This paper examines the current state of smart homes and proposes an alternative model based on biomimicry. It is argued that a house that is modeled on a basic living organism will be more efficient for the inhabitants, and more effective to insulate them from the unpredictable effects of climate change in the near future. By using an organism as a model, the house will be able to self-organize its systems, and adapt to both its inhabitants as well as environmental perturbations. This can be accomplished with the use of sensors and actuators in a decentralized configuration with artificial life programming. Since organisms are autonomous by definition, off-grid housing systems are infused to create a new housing model that is zero-emission, zerowaste, and can serve as a model for other forms of infrastructure at greater scales.
Daniel Marolt, JĂŒrgen Scheible, Göran Jerke, Vinko Marolt
This paper enhances SWARM, a novel deterministic analog layout automation approach based on the idea of cellular automata. SWARM implements a decentralized interaction model in which responsive layout modules, covering basic circuit types, autonomously move, rotate and deform themselves to let constraint-compliant, compact layout solutions emerge from a synergetic flow of self-organization. With the ability to consider design constraints both implicitly and explicitly, SWARM joins the layout quality of procedural generators with the flexibility of optimization algorithms, combining these two kinds of automation into a âbottom-up meets top-downâ flow. The new enhancements are demonstrated in an OTA example, depicting the power of SWARM and its enormous potential for future developments.
Modern ocean exploration and sensing approaches have been mainly based on Autonomous Underwater Vehicles (AUVs), Remotely Operated Vehicles (ROVs), and/or static Underwater Acoustic Sensor Networks (UASNs) deployments. Individual AUVs and ROVs represent a single point of failure in addition to being bulky and expensive as vehicles are usually full-featured and sophisticated. UASNs have traditionally been statically deployed. This limits their use to original deployment locations and renders them unsuitable for search tasks. Swarm Robotics (SR) are a natural, better alternative. Swarms possess superior features over a sophisticated AUV; they are smaller, cheaper, robust, reliable, and scalable by design and definition. They also have the sensing capabilities of UASNs and built-in active mobility.\nDesigning successful swarm missions in harsh aquatic environments is an involved task. We address this by analyzing the indispensable stages of a typical mission and carefully designing decentralized algorithms to achieve the desired per-stage goals. Important system and environmental parameters are taken into consideration to achieve the end goal: completing mission requirements while respecting time constraint, with best possible performance and minimum loss of agents. Special attention is given to target search, task identification and allocation, and mission-stage integration due to their importance. Identifying target location in an unbounded environment is challenging. Bandwidth limited and intermittent communication complicates the process further. Therefore, we develop global search algorithms that use minimal communication and utilize flocking to maintain cohesion. These algorithms have multiple advantages over traditional ones in terms of convergence time, omni-directionality, consideration of physical constraints, and being self-bounding. At the target, tasks are autonomously identified and allocated in a completely decentralized manner. Validation of the developed techniques is done through realistic simulations and analytical comparisons.\nOur main contributions are: 1) a general framework for underwater mission planning, 2) three novel global search algorithms for unbounded underwater environment, 3) an algorithm for initial self-organization, 4) an optimized same-position reorientation algorithm for use in certain mission stages, 5) three autonomous task allocation algorithms, 6) three local target search algorithms, 7) a measure of mission utility, and 8) the design of a human brain-inspired model to support learning and complete autonomy.
Self-organization provides a suitable model for developing self-managed complex distributed systems, such as grid computing and sensor networks. Unlike current related studies, which propose only a single principle of self-organization, this mechanism synthesizes the three principles of self-organization: cloning/ spawning, resource exchange and relation adaptation. Based on this mechanism, an agent can autonomously generate new agents when it is overloaded, exchange resources with other agents if necessary, and modify relations with other agents to achieve a better agent network structure. In this way, agents can adapt to dynamic environments. The proposed mechanism is evaluated through a comparison with three other approaches, each of which represents state-of-the-art research in each of the three self-organization principles. Experimental results demonstrate that the proposed mechanism outperforms the three approaches in terms of the profit of individual agents and the entire agent network, the load-balancing among agents, and the time consumption to finish a simulation run. In addition, in a dynamic environment, it is nearly impossible to use a static, design time generated system structure for efficient problem solving. Instead, the system needs to be able to self-organize at runtime, which means that the components of the system are responsible for adapting themselves to suit the dynamic environment. Self-organization is usually defined as "the mechanism or the process enabling the system to change its organization without explicit external command during its execution time.
The theory of cognitive development from Jean Piaget (1923) is a constructivist perspective of learning that has substantially influenced cognitive science domain.Indeed it seems that constructivism is a possible trail in order to overcome the limitations of classical techniques stemming from cognitivism or connectionism and create autonomous agents, fitted with strong adaptation ability within their environment, modelled on biological organisms.Potential applications concern intelligent agents in interaction with a complex environment, with objectives that cannot be predefined.There are numerous interesting works in developmental robotics going in this direction.In this work we investigate the application of these principles to a close domain: Ambient intelligence, which is extremely challenging but which also presents interesting aspects to exploit, like the participation of human users.From the perspective of a constructivist theory, the learning agent has to build a representation of the world that relies on the learning of sensori-motor patterns starting from its own experience only.This step is difficult to set up for systems evolving in continuous environments, using raw data from sensors without a priori modelling, primarily because they face a bootstrap problem.In this paper we address this particular issue and propose a decentralized approach based on a multi-agent framework, where the system's representations are constructed through a self-organization process that handles the dynamics between experience discretization and learning.
Engineers are torn between an attitude of strong design and dreams of autonomous devices. They want full mastery of their artifacts while wishing these were much more adaptive or âintelligent.â Today, while we must still spoon-feed (program, repair, upgrade) our most sophisticated computer and robotic systems, insatiable demand for novelty has created an escalation in system size and complexity. In this context, the tradition of rigid top-down planning and implementation in every detail has become unsustainable. Natural complex systems, large sets of elements interacting locally and producing nontrivial collective behaviors, offer a powerful alternative and source of innovative ideas. Going beyond metaheuristic disciplines based on âneuronsâ (machine learning), âgenesâ (genetic algorithms), or âantsâ (ant colony optimization), this article highlights a new avenue of bioinspired engineering that simulates the growth of fine-grained multicellular organisms. It presents a brief overview of morphogenetic engineering and one of its instances, embryomorphic engineering, which are two fields that explore the decentralized self-organization of artificial complex morphologies and behaviors. MapDevo3D, an embryomorphic engineering model of developmental animats in a 3D virtual physics world, is described in more detail. Bodies are composed of several hundreds of cells, giving them a quasi-continuous texture close to the tenets of âsoft robotics.â Motion results from local muscle twitching without a central nervous system. Altogether, the challenge is not to build a system directly but find the rules that its components must follow to build it for us.
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.
The Internet of Things (IoT) is emerging as the major trend in shaping the development of the next generation of information networks. The challenges of the enormous, dynamic, incredibly diverse and high complexity of the IoT urgently require novel self-organization scheme because most of the existing distributed self-organization schemes cannot be directly applied to it. In this paper, we propose an intelligent self-organizing scheme (ISOS) for the IoT inspired by the endocrine regulating mechanism. For each node in the network, an autonomous area is established, where the node can effectively interact with its peers and perform self-control according to its own status and dynamic circumstance in a decentralized infrastructure. By introducing the hormone mechanism as the medium for information transmission and data sharing, the nodes can collaborate with each other and work in a cooperative way. Through adjusting the release procedure of the hormones, the ability to effectively detect service randomly generated can also be guaranteed in the probabilistic partially-working IoT. Simulation results verify the performance of the proposed mechanism that entitles the IoT to the ability of maintaining its status in a globally stable status, while effectively discovering the random service requests in a resource-critical configuration. The ISOS would be of great significance for the practical implementation of the IoT.
Tomassino Ferrauto, Domenico Parisi, Gabriele Di Stefano, Gianluca Baldassarre
Organisms that live in groups, from microbial symbionts to social insects and schooling fish, exhibit a number of highly efficient cooperative behaviors, often based on role taking and specialization. These behaviors are relevant not only for the biologist but also for the engineer interested in decentralized collective robotics. We address these phenomena by carrying out experiments with groups of two simulated robots controlled by neural networks whose connection weights are evolved by using genetic algorithms. These algorithms and controllers are well suited to autonomously find solutions for decentralized collective robotic tasks based on principles of self-organization. The article first presents a taxonomy of role-taking and specialization mechanisms related to evolved neural network controllers. Then it introduces two cooperation tasks, which can be accomplished by either role taking or specialization, and uses these tasks to compare four different genetic algorithms to evaluate their capacity to evolve a suitable behavioral strategy, which depends on the task demands. Interestingly, only one of the four algorithms, which appears to have more biological plausibility, is capable of evolving role taking or specialization when they are needed. The results are relevant for both collective robotics and biology, as they can provide useful hints on the different processes that can lead to the emergence of specialization in robots and organisms.
Raphael Maas, Erik Maehle, Karl-Erwin Grosspietsch
This paper presents an overview of the Organic Robot Control Architecture (ORCA) and its previous applications. The architecture supports the decentralized operation and organization of autonomous subsystems and the avoidance of states that are considered as unhealthy. Additionally the paper discusses the application of ORCA in the context of cyber-physical systems (CPS), as CPS share common characteristics with organically controlled system, such as self-organization, self-configuration, self-optimization and other ones. This is underlined by an exemplary CPS layout that follows the ORCA design principles.
Ramachandra Kota, Nicholas Gibbins, Nicholas R. Jennings
Self-organizing multi-agent systems provide a suitable paradigm for developing autonomic computing systems that manage themselves. Towards this goal, we demonstrate a robust, decentralized approach for structural adaptation in explicitly modeled problem solving agent organizations. Based on self-organization principles, our method enables the autonomous agents to modify their structural relations to achieve a better allocation of tasks in a simulated task-solving environment. Specifically, the agents reason about when and how to adapt using only their history of interactions as guidance. We empirically show that, in a wide range of closed, open, static, and dynamic scenarios, the performance of organizations using our method is close (70â90%) to that of an idealized centralized allocation method and is considerably better (10â60%) than the current state-of-the-art decentralized approaches.
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.
Autonomic computing has become increasingly popular during recent years. Many mobile autonomic and context-aware applications exhibit self-organization in dynamic environments adopted from multi-agent, or swarm, research. The basic paradigm behind swarm systems is that tasks can be more efficiently dispatched through the use of multiple, simple autonomous agents instead of a single, sophisticated one. Such systems are much more adaptive, scalable, and robust than those based on a single, highly capable, agent. A swarm system can generally be defined as a decentralized group (swarm) of autonomous agents (particles) that are simple, with limited processing capabilities. Particles must cooperate intelligently to achieve common tasks.
We address a self-organizing network problem for autonomous robotic sensor swarms towards various ad hoc sensor network applications. For this purpose, a decentralized solution approach is proposed to enable individual robots to build and to maintain their network through locally communicative interactions with adjacent robots. Specifically, the communicative interaction allows robots to select specific neighbors with higher connectivity, and to adapt to network topological changes by robot movements. The proposed approach is verified to be effective through extensive simulations for the secure self-organization of a robotic sensor network. Considering the realistic conditions of communication delays and re-configuration for robot disappearances due to robot failures, simulations are also performed.
Interoperability between autonomous systems like robot swarm or mobile software agents rely on efficient and seamless communication. Such mobile and dynamic environments pertain self-organization and self configuration of computing entities, a need for autonomic publishing and discovery of resources, and communication from and to outside world. Furthermore, such systems are attributed by heterogeneous communication capabilities of various computing entities. We take Web services approach for robot swarm based on robotic communication capabilities and propose a collaborative and decentralized services discovery and management middleware. Our approach provides a loose coupling in terms of space and time and uses both Internet based communication and RFID tags as message post boxes/relays for communication between robots when communication over the Internet is not available.
Toshio Fukuda, Go Iritani, T. Ueyama, Fumihito Arai
This paper deals with a self-organizing robotic system that consists of a number of autonomous robots. We describe the idea of society of robots, which is based on decentralized autonomous systems such as human being or insects. As one of the self-organizing robotic systems, cellular robotic system (CEBOT) was proposed by T. Fukuda. The cellular robotic system is one of the distributed robotic systems, which is composed of a number of autonomous robotic units called cells. The cell is a fundamental unit that has a simple function. To carry out given tasks, the cells connect each other and cooperate mutually. This paper denotes the concept of the cellular robotic system (CEBOT), the social organization of robotic systems, and evolution of group systems.
A.J. Chakravarti, Gerald Baumgartner, Mario Lauria
Desktop grids have recently been used to perform some of the largest computations in the world and have the potential to grow by several more orders of magnitude. However, current approaches to utilizing desktop resources require either centralized servers or extensive knowledge of the underlying system, limiting their scalability. We propose a biologically inspired and fully-decentralized approach to the organization of computation that is based on the autonomous scheduling of strongly mobile agents on a peer-to-peer network. In a radical departure from current models, we envision large-scale desktop grids in which agents autonomously organize themselves so as to maximize resource utilization. By encapsulating computation and behavior into agents, the organization of the computation can be customized for different classes of applications. At the same time, the design of the underlying infrastructure is greatly simplified, resulting in a system that naturally lends itself to a true peer-to-peer implementation where each node can be at the same time provider and user of the computing utility infrastructure. We demonstrate this concept with a reduced-scale proof-of-concept implementation that executes a data-intensive independent-task application on a set of heterogeneous, geographically distributed machines. We present a detailed exploration of the design space of our system and a performance evaluation of our implementation using metrics appropriate for assessing self-organizing desktop grids.
As a flexible and robust intelligent robot system, we have been developing an autonomous and decentralized robot system called ACTRESS, which is composed of multiple robotic agents who have various kinds of functionalities. In order to achieve a given mission in multirobot environment, cooperative behaviors by autonomous agents are essential. In this paper, we focus on the case that multiple robots execute a task cooperatively with functional complement, especially for sensing function. We discuss cooperation of multiple robots using communication in a multi-agent robotic system, and propose a method of functional complement by multiple agents, including group organization and cooperative motion control. Finally, we show the experimental result to verify the proposed method.>
As a flexible and robust intelligent robot system, we have been developing an autonomous and decentralized robot system called ACTRESS, which is composed of multiple robotic agents. In this paper, introducing an idea of cooperative action with group organization and a strategy for cooperative task processing using communication, a method for team organization by efficient negotiation is presented. The efficient negotiation is realized by a newly developed communication functionality called groupcast and a learning mechanism utilizing historical records on past negotiation. The negotiation procedures for team organization are implemented, and as a result of simulation experiments, the efficiency of the learning mechanism is verified.>