Ingegneria Informatica | MOBILE ROBOTS FOR CRITICAL MISSIONS

cod. 0622700103

MOBILE ROBOTS FOR CRITICAL MISSIONS

	0622700103
	DIPARTIMENTO DI INGEGNERIA DELL'INFORMAZIONE ED ELETTRICA E MATEMATICA APPLICATA
	EQF7
	COMPUTER ENGINEERING
	2022/2023

CURRICULUM ARCHITECTURES AND SYSTEMSCohort 2021/2022

	YEAR OF COURSE 2
	YEAR OF DIDACTIC SYSTEM 2017
	SPRING SEMESTER

TEACHING UNITS

SSD	CFU	HOURS	ACTIVITY	TYPE OF ACTIVITY
ING-INF/05	3	24	LESSONS	OPTIONAL SUBJECTS
ING-INF/05	3	24	LAB	OPTIONAL SUBJECTS

PROFESSORS

	MARIO VENTO T Curriculum
	ALESSIA SAGGESE Curriculum

EXAMS

Exam	Date	Session
MOBILE ROBOTS FOR CRITICAL MISSIONS	22/06/2023 - 09:00	SESSIONE ORDINARIA
MOBILE ROBOTS FOR CRITICAL MISSIONS	20/07/2023 - 09:00	SESSIONE ORDINARIA
MOBILE ROBOTS FOR CRITICAL MISSIONS	08/09/2023 - 14:00	SESSIONE DI RECUPERO

	Objectives
	THE GOAL OF THE COURSE IS TO PROVIDE THE STUDENT WITH THE ARCHITECTURAL, METHODOLOGICAL, AND DESIGN ELEMENTS FOR THE CONSTRUCTION OF INTELLIGENT ROBOTS CAPABLE OF MOVING AUTONOMOUSLY IN INDOOR ENVIRONMENTS. IN PARTICULAR, THE COURSE FOCUSES ON ASPECTS RELATED TO MAP REPRESENTATION, MAP BUILDING, ROBOT LOCALIZATION, NAVIGATION AND OBSTACLE AVOIDANCE ALGORITHMS. KNOWLEDGE AND UNDERSTANDING THE COURSE PRESENTS THE METHODOLOGIES TO ALLOW THE AUTONOMOUS MOVEMENT OF THE ROBOT IN INDOOR ENVIRONMENTS, WHERE THE MAP AND THE ELEMENTS PRESENT WITHIN THE SCENE (FOR INSTANCE THE OBSTACLES) AND THE RELATED POSITION ARE NOT KNOWN A PRIORI. APPLYING KNOWLEDGE AND UNDERSTANDING ABILITY TO DESIGN AND IMPLEMENT SOLUTIONS TO INTELLIGENT ROBOT PROBLEMS, BY CHOOSING AND APPLYING THE APPROPRIATE METHODS PRESENTED IN THE COURSE, AND THE SOFTWARE ENVIRONMENTS SPECIFICALLY DEVISED FOR COGNITIVE ROBOTICS.

THE GOAL OF THE COURSE IS TO PROVIDE THE STUDENT WITH THE ARCHITECTURAL, METHODOLOGICAL, AND DESIGN ELEMENTS FOR THE CONSTRUCTION OF INTELLIGENT ROBOTS CAPABLE OF MOVING AUTONOMOUSLY IN INDOOR ENVIRONMENTS. IN PARTICULAR, THE COURSE FOCUSES ON ASPECTS RELATED TO MAP REPRESENTATION, MAP BUILDING, ROBOT LOCALIZATION, NAVIGATION AND OBSTACLE AVOIDANCE ALGORITHMS.

KNOWLEDGE AND UNDERSTANDING
THE COURSE PRESENTS THE METHODOLOGIES TO ALLOW THE AUTONOMOUS MOVEMENT OF THE ROBOT IN INDOOR ENVIRONMENTS, WHERE THE MAP AND THE ELEMENTS PRESENT WITHIN THE SCENE (FOR INSTANCE THE OBSTACLES) AND THE RELATED POSITION ARE NOT KNOWN A PRIORI.

APPLYING KNOWLEDGE AND UNDERSTANDING
ABILITY TO DESIGN AND IMPLEMENT SOLUTIONS TO INTELLIGENT ROBOT PROBLEMS, BY CHOOSING AND APPLYING THE APPROPRIATE METHODS PRESENTED IN THE COURSE, AND THE SOFTWARE ENVIRONMENTS SPECIFICALLY DEVISED FOR COGNITIVE ROBOTICS.

	Prerequisites
	IN ORDER TO ACHIEVE THE GOALS OF THE COURSE, THE KNOWLEDGE OF THE C AND PYTHON PROGRAMMING LANGUAGE IS REQUIRED.

	Contents
	Didactic unit 1: Introduction to map based navigation and map based representations (LECTURE/PRACTICE/LABORATORY HOURS 10/5/0) - 1 (3 Hours Lecture): Introduction to the course - 2 (2 Hours Lecture): Sensors for mobile robots navigation: IMU, GPS, Laser Scan - 3 (3 Hours Lecture): Behavior based vs map based navigation. Differential robot: motion model - 4 (2 Hours Lecture): Map based representations - 5 (3 Hours Practice): Introduction to Gazebo Simulator - 6 (2 Hours Practice): RVIZ. Sensors data acquisition and robot interaction in Gazebo KNOWLEDGE AND UNDERSTANDING: acquiring the knowledge related to the sensors necessary for the autonomous navigation of a robot and to the representations of the maps; know the GAZEBO simulator and the libraries to move the robot manually APPLYING KNOWLEDGE AND UNDERSTANDING: knowing how to move the robot inside the Gazebo simulator and being able to acquire data from the sensors in simulation using the ROS framework Didactic unit 2: Map based localization algorithms (LECTURE/PRACTICE/LABORATORY HOURS 5/3/0) - 7 (3 Hours Lecture): Map based localization: an overview. Markov Based localization. Theory and example - 8 (2 Hours Lecture): 2D Kalman Filter. Kalman Filter for localization. Kalman Filter for differential robot - 9 (3 Hours Practice): Localization with Kalman Filter in Gazebo KNOWLEDGE AND UNDERSTANDING: knowing the localization algorithms based on Kalman Filter and particle Filtering APPLYING KNOWLEDGE AND UNDERSTANDING: knowing how to design and implement localization algorithms integrated into Gazebo. Knowing how to use ROS nodes already available in Gazebo for localization (EKF, AMCL) Didactic unit 3: Simultaneous Localization and Mapping algorithms (LECTURE/PRACTICE/LABORATORY HOURS 5/2/0) - 10 (2 Hours Lecture): Autonomous map building. SLAM Algorithm. EKF SLAM - 11 (3 Hours Lecture): Particle filtering. MonteCarlo localization based on particle Filtering. PF based SLAM - 12 (2 Hours Practice): SLAM in Gazebo KNOWLEDGE AND UNDERSTANDING: knowing the SLAM algorithms based on EFK and PF APPLYING KNOWLEDGE AND UNDERSTANDING: knowing how to use and configure the algorithms already available in Gazebo for SLAM (GMAPPING) Didactic unit 4: Navigation: Path planning and obstacle avoidance algorithms (LECTURE/PRACTICE/LABORATORY HOURS 5/5/8) - 13 (3 ORE Esercitazione): From Gazebo simulator to Turtlebot robot. - 14 (2 Hours Lecture): Navigation. Path planning Algorithms. Road Map, Cell Decomposition - 15 (3 Hours Lecture): Obstacle avoidance algorithms - 16 (2 Hours Practice): Navigation, Path Planning and Obstacle Avoidance - 17 (3 Hours Laboratory): Final Project - 18 (2 Hours Laboratory): Final Project - 19 (3 Hours Laboratory): Final Project KNOWLEDGE AND UNDERSTANDING: knowing the path planning algorithms (based on Road Map, Cell Decomposition) and obstacle avoidance algorithms APPLYING KNOWLEDGE AND UNDERSTANDING: Knowing how to use and configure the algorithms already available in Gazebo for navigation. Knowing how to integrate all the algorithms studied on the Turtlebot robotic platform (not in simulation) TOTAL LECTURE/PRACTICE/LABORATORY HOURS 25/15/8

Contents

Didactic unit 1: Introduction to map based navigation and map based representations
(LECTURE/PRACTICE/LABORATORY HOURS 10/5/0)
- 1 (3 Hours Lecture): Introduction to the course
- 2 (2 Hours Lecture): Sensors for mobile robots navigation: IMU, GPS, Laser Scan
- 3 (3 Hours Lecture): Behavior based vs map based navigation. Differential robot: motion model
- 4 (2 Hours Lecture): Map based representations
- 5 (3 Hours Practice): Introduction to Gazebo Simulator
- 6 (2 Hours Practice): RVIZ. Sensors data acquisition and robot interaction in Gazebo
KNOWLEDGE AND UNDERSTANDING: acquiring the knowledge related to the sensors necessary for the autonomous navigation of a robot and to the representations of the maps; know the GAZEBO simulator and the libraries to move the robot manually
APPLYING KNOWLEDGE AND UNDERSTANDING: knowing how to move the robot inside the Gazebo simulator and being able to acquire data from the sensors in simulation using the ROS framework

Didactic unit 2: Map based localization algorithms
(LECTURE/PRACTICE/LABORATORY HOURS 5/3/0)
- 7 (3 Hours Lecture): Map based localization: an overview. Markov Based localization. Theory and example
- 8 (2 Hours Lecture): 2D Kalman Filter. Kalman Filter for localization. Kalman Filter for differential robot
- 9 (3 Hours Practice): Localization with Kalman Filter in Gazebo
KNOWLEDGE AND UNDERSTANDING: knowing the localization algorithms based on Kalman Filter and particle Filtering
APPLYING KNOWLEDGE AND UNDERSTANDING: knowing how to design and implement localization algorithms integrated into Gazebo. Knowing how to use ROS nodes already available in Gazebo for localization (EKF, AMCL)

Didactic unit 3: Simultaneous Localization and Mapping algorithms
(LECTURE/PRACTICE/LABORATORY HOURS 5/2/0)
- 10 (2 Hours Lecture): Autonomous map building. SLAM Algorithm. EKF SLAM
- 11 (3 Hours Lecture): Particle filtering. MonteCarlo localization based on particle Filtering. PF based SLAM
- 12 (2 Hours Practice): SLAM in Gazebo
KNOWLEDGE AND UNDERSTANDING: knowing the SLAM algorithms based on EFK and PF
APPLYING KNOWLEDGE AND UNDERSTANDING: knowing how to use and configure the algorithms already available in Gazebo for SLAM (GMAPPING)

Didactic unit 4: Navigation: Path planning and obstacle avoidance algorithms
(LECTURE/PRACTICE/LABORATORY HOURS 5/5/8)
- 13 (3 ORE Esercitazione): From Gazebo simulator to Turtlebot robot.
- 14 (2 Hours Lecture): Navigation. Path planning Algorithms. Road Map, Cell Decomposition
- 15 (3 Hours Lecture): Obstacle avoidance algorithms
- 16 (2 Hours Practice): Navigation, Path Planning and Obstacle Avoidance
- 17 (3 Hours Laboratory): Final Project
- 18 (2 Hours Laboratory): Final Project
- 19 (3 Hours Laboratory): Final Project
KNOWLEDGE AND UNDERSTANDING: knowing the path planning algorithms (based on Road Map, Cell Decomposition) and obstacle avoidance algorithms
APPLYING KNOWLEDGE AND UNDERSTANDING: Knowing how to use and configure the algorithms already available in Gazebo for navigation. Knowing how to integrate all the algorithms studied on the Turtlebot robotic platform (not in simulation)

TOTAL LECTURE/PRACTICE/LABORATORY HOURS 25/15/8

	Teaching Methods
	THE COURSE CONTAINS THEORETICAL LECTURES, IN-CLASS EXERCITATIONS AND PRACTICAL LABORATORY EXERCITATIONS. DURING THE IN-CLASS EXERCITATIONS THE STUDENTS ARE DIVIDED IN TEAMS AND ARE ASSIGNED SOME PROJECT-WORKS TO BE DEVELOPED ALONG THE DURATION OF THE COURSE. THE PROJECTS INCLUDE ALL THE CONTENTS OF THE COURSE AND IS ESSENTIAL BOTH FOR THE ACQUISITION OF THE RELATIVE ABILITIES AND COMPETENCES, AND FOR DEVELOPING AND REINFORCING THE ABILITY TO WORK IN A TEAM. IN THE LABORATORY EXERCITATIONS THE STUDENTS IMPLEMENT THE ASSIGNED PROJECTS USING ROS. IN ORDER TO PARTICIPATE TO THE FINAL ASSESSMENT AND TO GAIN THE CREDITS CORRESPONDING TO THE COURSE, THE STUDENT MUST HAVE ATTENDED AT LEAST 70% OF THE HOURS OF ASSISTED TEACHING ACTIVITIES.

Teaching Methods

THE COURSE CONTAINS THEORETICAL LECTURES, IN-CLASS EXERCITATIONS AND PRACTICAL LABORATORY EXERCITATIONS. DURING THE IN-CLASS EXERCITATIONS THE STUDENTS ARE DIVIDED IN TEAMS AND ARE ASSIGNED SOME PROJECT-WORKS TO BE DEVELOPED ALONG THE DURATION OF THE COURSE. THE PROJECTS INCLUDE ALL THE CONTENTS OF THE COURSE AND IS ESSENTIAL BOTH FOR THE ACQUISITION OF THE RELATIVE ABILITIES AND COMPETENCES, AND FOR DEVELOPING AND REINFORCING THE ABILITY TO WORK IN A TEAM. IN THE LABORATORY EXERCITATIONS THE STUDENTS IMPLEMENT THE ASSIGNED PROJECTS USING ROS.

IN ORDER TO PARTICIPATE TO THE FINAL ASSESSMENT AND TO GAIN THE CREDITS
CORRESPONDING TO THE COURSE, THE STUDENT MUST HAVE ATTENDED AT LEAST 70% OF THE HOURS OF ASSISTED TEACHING ACTIVITIES.

	Verification of learning
	THE EXAM AIMS AT EVALUATING, AS A WHOLE: THE KNOWLEDGE AND UNDERSTANDING OF THE CONCEPTS PRESENTED IN THE COURSE, THE ABILITY TO APPLY THAT KNOWLEDGE TO SOLVE PROGRAMMING PROBLEMS REQUIRING THE USE OF TECHNIQUES FOR AUTONOMOUS ROBOT NAVIGATION; INDEPENDENCE OF JUDGMENT, COMMUNICATION SKILLS AND THE ABILITY TO LEARN. THE EXAM INCLUDES TWO STEPS: THE FIRST ONE CONSISTS IN AN ORAL EXAMINATIONS AND IN THE DISCUSSION OF MID TERM PROJECTS REALIZED DURING THE COURSES. THE SECOND STEP CONSISTS IN THE REALIZATION OF A FINAL TERM PROJECT: THE STUDENTS, PARTITIONED INTO TEAMS, ARE REQUIRED TO REALIZE A SYSTEM, FINALIZED TO A COMPETITION AMONG THE TEAMS, DESIGNING AND METHODOLOGICAL CONTRIBUTIONS OF THE STUDENTS, TOGETHER WITH THE SCORE ACHIEVED DURING THE COMPETITION, ARE CONSIDERED FOR THE EVALUATION. THE AIM IS TO ASSESS THE ACQUIRED KNOWLEDGE AND ABILITY TO UNDERSTANDING, THE ABILITY TO LEARN, THE ABILITY TO APPLY KNOWLEDGE, THE INDEPENDENCE OF JUDGMENT, THE ABILITY TO WORK IN A TEAM. IN THE FINAL EVALUATION, EXPRESSED IN THIRTIETHS, THE EVALUATION OF THE INTERVIEW AND OF THE MID TERM PROJECTS WORK WILL ACCOUNT FOR 40% WHILE THE FINAL TERM PROJECT WILL ACCOUNT FOR 60%. THE CUM LAUDE MAY BE GIVEN TO STUDENTS WHO DEMONSTRATE THAT THEY CAN APPLY THE KNOWLEDGE AUTONOMOUSLY EVEN IN CONTEXTS OTHER THAN THOSE PROPOSED IN THE COURSE.

Verification of learning

THE EXAM AIMS AT EVALUATING, AS A WHOLE: THE KNOWLEDGE AND UNDERSTANDING OF THE CONCEPTS PRESENTED IN THE COURSE, THE ABILITY TO APPLY THAT KNOWLEDGE TO SOLVE PROGRAMMING PROBLEMS REQUIRING THE USE OF TECHNIQUES FOR AUTONOMOUS ROBOT NAVIGATION; INDEPENDENCE OF JUDGMENT, COMMUNICATION SKILLS AND THE ABILITY TO LEARN.

THE EXAM INCLUDES TWO STEPS: THE FIRST ONE CONSISTS IN AN ORAL EXAMINATIONS AND IN THE DISCUSSION OF MID TERM PROJECTS REALIZED DURING THE COURSES. THE SECOND STEP CONSISTS IN THE REALIZATION OF A FINAL TERM PROJECT: THE STUDENTS, PARTITIONED INTO TEAMS, ARE REQUIRED TO REALIZE A SYSTEM, FINALIZED TO A COMPETITION AMONG THE TEAMS, DESIGNING AND METHODOLOGICAL CONTRIBUTIONS OF THE STUDENTS, TOGETHER WITH THE SCORE ACHIEVED DURING THE COMPETITION, ARE CONSIDERED FOR THE EVALUATION.
THE AIM IS TO ASSESS THE ACQUIRED KNOWLEDGE AND ABILITY TO UNDERSTANDING, THE ABILITY TO LEARN, THE ABILITY TO APPLY KNOWLEDGE, THE INDEPENDENCE OF JUDGMENT, THE ABILITY TO WORK IN A TEAM.

IN THE FINAL EVALUATION, EXPRESSED IN THIRTIETHS, THE EVALUATION OF THE INTERVIEW AND OF THE MID TERM PROJECTS WORK WILL ACCOUNT FOR 40% WHILE THE FINAL TERM PROJECT WILL ACCOUNT FOR 60%. THE CUM LAUDE MAY BE GIVEN TO STUDENTS WHO DEMONSTRATE THAT THEY CAN APPLY THE KNOWLEDGE AUTONOMOUSLY EVEN IN CONTEXTS OTHER THAN THOSE PROPOSED IN THE COURSE.

	Texts
	INTRODUCTION TO AUTONOMOUS MOBILE ROBOTS. ROLAND SIEGWART, ILLAH R. NOURBAKHSH, A BRADFORD BOOK, THE MIT PRESS, 2004 THE TEACHING MATERIAL IS AVAILABLE ON THE UNIVERSITY E-LEARNING PLATFORM (HTTP://ELEARNING.UNISA.IT) ACCESSIBLE TO STUDENTS USING THEIR OWN UNIVERSITY CREDENTIALS.