The Course introduces to optimization models and methods for the solution of decision problems, with particular attention to models and problems arising in Robotics Engineering. It is structured according to the basic topics of problem modelling, its tractability, and its solution by means of algorithms that can be implemented on computers.
The lectures are organized in i) methodology and ii) case-studies from real-world applications. Additional exercises and use of software tools are presented during exercise hours.
The Course presents methodological and computational aspects of optimization methods for the solution of a variety of problems, with particular attention to models and tasks arising in Robotics Engineering. Algorithms and software tools are illustrated. The lectures are structured according to the basic topics of problem modelling, its tractability, its solution by means of algorithms that can be implemented on computers, and related software tools. Several case-studies from Robotics are considered and solved by means of the described algorithms and available software
The Course aims at providing the students with the skills required to deal with engineering problems, with particular emphasis on Robotics Engineering, by developing models and methods that work efficiently in the presence of limited resources.
The students will be taught to: interpret and shape a decision-making process in terms of an optimization problem, identifying the decision-making variables, the cost function to minimize (or the figure of merit to maximize), and the constraints; framing the problem within the range of problems considered "canonical" (linear / nonlinear, discrete / continuous, deterministic / stochastic, static / dynamic, etc.); realizing the "matching" between the solving algorithm (to choose from existing or to be designed) and an appropriate processing software support.
Linear algebra. Vector and matrix calculus. Basic mathematical analysis and geometry.
Lectures and exercises.
Introduction. Optimization and Operations Research for Robotics. Optimization models and methods.
Linear programming (LP): application examples, model, and algorithms. A case-study of LP from Robotics.
Integer linear programming (ILP): application examples, model, and algorithms. A case-study of ILP from Robotics.
Nonlinear programming (NLP): application examples, model, and algorithms. A case-study of NLP from Robotics.
Graph optimization: application examples, model, and algorithms. A case-study of graph optimization from Robotics.
N-stage optimization and dynamic programming: application examples, model and algorithms. A case-study of N-stage optimization from Robotics.
Putting things together: models, methods, and algorithms for the optimisation of robotic systems.
Software tools for optimization.
Lecture notes provided by the teacher (study material will be available in the official study portal).
Ricevimento: By appointment
MARCELLO SANGUINETI (President)
MAURO GAGGERO
MASSIMO PAOLUCCI (President Substitute)
DANILO MACCIO' (Substitute)
ELENA TANFANI (Substitute)
https://easyacademy.unige.it/portalestudenti/index.php?view=easycourse&_lang=it&include=corso
Written.
Exercises and questions on the applications illustrated and the main concepts explained during the lectures.
Comprehension of the concepts explained during the Course.
Capability to:
- interpret and shape a decision-making process in terms of an optimization problem, with particular attention to decision problems in Robotics;
- frame the problem in the range of problems considered "canonical" (linear / nonlinear, discrete / continuous, deterministic / stochastic, static / dynamic, etc.);
- choose and/or develop a solution algorithm that implements a suitable optimization technique, with particular attention to problems arising in Robotics.
