Mechanical Engineering

MARILENA VENDITTELLI

General objectives

The course is composed of two modules and presents a selection of advanced topics in Robotics and is intended as an introduction to research.
Guided through case studies taken from the research activities of the teachers, the student will be able to fully develop a problem in Robotics, from its analysis to the proposal of solution methods and their implementation.


Specific objectives

Knowledge and understanding:
Students will learn some advanced control techniques used in some robotic research areas where the lecturers are active.

Apply knowledge and understanding:
Students will be able to use and design complex control systems for advanced robotic problems.

Critical and judgment skills:
Students will be able to evaluate some methodologies used in the difference robotic applied illustrated areas.

Communication skills:
The course activities will allow students to be able to communicate and share the different solutions, adopted in a research framework, for the different illustrated robotic areas.

Learning ability:
The course development aims at giving the student the capacity to design complex control systems for advanced robotic systems.

Knowledge and understanding:
Students will learn some advanced control techniques used in some robotic research areas where the lecturers are active.

Apply knowledge and understanding:
Students will be able to use and design complex control systems for advanced robotic problems.


Critical and judgment skills:
Students will be able to evaluate some methodologies used in the difference robotic applied illustrated areas.


Communication skills:
The course activities will allow students to be able to communicate and share the different solutions, adopted in a research framework, for the different illustrated robotic areas.

Learning ability:
The course development aims at giving the student the capacity to design complex control systems for advanced robotic systems.

A general background in robotics (kinematics, dynamics, planning, control) is desirable but not mandatory.

The course is organized in two modules of 3 credits, chosen from the 4 modules of the Elective in Robotics course. Each module has its own specific program available at the beginning of the lesson cycle.
A detailed and updated description of the programs is available at the page: http://www.diag.uniroma1.it/vendittelli/EIR/

Here is a summary of the 4 Elective in Robotics modules to choose from:

Module 1: Modeling and control of multi-rotor UAVs (Marilena Vendittelli)

- Introduction to the course and aerial vehicles modeling
- Quadrotor modeling
- Control based on linear approximation
- Backstepping-based control
- Control based on dynamic feedback linearization
- Geometric control on SE(3)
- State estimation
- Motion planning
- Navigation among obstacles based on control barrier functions


Module 2: Underactuated Robots (Leonardo Lanari, Nicola Scianca)

1. Introduction
Motivation. Definition of underactuated system (generalized coordinates vs degrees of freedom). Examples of underactuated robots.

2. Modeling and Properties
Eulero-Lagrange modeling (classic and alternate). State-space form. Control problems of interest. Controllabiity (STLA, STLC, natural controllability). Comparison with fully actuated robots. Integrability conditions for passive dynamics. Equilibrium points and linear controllability.

3. Case Studies: Acrobot and Pendubot
Modeling. Approximate linearization at equilibria. Linear controllability. Balancing. Partial feedback linearization. Swing-up (1) via analysis of the zero dynamics (2) via energy pumping.

4. Zero dynamics in underactuated systems
Normal form and zero dynamics. Importance of the zero dynamics in control. Zero-dynamics in linear and nonlinear underactuated systems. The homoclinic orbit.

5. Passivity
Definition and physical interpretation. Linear and nonlinear mechanical systems examples. Dissipativity in state space representations. Feedback equivalence to a passive system. Output stabilization of passive systems

6. Energy-based control of underactuated systems
The convey-crane and reaction-wheel cases.

7. Optimization methods for Planning and Control
Introduction to Dynamic Programming. Hamilton-Jacobi-Bellman equation. Derivation of the Linear Quadratic Regulator
Linear-Time-Varying LQR. Trajectory optimization with Iterative LQR. Constrained optimization. Model Predictive Control (Linear, LTV and Nonlinear). LQR-trees.

Module 3: Physical Human-Robot Interaction (Antonio Franchi)

Physical and cognitive Human-Robot Interaction (pHRI and cHRI). Robot safety and dependability (mechanics, sensing, planning, and control). Lightweight and compliant robotic manipulators. Robots with Variable Stiffness Actuation (VSA). Soft robotics. An architecture for pHRI: safety, coexistence, and collaborative layers. Safety standards in robotics. The collision event pipeline. Sensorless detection and isolation of collisions and contacts. Collision detection in industrial robots with a closed control architeture. Safe reaction strategies to collisions. Use of redundancy. Human-robot coexistence. Monitoring distances in the workspace. Collision avoidance in dynamic/anthropic domains. Industrial case study. Human-robot collaboration: contactless/visual coordination or with physical interaction. Contact localization and contact force estimation. Control schemes for collaborative human-robot tasks: admittance control, force regulation, impedance control, hybrid force/motion control. Case studies in two industrial prototype cells.

Module 4: Control of Multi-Robot Systems (Andrea Cristofaro)

- Examples of applications of multi-robot systems.
- Centralized vs. decentralized architectures.
- Elements of graph theory.
- Connectivity and Consensus; Passivity and Lyapunov stability; Interconnection of mechanical systems.
- Application to multi-UAV systems: Formation control with time-varying topology; Formation control with connectivity maintenance; Steady-state behaviors;
- Overview of other multi robot problems.

Material distributed by the lecturers.

Lectures illustrating the methodologies used in the areas considered in the different modules of the course. Case study analysis, examples of application to real systems. Hands-on laboratory activities, if allowed by the anti-covid measures.

Not mandatory in general, although instructors of each module may overrule this general modality.

Projects or presentations for topics in each module.

Not applicable, given the exam modalities (project or paper presentation). Visit the website of each single module where examples of projects from past years and lists of papers eligible for presentations might be available.