From a visual standpoint it is often easy to point out whether a system is considered to be self-organizing or not, though a quantitative approach would be more helpful. Information theory, as introduced by Shannon, provides the right tools not only quantify self-organization, but also to investigate it in relation to the information processing performed by individual agents within a collective. This thesis sets out to introduce methods to quantify spatial self-organization in collective systems in the continuous domain as a means to investigate morphogenetic processes. In biology, morphogenesis denotes the development of shapes and form, for example embryos, organs or limbs. Here, I will introduce methods to quantitatively investigate shape formation in stochastic particle systems. In living organisms, self-organization, like the development of an embryo, is a guided process, predetermined by the genetic code, but executed in an autonomous decentralized fashion. Information is processed by the individual agents (e.g. cells) engaged in this process. Hence, information theory can be deployed to study such processes and connect self-organization and information processing. The existing concepts of observer based self-organization and relevant information will be used to devise a framework for the investigation of guided spatial self-organization. Furthermore, local information transfer plays an important role for processes of self-organization. In this context, the concept of synergy has been getting a lot attention lately. Synergy is a formalization of the idea that for some systems the whole is more than the sum of its parts and it is assumed that it plays an important role in self-organization, learning and decision making processes. In this thesis, a novel measure of synergy will be introduced, that addresses some of the theoretical problems that earlier approaches posed.
Many tasks requiring multiple autonomous underwater vehicles (AUVs) are simple, with static goals, of short duration, and require few AUVs, often of the same type. Simple coordination mechanisms that assign roles to AUVs before the mission are sufficient for these multi-AUV systems. However, for tasks that are complex and dynamic, of long duration (implying that AUVs will come and go during the mission), and that have many heterogeneous AUVs, organization of the system will not work. In addition, due to changes in the situation, the system will likely need to be reorganized during the mission. We are developing a distributed, context-aware self-organization/reorganization scheme for advanced multi-AUV systems. This is a two-level approach in which a meta-level organization first self-organizes, assesses the context, and uses contextual knowledge to design a task-level organization appropriate for the context that can then carry out the mission. We are extending our prior work by distributing both the context assessment process and the organization design process. The result will be a system that can self-organize efficiently and effectively for its context and that can reorganize appropriately as the context changes.
In recent decades, there has been an increasing interest in systems comprised of several autonomous mobile robots, and as a result, there has been a substantial amount of development in the eld of Articial Intelligence, especially in Robotics. There are several studies in the literature by some researchers from the scientic community that focus on the creation of intelligent machines and devices capable to imitate the functions and movements of living beings. Multi-Robot Systems (MRS) can often deal with tasks that are dicult, if not impossible, to be accomplished by a single robot. In the context of MRS, one of the main challenges is the need to control, coordinate and synchronize the operation of multiple robots to perform a specic task. This requires the development of new strategies and methods which allow us to obtain the desired system behavior in a formal and concise way. This PhD thesis aims to study the coordination of multi-robot systems, in particular, addresses the problem of the distribution of heterogeneous multi-tasks. The main interest in these systems is to understand how from simple rules inspired by the division of labor in social insects, a group of robots can perform tasks in an organized and coordinated way. We are mainly interested on truly distributed or decentralized solutions in which the robots themselves, autonomously and in an individual manner, select a particular task so that all tasks are optimally distributed. In general, to perform the multi-tasks distribution among a team of robots, they have to synchronize their actions and exchange information. Under this approach we can speak of multi-tasks selection instead of multi-tasks assignment, which means, that the agents or robots select the tasks instead of being assigned a task by a central controller. The key element in these algorithms is the estimation ix of the stimuli and the adaptive update of the thresholds. This means that each robot performs this estimate locally depending on the load or the number of pending tasks to be performed. In addition, it is very interesting the evaluation of the results in function in each approach, comparing the results obtained by the introducing noise in the number of pending loads, with the purpose of simulate the robot's error in estimating the real number of pending tasks. The main contribution of this thesis can be found in the approach based on self-organization and division of labor in social insects. An experimental scenario for the coordination problem among multiple robots, the robustness of the approaches and the generation of dynamic tasks have been presented and discussed. The particular issues studied are: Threshold models: It presents the experiments conducted to test the response threshold model with the objective to analyze the system performance index, for the problem of the distribution of heterogeneous multitasks in multi-robot systems; also has been introduced additive noise in the number of pending loads and has been generated dynamic tasks over time. Learning automata methods: It describes the experiments to test the learning automata-based probabilistic algorithms. The approach was tested to evaluate the system performance index with additive noise and with dynamic tasks generation for the same problem of the distribution of heterogeneous multi-tasks in multi-robot systems. Ant colony optimization: The goal of the experiments presented is to test the ant colony optimization-based deterministic algorithms, to achieve the distribution of heterogeneous multi-tasks in multi-robot systems. In the experiments performed, the system performance index is evaluated by introducing additive noise and dynamic tasks generation over time.
Toshio Fukuda, Go Iritani, Fumihito Arai, Koji Yamada
In this research, we address the organization of group behavior on decentralized autonomous robotic systems. Collective group behavior is exhibited in the natural world, such as by ants and fish, in teamwork in sports and by the human society. Therefore, research on group behavior of decentralized autonomous robotic systems can be regarded as one the research fields of Artificial Life. Decentralized autonomous robotic systems refer to multiple robotic systems including many autonomous robots, such as the Cellular Robotic System (CEBOT). The CEBOT, which has been studied by the authors, consists of a number of robotic units called cells. In research on the CEBOT, it is necessary to evolve a cooperative group behavior effectively in the system, since a well-organized group behavior is required to carry out given tasks efficiently and influences its performance ability. In order to organize the behavior in a dynamic environment, we proposed a concept of the self-recognition for the decision making of the behavior in a robotic group. In addition to the proposed concept, this paper will show a construction mechanism of group behavior using the character of the attractor. Based on this idea, we present the behavioral evolution of a group robotic system.
This paper deals with the self-organization of a group of multiple robotic systems consisting of several or a number of autonomous robots, such as the Cellular Robotic System (CEBOT). In paticular, in this paper, we will present the self-organization of the hierarchical structure of the CEBOT as one of the decentralized autonomous robotic systems. The structural organization is simulated based on the idea of interdependence between autonomous robots, which is derived from the model of group organization of human beings. That is, groups are organized and leaders emerge from the interdependence of the individuals. Additionaley, an evaluation method for the hierarchical architecture is also discussed to evaluate the structure of the whole system. This paper will represent simulation results of self-organization of hierarchical structure on the basis of a given task model. The sensitivity of the organization of the decentralized autonomous robotic systems is also discussed in the simulation.
In this research, we address the organization of group behavior on decentralized autonomous robotic systems. Collective group behavior is exhibied in the natural world, such as by ants and fish, in teamwork in sports and by the human society. Therefore, research on group behavior of decentralized autonomous robotic systems can be regarded as one of the research fields of Artificial Life. Decentralized autonomous robotic systems refer to multiple robotic systems including many autonomous robots, such as the Cellular Robotic System (CEBOT). The CEBOT, which has been studied by the authors, consists of a number of robotic units called "cells". In the research on the CEBOT, it is necessary to evolve a cooperative group behavior effectively in the system, since a well-organized group behavior is required to carry out given tasks efficiently and influences its perfor-mance ability. In order to organize the behavior in a dynamic environment, we proposed a concept of "self-recognition" for decision making of the behavior in a robotic group. In this paper, in addition to the proposed concept, we will show the organization and adaptation of group behavior with the coordination of intention, and represent some simulation results with the coordination of intention.