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Programme of Course "Intelligent Systems And Robotics Laboratory"

Code:

DT0201

Type of course unit:

Bachelor Degree in Computer Science curriculum General: Elective
Master Degree in Computer Science curriculum NEDAS: Elective
Master Degree in Computer Science curriculum UBIDIS: Elective

Level of course unit:

Undergraduate Degrees
Postgraduate Degrees

Semester:

1st semester

Number of credits:

Bachelor Degree in Computer Science: 6 (workload 150 hours)
Master Degree in Computer Science: 6 (workload 150 hours)

Teachers:

Giovanni De Gasperis (giovannidotdegasperisatunivaqdotit)

1. Course Objectives

The future master graduate on ICT will have a hands-on experience with a project involving most of the technologies of the sector, with particular focus on cognitive robotics.

2. Course Contents and learning outcomes (Dublin Descriptors)

Topics of the course include:

  • Advanced network programming with Python / PyPy with Flask, Falcon extension libraries
  • Using open source tools for cloud computing, no-sql servers, asynchronous inter-process communication
  • Using a virtualization / back end simulation system on personal cloud computing
  • Experimenting with virtual physical simulation environments and virtual worlds
  • In-depth study of the Robotic Operation System R.O.S. and YARP middleware, DDS.
  • Application of the extended DALI framework (PyDALI) for Multiagent Systems in controlling real or virtual robotic systems in cloud computing.
  • Control of the anthropomorphic NAO robot through the Coreographe environment

On successful completion of this course, the student should

  • Know how to design a cognitive robotics application, know how to implement a working prototype by means of virtualization and cloud computing techniques and to validate the impalementation on real roboti platform.

3. Course Prerequisites

The course will use knowledge about: • Object oriented programming • Interprocess communication by TCP/UDP sockets or more • Software development experience in team work, especially for network distributed applications • topics addressed by the Artificial Intelligence course • DALI extended framework for multi agent systems • Unix or GNU/Linux bash shell bash • Linux kernel

4. Teaching methods and language

Lectures, collaborative learning activity through the e-Learning platform, laboratory sessions.

Language:English[info]

Reference textbooks

  • Learning Robotics Using Python, Lentin Joseph. PACKT Publishing.
  • Joseph Howse, OpenCV Computer Vision with Python. PACKT Publishing.
  • Robotics Operative System User Manual (online).

5. Assessment Methods

Written exam about the Prolog language and oral discussione of a small robotics project on real or virtual cognitive robotics.

Course information last updated on: 18 ottobre 2017, 17:43