Contributions to robust motion planning and control of autonomous vehicles
(English) Autonomous driving is a promising solution for current transportation systems that would provide sustainable traffic flow, efficient mobility, and pollution reduction. However, the interaction of multiple vehicles presents several engineering challenges, such as vehicle-to-vehicle communic...
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| Tipo de recurso: | tesis doctoral |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/452576 |
| Acceso en línea: | https://hdl.handle.net/2117/452576 https://dx.doi.org/10.5821/dissertation-2117-452576 |
| Access Level: | acceso abierto |
| Palabra clave: | 004 - Informàtica 68 - Indústries oficis i comerç d'articles acabats. Tecnologia cibernètica i automàtica 629 - Enginyeria dels vehicles de transport Àrees temàtiques de la UPC::Informàtica Àrees temàtiques de la UPC::Enginyeria mecànica |
| Sumario: | (English) Autonomous driving is a promising solution for current transportation systems that would provide sustainable traffic flow, efficient mobility, and pollution reduction. However, the interaction of multiple vehicles presents several engineering challenges, such as vehicle-to-vehicle communication and critical decision-making. Robustness is another challenge that is crucial to ensure the proper design of autonomous driving technology. Vehicles are subject to significant uncertainty due to modeling errors, discretization of sensor signals, or external signals that adversely affect their nominal behaviour. This thesis proposes robust and optimal model-based approaches to address motion planning and control problems. Robustness is addressed from a set theory perspective. Specifically, the advantages of zonotopic sets are used to exploit the design of tube constraints in planning and control, as well as to quantify and propagate uncertainty in the modeling and estimation phases. In the field of modeling, this thesis utilizes control/planning-oriented models using the Takagi-Sugeno (TS) and linear parameter-varying (LPV) paradigms to represent the non-linear model of the vehicle. These representations are obtained from the physical equations of motion or directly from data. The data-driven dynamic model is trained using an adaptive neuro-fuzzy inference system (ANFIS). Meanwhile, the uncertainty associated with the model parameters is estimated through a zonotopic recursive least-square (ZRLS) approach. In the field of motion control, this thesis proposes a two-layer trajectory tracking robust strategy based on model predictive control (MPC) in the outer layer. In the inner layer, a combination of a linear quadratic zonotopic controller (LQZ) and a zonotopic Kalman filter (ZKF) is used. The vehicle's energy is managed by the MPC through the state of charge of its battery while ensuring performance indexes in terms of longitudinal, lateral, and angular velocities. To achieve this, the strategy combines a data-driven dynamic model with kinematic and state-of-charge models. Subsequently, the two-layer robust configuration is utilized for planning purposes. In the first step, the model predictive planner (MPP) generates feasible references online while maximizing both the longitudinal velocity and the saving energy through the battery state of charge. This is different to the control stage, in which the controller follows the reference previously computed offline. Additionally, the planner integrates a data-driven prediction model and a computationally efficient driving-corridor-like method based on track width constraints to handle collision avoidance for both static and moving obstacles. In the second step, an MPP is proposed to address coordination between connected autonomous vehicles. This planner utilizes the LPV representation of the non-linear vehicle model, which is derived from the physical equations of motion using the non-linear embedding approach. Furthermore, it incorporates mixed-integer linear inequalities to ensure safe overtaking maneuvers and avoid non-linear constraints imposed by obstacle boundaries. To assess the effectiveness of the proposed control and planning approaches, a small 1/10 scale electric car is utilized. Furthermore, simulations are performed in aggressive autonomous driving regimes, which involve steep curves and driving at maximum velocity. |
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