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.