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.
In recent years, formation flying has been recognized as an enabling technology for a variety of mission concepts in both the scientific and defense arenas. Examples of developing missions at NASA include magnetospheric multiscale (MMS), solar imaging radio array (SIRA), and terrestrial planet finder (TPF). For each of these missions, a multiple satellite approach is required in order to accomplish the large-scale geometries imposed by the science objectives. In addition, the paradigm shift of using a multiple satellite cluster rather than a large, monolithic spacecraft has also been motivated by the expected benefits of increased robustness, greater flexibility, and reduced cost. However, the operational costs of monitoring and commanding a fleet of close-orbiting satellites is likely to be unreasonable unless the onboard software is sufficiently autonomous, robust, and scalable to large clusters. This paper presents the prototype of a system that addresses these objectives-a decentralized guidance and control system that is distributed across spacecraft using a multiple team framework. The objective is to divide large clusters into teams of "manageable" size, so that the communication and computation demands driven by N decentralized units are related to the number of satellites in a team rather than the entire cluster. The system is designed to provide a high level of autonomy, to support clusters with large numbers of satellites, to enable the number of spacecraft in the cluster to change post-launch, and to provide for on-orbit software modification. The distributed guidance and control system will be implemented in an object-oriented style using a messaging architecture for networking and threaded applications (MANTA). In this architecture, tasks may be remotely added, removed, or replaced post launch to increase mission flexibility and robustness. This built-in adaptability will allow software modifications to be made on-orbit in a robust manner. The prototype system, which is implemented in Matlab, emulates the object-oriented and message-passing features of the MANTA software. In this paper, the multiple team organization of the cluster is described, and the modular software architecture is presented. The relative dynamics in eccentric reference orbits is reviewed, and families of periodic, relative trajectories are identified, expressed as sets of static geometric parameters. The guidance law design is presented, and an example reconfiguration scenario is used to illustrate the distributed process of assigning geometric goals to the cluster. Next, a decentralized maneuver planning approach is presented that utilizes linear-programming methods to enact reconfiguration and coarse formation keeping maneuvers. Finally, a method for performing online collision avoidance is discussed, and an example is provided to gauge its performance.
Decentralized control methods are appealing in coordination of multiple vehicles due to their low demand for long-range communication and their robustness to single-point failures. In this paper we explore a decentralized approach to path generation for a group of vehicles in a battlefield scenario. The mission is to maneuver the vehicles to cover a target area while avoiding obstacles and threats during the maneuver. Each vehicle makes its moving decision by minimizing a potential function that encodes information about its neighbours, obstacles, threats and the target. Preliminary analysis of vehicle behaviors is conducted. Simulation has shown that this approach leads to interesting emergent behaviors, and the behaviors can be varied by adjusting the weighting coefficients of different potential function terms.
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.
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.