Multi-agent Optimal Control of Ball Balancing on a Mobile

Multi-agent systems have origin in computer engineering however, they have found applications in different field. One of the newly emerged problems in multi-agent systems is multi-agent control. In multi-agent control it is desired that the control is done in distributed manner. That is the controller of each agent should be implemented based on local feedback. In this a mechanism is introuded as a test bed for multi-agent control systems. The introduced mechanism is balancing of a ball on link located on a planar mobile robot. Dynamic equations of the mechanism is derived and the control task is distributed among two agents. For each agent a two loop controller designed wherein external loop is a LQR controller and inner loop is a simple proportional controller. Regulation and fault tolerance performance of controller scheme is evaluated by simulations

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

Learning and Co-operation in Mobile Multi-Robot Systems

Merged with duplicate record 10026.1/1984 on 27.02.2017 by CS (TIS)This thesis addresses the problem of setting the balance between exploration and exploitation in teams of learning robots who exchange information. Specifically it looks at groups of robots whose tasks include moving between salient points in the environment. To deal with unknown and dynamic environments,such robots need to be able to discover and learn the routes between these points themselves. A natural extension of this scenario is to allow the robots to exchange learned routes so that only one robot needs to learn a route for the whole team to use that route. One contribution of this thesis is to identify a dilemma created by this extension: that once one robot has learned a route between two points, all other robots will follow that route without looking for shorter versions. This trade-off will be labeled the Distributed Exploration vs. Exploitation Dilemma, since increasing distributed exploitation (allowing robots to exchange more routes) means decreasing distributed exploration (reducing robots ability to learn new versions of routes), and vice-versa. At different times, teams may be required with different balances of exploitation and exploration. The main contribution of this thesis is to present a system for setting the balance between exploration and exploitation in a group of robots. This system is demonstrated through experiments involving simulated robot teams. The experiments show that increasing and decreasing the value of a parameter of the novel system will lead to a significant increase and decrease respectively in average exploitation (and an equivalent decrease and increase in average exploration) over a series of team missions. A further set of experiments show that this holds true for a range of team sizes and numbers of goals

A Survey on Aerial Swarm Robotics

The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional space and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of this paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Artificial Collective Intelligence Engineering: a Survey of Concepts and Perspectives

Collectiveness is an important property of many systems--both natural and artificial. By exploiting a large number of individuals, it is often possible to produce effects that go far beyond the capabilities of the smartest individuals, or even to produce intelligent collective behaviour out of not-so-intelligent individuals. Indeed, collective intelligence, namely the capability of a group to act collectively in a seemingly intelligent way, is increasingly often a design goal of engineered computational systems--motivated by recent techno-scientific trends like the Internet of Things, swarm robotics, and crowd computing, just to name a few. For several years, the collective intelligence observed in natural and artificial systems has served as a source of inspiration for engineering ideas, models, and mechanisms. Today, artificial and computational collective intelligence are recognised research topics, spanning various techniques, kinds of target systems, and application domains. However, there is still a lot of fragmentation in the research panorama of the topic within computer science, and the verticality of most communities and contributions makes it difficult to extract the core underlying ideas and frames of reference. The challenge is to identify, place in a common structure, and ultimately connect the different areas and methods addressing intelligent collectives. To address this gap, this paper considers a set of broad scoping questions providing a map of collective intelligence research, mostly by the point of view of computer scientists and engineers. Accordingly, it covers preliminary notions, fundamental concepts, and the main research perspectives, identifying opportunities and challenges for researchers on artificial and computational collective intelligence engineering.Comment: This is the author's final version of the article, accepted for publication in the Artificial Life journal. Data: 34 pages, 2 figure

Exploring the intersection of biology and design for product innovations

Design, development, productization, and applications of advanced product concepts are pressing for higher multifunctionality, resilience, and maximization of available resources equitably to meet the growing and continuing demands of global customers. These demands have further accelerated during the recent COVID- 19 pandemic and are continuing to be a challenge. Engineering designs are one of the most effective ways to endow products with functions, resilience, and sustainability. Biology, through millions of years of evolution, has met these acute requirements under severe resource and environmental constraints. As the manufacturing of products is reaching the fundamental limits of raw materials, labor, and resource constraints in terms of availability, accessibility, and affordability, new approaches are a call to action to meet these challenges. Understanding the designs in biology is an attractive, novel, and desired frontier for learning and implementation to meet this call to action. This is the focus of the paper discussed through examples for convergence of fundamental engineering design concepts and the lessons learned and applied from biology

Multi-Agent Systems

A multi-agent system (MAS) is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems which are difficult or impossible for an individual agent or monolithic system to solve. Agent systems are open and extensible systems that allow for the deployment of autonomous and proactive software components. Multi-agent systems have been brought up and used in several application domains

Development of cooperative behavioural model for autonomous multi-robots system deployed to underground mines

The number of disasters that occur in underground mine environments monthly all over the world cannot be ignored. Some of these disasters for instance are roof-falls; explosions, toxic gas inhalation, in-mine vehicle accidents, etc. can cause fatalities and/or disabilities. However, when such accidents happen during mining operations, rescuers find it difficult to respond to it immediately. This creates the necessity to bridge the gap between the lives of miners and the product acquired from the underground mines by using multi-robot systems. This thesis proposes an autonomous multi-robot cooperative behavioural model that can help to guide multi-robots in pre-entry safety inspection of underground mines. A hybrid swarm intelligent model termed, QLACS, that is based on Q-Learning (QL) and the Ant Colony System (ACS) is proposed to achieve cooperative behaviour in a MRS. The intelligent model was developed by harnessing the strengths of both QL and ACS algorithms. The ACS is used to optimize the routes used for each robot while the QL algorithm is used to enhance cooperation among the autonomous robots. The communication within the QLACS model for cooperative behavioural purposes is varied. The performance of the algorithms in terms of communication was evaluated by using a simulation approach. An investigation is conducted on the evaluation/scalability of the model using the different numbers of robots. Simulation results show that the methods proposed in this thesis achieved cooperative behaviour among the robots better than state-of-the-art or other common approaches. Using time and memory consumption as performance metrics, the results reveal that the proposed model can guide two, three and up to four robots to achieve efficient cooperative inspection behaviour in underground terrains

Artificial societies and information theory: modelling of sub system formation based on Luhmann's autopoietic theory

This thesis develops a theoretical framework for the generation of artificial societies. In particular it shows how sub-systems emerge when the agents are able to learn and have the ability to communicate. This novel theoretical framework integrates the autopoietic hypothesis of human societies, formulated originally by the German sociologist Luhmann, with concepts of Shannon's information theory applied to adaptive learning agents. Simulations were executed using Multi-Agent-Based Modelling (ABM), a relatively new computational modelling paradigm involving the modelling of phenomena as dynamical systems of interacting agents. The thesis in particular, investigates the functions and properties necessary to reproduce the paradigm of society by using the mentioned ABM approach. Luhmann has proposed that in society subsystems are formed to reduce uncertainty. Subsystems can then be composed by agents with a reduced behavioural complexity. For example in society there are people who produce goods and other who distribute them. Both the behaviour and communication is learned by the agent and not imposed. The simulated task is to collect food, keep it and eat it until sated. Every agent communicates its energy state to the neighbouring agents. This results in two subsystems whereas agents in the first collect food and in the latter steal food from others. The ratio between the number of agents that belongs to the first system and to the second system, depends on the number of food resources. Simulations are in accordance with Luhmann, who suggested that adaptive agents self-organise by reducing the amount of sensory information or, equivalently, reducing the complexity of the perceived environment from the agent's perspective. Shannon's information theorem is used to assess the performance of the simulated learning agents. A practical measure, based on the concept of Shannon's information ow, is developed and applied to adaptive controllers which use Hebbian learning, input correlation learning (ICO/ISO) and temporal difference learning. The behavioural complexity is measured with a novel information measure, called Predictive Performance, which is able to measure at a subjective level how good an agent is performing a task. This is then used to quantify the social division of tasks in a social group of honest, cooperative food foraging, communicating agents