Nowadays, more attention is put on solving tasks with more than one robotic platform. We can observe many publications solving this problem in a static world where the only moving objects are controlled agents. However, in reality, robots have to operate in the presence of other moving agents. In this thesis, your task is to develop a method that would solve the multi-robot task allocation problem considering a dynamic environment. 

[1]F. Surma, T. P. Kucner, and M. Mansouri, “Multiple Robots Avoid Humans To Get the Jobs Done: An Approach to Human-aware Task Allocation,” in 2021 European Conference on Mobile Robots (ECMR), Bonn, Germany, Aug. 2021, pp. 1–6. doi: 10.1109/ECMR50962.2021.9568843. 

This course on Mobile Robotics provides an in-depth exploration of key concepts and advanced techniques essential for the development of autonomous mobile robots. Students will learn about robotic locomotion, sensing and perception, and the probabilistic approaches to mapping and localization. The curriculum covers contemporary software tools, simultaneous localization and mapping (SLAM), inertial navigation systems (INS), and global navigation satellite systems (GNSS). Additionally, it delves into task and motion planning, as well as control systems that ensure precise and efficient robot operation. By the end of the course, students will be proficient in designing, implementing, and evaluating sophisticated mobile robotic systems, preparing them for cutting-edge research and careers in robotics.

LEARNING OUTCOMES

Upon completion of this course, the student will be able to design a comprehensive, high-level architecture for a mobile robotic system capable of addressing diverse challenges in the domain of field, indoor, service, agricultural robotics and similar, as well as address some challenges in the field of and autonomous vehicles.

This proficiency will encompass the ability to:

• Identify and name the primary challenges encountered by mobile robots in their respective fields and propose effective solutions.
• Illustrate the composition of a robotic system well-suited to the specific problem, involving the selection of appropriate subsystem instances. The student will be able to present the interplay between these subsystems, utilizing accurate terminology.
• Assess, select, and, to a limited extent, apply and implement fundamental methodologies and algorithms relevant to the identified challenges.

Study Material 

• Alonzo Kelly, CMU, Mobile Robotics: Mathematics Models and Methods, Cambridge University Press, 2014; 
• Trun & al, Probabilistic robotics, MIT Press 2005; 
• Siegwart, Nourbakhsh, Introduction to Autonomous Mobile Robots, MIT Press (2nd ed.)

This course is designed to provide students with hands-on, practical experience through collaborative project work. It emphasizes teamwork, project management, and real-world application of engineering concepts, preparing students for professional challenges in the field.

Course Structure:

• The core of this course involves group projects consisting of 4-5 students. These projects foster teamwork and hands-on application of theoretical knowledge.
• All topics are proposed by the EEA department, ensuring relevance and expert guidance. Students cannot propose their own topics.
• Each project is supported by an expert instructor from the department, providing valuable insights and guidance throughout the course.
• Before the course kick-off lecture, students will find a list of project topics in the "Project topics" section. Each student should select five topics based on their preferences.
• The teachers' board will form groups after an overall assessment, taking into account student preferences and available positions.
• Within the first 2-3 weeks, each group will select a project manager from among themselves.
•  Midway through the course, there will be a business case exercise related to your project topic, supplemented with relevant lectures.

• The Final Gala marks the completion of all technical work on the projects. Following this, students will have about one week to complete their final report.

  
  Assessment:

  The course grade is a composite of self-assessment, peer-evaluation within the project group, and teacher assessment. These evaluations occur twice: once after week 12 and again after week 21. Each student will receive a personal grade ranging from 1 to 5.

  
  This course not only aims to enhance your technical skills but also to develop your ability to work effectively in teams, manage projects, and apply business insights to technical problems. Join us for an engaging and enriching learning experience!