower layer, a control allocation approach optimally transforms the virtual control actions provided by the upper layer into steering and/or braking commands for each wheel. In this approach, actuator failure in addition to actuator and tire capacity constraints is taken into account in real-time. A hybrid A*-based motion planning is developed to generate a trajectory while considering the trailer effect in terms of stability in high speed and off-tracking in high curvature paths. The proposed motion planning utilizes a hybrid A* algorithm combined with potential fields to find a feasible and collision-free trajectory for the vehicle-trailer system. To assess the performance of the proposed control structure in instability prevention both in snaking and jackknifing, different simulations are conducted using different control actions. Additionally, the fault tolerance and robustness of the proposed controller are investigated. To validate the real-time performance of the proposed control strategy, experimental tests are performed on a vehicle-trailer system with differential braking capability. The results show that the proposed control strategy is able to effectively prevent both instability modes as well as unnecessary engagement of the control action to reduce control intervention. The performance of the developed motion planning module is also evaluated for normal driving, obstacle avoidance, off-tracking compensation, and crash mitigation using high-fidility Carsim model. It is observed that proposed motion planning is able to effectively satisfy all the expected requirements

Hybrid PSO-PWL-Dijkstra approach for path planning of non holonomic platforms in dense contexts

Planning is an essential capability for autonomous robots. Many applications impose a diversity of constraints and traversing costs in addition to the usually considered requirement of obstacle avoidance. In applications such as route planning, the use of dense properties is convenient as these describe the terrain and other aspects of the context of operation more rigorously and are usually the result of a concurrent mapping and learning process. Unfortunately, planning for a platform with more than three degrees of freedom can be computationally expensive, particularly if the application requires the platform to optimally deal with a thorough description of the terrain. The objective of this thesis is to develop and demonstrate an efficient path planning algorithm based on dynamic programming. The goal is to compute paths for ground vehicles with and without trailers, that minimise a specified cost-to-go while taking into account dynamic constraints of the vehicle and dense properties of the environment. The proposed approach utilises a Quadtree Piece-Wise Linear (QT-PWL) approximation to describe the environment in a low dimensional subspace and later uses a particle approach to introduce the dynamic constraints of the vehicle and to smooth the path in the full dimensional configuration space. This implies that the optimisation process can exploit the QT-PWL partition. Many usual contexts of operation of autonomous platforms have cluttered spaces and large regions where the dense properties are smooth; therefore, the QT-PWL partition is able to represent the context in a fraction of cells that would be needed by a homogeneous grid. The proposed methodology includes adaptations to both algorithms to achieve higher efficiency of the computational cost and optimality of the planned path. In order to demonstrate the capabilities of the algorithm, an idealized test case is presented and discussed. The case for a car and a tractor with multiple trailers is presented. A real path planning example is presented in addition to the synthetic experiments. Finally, the experiments and results are analysed and conclusions and directions for possible future work are presented

Advanced Sensing and Control for Connected and Automated Vehicles

Connected and automated vehicles (CAVs) are a transformative technology that is expected to change and improve the safety and efficiency of mobility. As the main functional components of CAVs, advanced sensing technologies and control algorithms, which gather environmental information, process data, and control vehicle motion, are of great importance. The development of novel sensing technologies for CAVs has become a hotspot in recent years. Thanks to improved sensing technologies, CAVs are able to interpret sensory information to further detect obstacles, localize their positions, navigate themselves, and interact with other surrounding vehicles in the dynamic environment. Furthermore, leveraging computer vision and other sensing methods, in-cabin humans’ body activities, facial emotions, and even mental states can also be recognized. Therefore, the aim of this Special Issue has been to gather contributions that illustrate the interest in the sensing and control of CAVs

Control and supervision of an AGV with energy consumption optimization

Os veículos guiados autónomos (AGVs) ganharam enorme importância e interesse no campo da indústria. Estes são soluções populares para o transporte de bens materiais para diferentes partes das fábricas. No entanto, em muitas fábricas, os armazéns estão localizados à parte da linha de produção ou em edifícios separados, exigindo que o transporte de bens materiais seja feito exteriormente. Os ambientes exteriores representam um desafio particular para os AGVs. Por um lado, estes ambientes causam mais desgaste nos componentes dos veículos e o clima na Europa pode atingir extremos opostos, dependendo da estação do ano e das regiões. Por outro lado, estes ambientes aumentam as preocupações de segurança, uma vez que outros veículos ou peões podem circular no mesmo espaço e ao mesmo tempo. Neste projecto, um rebocador eléctrico XXL será transformado num AGV, que opera em ambiente exterior. Este veículo é responsável pelo transporte de mercadorias do final da linha de produção para o armazém exterior numa fábrica de automóveis. O principal objectivo é assegurar o seu funcionamento contínuo durante um turno de 16 horas, garantindo o mínimo de interrupções para v«carregamento da bateria. Desta forma, nesta dissertação foram abordados dois capítulos distintos: para a análise e estudo do consumo energético foi simulado a powertrain de um veículo eléctrico. Neste, foi considerado um motor de indução cujo método de controlo aplicado foi o Field Oriented Control (FOC). Para além do comportamento eléctrico, também foi simulado o modelo físico da carga, bem como o cálculo da energia eléctrica consumida. Para a navegação, foi estudada uma solução baseada na integração do GPS com o INS. Dadas as restrições temporais, apenas a solução GPS foi testada e a técnica Loosely Coupled foi abordada como uma possível solução de integração.Autonomous guided vehicles (AGVs) have gained enormous importance and interest in the industry field. These are popular solutions for transport of good and material to different parts of the factories. However, in many factories, warehouses are located apart from the factory floor or in separate buildings, requiring the transport of material goods to be done outdoors. Outdoor environments represent a particular challenge for AGVs. On one hand, these environments causes more wear and tear on vehicle components and the weather in Europe can reach opposite extremes depending on the season and regions. On the other hand, these environments increase safety concerns since other vehicles or pedestrians can circulate in the same space at the same time. In this project, an electric tugger XXL will be transformed into an AGV, which operates in outdoor environment. This vehicle is responsible for transporting goods from the end of the production line to the outside warehouse in a car manufacturing plant. The main objective is to ensure its continuous operation during a 16-hour shift, and guarantee the minimum battery charging actions. In this way, in this dissertation two distinct chapters were approached: for the analysis and study of the energy consumption it was simulated the powertrain of an electric vehicle. In this one it was considered an induction motor whose control method applied was the Field Oriented Control (FOC). Besides the electrical behaviour, also the physical model of the load was simulated as well as the calculation of the consumed electrical energy. For navigation, a solution based on the integration of GPS with INS was studied. Given the temporal constraints, only the GPS solution was tested and the loosely coupled technique was approached as a possible integration solution