Learning objectives
Il corso mira a fornire le nozioni necessarie per comprendere la pipeline di elaborazione di un sistema di guida autonoma, compresa la percezione, la pianificazione e l'attuazione su piattaforme embedded ad alte prestazioni. Applicherà un insieme eterogeneo di tecniche che vanno dalla visione artificiale e dalle tecniche di machine/deep learning, alla robotica ed al controllo, affrontando molteplici problemi nel settore automobilistico, come il rilevamento di corsie e oggetti, la stima dello stato, il posizionamento preciso, la fusione di sensori e la pianificazione del percorso.
Prerequisites
Per seguire il corso, sono necessarie conoscenze di programmazione (C/C++ o Python).
Course unit content
1-Vehicle modeling
This block presents the most used vehicle models oriented to the design of path planners and vehicle motion control algorithms, including the unicycle, the bicycle and the two tracks models, with special emphasis on the model nonlinearities and uncertainties, which can be relevant for path planning and vehicle motion control.
2-Basics of control theory and constrained optimization The objective of this block is to recall the basics of control theory and optimization that will be used for path planning and vehicle motion control design. Stability analysis tools, SISO design tools methods, with emphasis on the satisfaction of the performance requirements relevant for vehicle motion control will be recalled, while MIMO design tools like LQ, pole-placement will be overviewed. Basics of constrained convex optimization will be recalled.
3-Path planning
After mathematically formulating the path planning problem, in this block we will overview state-of-the-art path planning methods. Model-based path planning tools will be studied in depth, with special emphasis on optimization-based and potential fields methods. The hierarchical decision-making architecture will be introduced to illustrate the constraints set by the underlying vehicle motion control on the path planning and vice versa.
4-Vehicle control for path following
This block will formulate the longitudinal and lateral vehicle motion control problem for path following applications. Feedback/Feedforward schemes will be presented taking advantage of preview information. The impact of sensor noises and actuator dynamics on the closed-loop performance will be emphasized to highlight the connections with the previous blocks of the course. Robustness design issues will be introduced. The impact of steering actuators nonlinearities on the closed-loop performance will be illustrated.
Full programme
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Bibliography
- “FeedbackSystems. AnIntroductionforScientistsand Engineers”. Karl Johan Åström, Richard M. Murray. Available here: https://www.cds.caltech.edu/~murray/books/AM05/pdf/am08-complete_22Feb09.pdf
- “PredictiveControl forLinear and HybridSystems”, Francesco Borrelli, Alberto Bemporad, ManfredMorari. Available here: https://www.amazon.it/Predictive-Control-Linear-Hybrid-Systems/dp/1107016886
- “ModelPredictiveControl: Theory, Computation, and Design”. James B. Rawlings, David Q. Mayne, MoritzM. Diehl. Available here: https://sites.engineering.ucsb.edu/~jbraw/mpc/
Teaching methods
Theoretical classes followed by practical exercises in the laboratory. The teaching material will be made available online from the course website.
Assessment methods and criteria
– 3 compulsory assignments with grades
– Oral exam
Other information
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2030 agenda goals for sustainable development
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