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Background: The technological advancement of the past decade have led to a widespread deployment of mobile robots in several application scenarios, including search and rescue, agriculture, delivery. These robots are equipped with sensing and computational resources, and linked by a wireless network, enabling them to cooperatively execute different tasks. One of these tasks is to map and navigate an unknown environment, for example when the robots are deployed for search and rescue in disaster areas. This can be accomplished in a cooperative fashion, with the different robots collecting sensor measurements and then fusing them in a single map, represented by a neural network.

Objective: The project focuses on the development of efficient cooperative learning techniques to construct the map of an unknown environment. The roadmap of the project is as follows: 1) guided literature review, 2) application and comparison of different cooperative learning algorithms, 3) numerical evaluations in realistic robot exploration scenarios. The project has the potential to lead to conference and journal publications in top venues.

We are looking for 1 student to join this project. Requirements are: some background on machine learning, good programming skills (Python), and optionally background on optimization.

The start date is flexible, and the project will take around 20 weeks.

The project will be conducted in the Division of Decision and Control Systems, with flexible remote work, under the supervision of Dr. Nicola Bastianello (nicolba@kth.se).

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