SwarmCity was an idea that came up during a conversation between my colleague Pablo and me in the summer of 2017. We did not stay there, but planned to make it real: we shaped the objectives  (a city, a swarm and an interface) and formed a team with 11 people (a professor, 2 PhD, 2 MSc and 6 BSc students). During the academic year 2017-2018, we have been working hard to develop the city simulator, swarm intelligence and control interface. The next academic year we have a clear objective: Make SwarmCity great again!

You may ask... What is SwarmCity

Future cities will be bigger and their management more complex, posing challenges related to the traffic, management of resources, maintenance of green zones, pollution, etc. The aim of this project is to provide a tool to collect the relevant information of these cities to allow their authorities to make correct and efficient decisions. The proposal goes further than the current solutions based on fixed sensors, making use of an aerial robot swarm to obtain information at desired locations and times. The work is focused on the development of swarming algorithms to solve complex tasks through the combination of simple individual behaviors, as well as data mining techniques and immersive interfaces for processing and visualizing the information. Moreover, the scenario will be used as a testbed for new algorithms designed to create an emergent and distributed intelligence that will learn from the environment.

In other words, SwarmCity is the sum of a smart city, a robot swarm and a control interface:
  • The smart city has been developed by using Unity game engine. It is a simulator of a scaled city that includes traffic, pedestrians, garbage, climate and pollution. It is used not only as a view tool but also as a data source.
  • The robot swarm has to cover the city collecting data about the traffic jams, people crowds, contaminant emissions... For this purpose, we are going to study behavior-based architectures, multiple techniques of optimization, game theoretical decision making...
  • The control interface must allow the operators to monitor the state of the city, as well as to configure the swarm. For this purpose, we are going to explore data mining techniques to discover information, machine learning algorithms to adapt it to operator and immersive technologies to show it in a easy to understand way.