Monitoring traffic in future cities with aerial swarms: developing and optimizing a behavior-based surveillance algorithm

SwarmCity Project has its first journal publication!

The paper is entitled "Monitoring traffic in future cities with aerial swarms: developing and optimizing a behavior-based surveillance algorithm" and has been published by Cognitive Systems Research.

Traffic monitoring is a key issue to develop smarter and more sustainable cities in the future, allowing to make a better use of the public space and reducing pollution. This work presents an aerial swarm that continuously monitors traffic in SwarmCity, a simulated city developed in Unity game engine where drones and cars are modeled in a realistic way. The control algorithm of the aerial swarm is based on six behaviors with twenty-three parameters that must be tuned. The optimization of parameters is carried out with a genetic algorithm in a simplified and faster simulator. The best resulting configurations are tested in SwarmCity showing good efficiencies in terms of observed cars over total cars during time windows. The algorithm reaches a good performance making use of an acceptable computational time for the optimization.

P. Garcia-Aunon, J.J. Roldán and A. Barrientos. “Monitoring traffic in future cities with aerial swarms: developing and optimizing a behavior-based surveillance algorithm”. Cognitive Systems Research, 2018. Article

Download the article!

SwarmCity: control and monitoring of cities with aerial swarms

Our student Diego Soto Martín has presented his MSc thesis "SwarmCity: control y monitorización de ciudades con enjambres aéreos" ("SwarmCity: control and monitoring of cities with aerial swarms").

During this academic year, he has worked to provide functionalities to the swarm. Specifically, he has developed a behavior-based architecture using IB2C library that allows the drones to perform multiple tasks. Among these tasks, we can find go to points, avoid obstacles, detect and follow cars and people, search the gradients of temperature and pollution, and fly in formation. Some videos are shown below...

Go to point and avoid obstacles:

Follow a car:

Follow a gradient of pollution:

Algorithms for resource collection through ground robot swarms

Our student Fernando Cipriano Díaz has presented his BSc thesis "Algoritmos de recolección de recursos mediante enjambres de robots terrestres" ("Algorithms for resource collection through ground robot swarms").

This work has explored some of the key points of SwarmCity project, but developing and applying them in a foraging scenario with a ground swarm. Specifically, fourteen strategies have been developed to lead the robots to collect the resources, taking into account behaviors such as random move, come back to resources, area coverage, manage energy... These strategies have been developed as iB2C networks and implemented in Python with py-iB2C library.

The developed strategies have been integrated in ROS and tested in ARGoS, which is one of the most common simulators for multi-agent systems and robot swarms. This simulator has been used to evaluate the strategies with multiple fleets (from 5 to 25 robots) and resources (random, clustered and combined distribution). The results allow to compare the strategies in terms of resources collected in a certain time and time required to collect all the resources.

This work has familiarized us with behavior-based architectures, as well as has shown their potential in a widely-used testbed. We will work in the following months to transfer the results of the work to SwarmCity.

PyiB2C: The python implementation of iB2C

Py-iB2C has been released!

This is the Python implementation of iB2C (integrated Behavior-Based Control) developed by Pablo García Auñón. This architecture can be used to develop and execute behavioral networks to control intelligent agents. The Python library allows to easily create behaviors, implement them in networks and execute them in simulators.

The main functionalities are listed below:

• Create new behaviors from scratch, implementing the transfer, activity and rating functions.
• Create networks using the already implemented behaviors or new ones.
• Save specific behaviors and reuse them in another network.
• Save networks and use them as behavior modules.

Download the code!

Visit the wiki!

3, 2, 1... Go!

SwarmCity Project has started with a first meeting at the Centre for Automation and Robotics (UPM-CSIC). The 11 members of the team (1 professor, 2 PhD students, 2 MSc students and 6 BSc students) have met for the first time. In this meeting, we have presented the project and discussed its work packages. In the following ones, we are going to teach students on multi-agent systems, behavior-based architectures and machine learning. 

Seven students will develop their MSc and BSc under SwarmCity Project. Some of them will develop components related to the city, swarm and interface, whereas other ones will work in other relevant environments such as the Robocup.

• SwarmCity: control and monitoring of cities with aerial swarms (MSc thesis)
• SwarmCity: Applying game theory to the management of drone swarms (BSc thesis)
• SwarmCity: visual recognition of targets in the city (BSc thesis)
• Treatment and visualization of urban data obtained by a drone swarm (BSc thesis)
• Attack and defense techniques with swarms of heterogeneous drones (BSc thesis)
• Algorithms for resource collection through ground robot swarms (BSc thesis)
• Creation of strategies for robotic football teams (BSc thesis)

Good luck to everyone!

SwarmCity: monitoring future cities with intelligent flying swarms

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 monitorize 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.