Tesis Doctorales UPM: Consulta online

Título: SOCIAL ODOMETRY: DISTRIBUTED LOCATION KNOWLEDGE FOR SWARM ROBOTICS BASED ON LOCAL COMMUNICATION.

Resumen: In social insects, such as ants or bees, the individuals have no knowledge of the global state of the environment. There is no leader that guides all the other individuals to accomplish their goals. Instead, the knowledge of the swarm is distributed throughout all the agents. An individual is not able to accomplish its task without the rest of the group and the group is not able to do it without the help of the individuals. The goal of this Thesis is the design of intelligent behaviors for distributed collective robotic localization, in which social insect colonies serve as an inspiration. More specifically, we propose Social Odometry, a localization strategy inspired by social insect colonies, in which robots have no access to centralized information. Each robot has an estimate of its own location with respect to one or more landmarks, and an associated confidence level that decreases with the distance traveled. Robots use estimates advertised by neighboring robots to improve their own location estimate. This online social method allows a group of robots both to increase the quality of individuals' estimates and efficiently improve their collective performance. Furthermore, Social Odometry produces a successful self-organized collective pattern.