In this paper, we propose an emotional model for robots in a multi-robot system, in order to allow emerging behaviors. The emotional model uses four universal emotions: anger, disgust, sadness, and joy, assigned to each robot based on the level of satisfaction of its basic needs. These four universal emotions lie on a spectrum where depending where the emotion of the robot lies, can affect its behavior and of its neighboring robots. The more negative the emotion is, the more individualistic it becomes in its decisions (anger, sadness or disgust). The more positive the robot is in its emotion, the more it will consider the group and global goals (joy). Each robot is able to recognize another robot′s emotion in the system based on their current state, using the AR2P (AR2P for its acronym in Spanish: Algoritmo Recursivo de Reconocimiento de Patrones) recognition algorithm. In this way, it can use this information of the emotions to decide with whom collaborate. Specifically, the paper addresses emotions’ influence on the behavior of the system, at the individual and collective levels, and the emotions’ effects on the emergent behaviors of the multi-robot system. The paper explores the emerging behavior in two multi-robot scenarios; nectar harvesting and object transportation. The results show that the emotions are important to the emergent behavior in a multi-robot system.

A control architecture for heterogeneous robot swarms (AMEB) is proposed in this work. The architecture manages the processes that occur in the system and its main objectives are to allow the emergence and self-organization in the group. It is structured in three levels: an individual level, a collective level under the philosophy of emergent behavior, and a level for the management of learning and knowledge. The architecture includes a behavioral component that allows the inclusion of emotions in the members of the swarm. Finally, it described a method for verifying the occurrence of the emergence in the swarm, using fuzzy cognitive maps.

In this paper, we study the emergent behaviors in a multi-robot system. The multi-robot system uses a model for decision making that is composed of three levels: one individual, one collective, and another for the knowledge and learning management. In particular, the individual level, the base of the emergent behavior of the system, is composed of a module of perception/interpretation, an executing module and a behavioral module that has an emotional component, a reactive component, a cognitive component and a social component. In this paper, we analyze the robot performance, in order to produce an emergent behavior in the system. We present an example of an emergent scenario, and study its instantiation in our multi-robot architecture.

This paper shows the robustness path planning for mobile robots in indoors environments. The planning includes an accurate uncertainty in position analysis and the probability of success calculation as a path quality measurement. A topological and metric environment model, for the environment representation, is proposed. This model includes localization and risk transit zones. During the planning process, all the paths to complete a mission, are generated. Obtaining parameters such as: goal uncertainty, path feasibility, path distance and path uncertainty. These parameters allow the most adequate paths selection to fulfil a mission.