Methodologies of Mobile Robotics

Recommended Prerequisites

Advanced engineering mathematics; Programming; Robotics; Control theory.

Teaching Methods

Tutorial and seminar classes. Assessment: practical work with elaboration of a technical report and final presentation.

Learning Outcomes

After attending this course, students should have acquired the scientific and technical knowledge required for analysis and design of methods for perception and navigation in mobile robots, autonomous vehicles, and multi-robot cooperative systems; as well as computational intelligence methodologies applied in this domain.

Acquisition of skills in a research environment, such as analysis and synthesis, autonomous learning, adaptability to new contexts, and applying in practice the theoretical knowledge.

Work Placement(s)

No

Syllabus

Mobile robots and autonomous vehicles. Mapping, localization and navigation methods. Sensing, multi-sensor fusion and perception. Detection and tracking of objects, and local navigation. Multi-robot systems, cooperative perception, multi-robot coordination and decentralized decision-making. Computational intelligence and reinforcement learning techniques applied to mobile robots and autonomous vehicles.

Head Lecturer(s)

Urbano José Carreira Nunes

Assessment Methods

Assessment
Research work: 100.0%

Bibliography

- Kelly, K. (2014), Mobile Robotics: Mathematics, Models, and Methods, Cambridge University Press.

- Siegwart, S., Nourbakhsh, I.R. Scaramuzza, D. (2011), Introduction to Autonomous Mobile Robots, 2nd edition, The MIT Press.

- Thrun, S., Burgard, W., Fox, D. (2005), Probabilistic Robotics, The MIT Press, 2005.

- Balch, T., Parker, L. (2002), Robot Teams: From Diversity to Polymorphism, A.K. Peters.

- Kagan, E., Shvalb, N., Ben-Gal, I. (2020), Autonomous Mobile Robots and Multi-Robot Systems: Motion-Planning, Communication, and Swarming, Wiley.

- Haykin, S. (2009), Neural Networks and Learning Machines, 3rd Edition, Pearson.

- Sutton, R.S., Barto, A.G. (2018), Reinforcement Learning: An Introduction, 2nd Edition, The MIT Press.

- Murphy, K.P. (2021), Probabilistic Machine Learning: An Introduction, The MIT Press.

- Aggarwal, C.C. (2018), Neural Networks and Deep Learning: A Textbook, Springer.