In this paper, we present a novel robotic system developed for researching collective social mechanisms in a biohybrid society of robots and honeybees. The potential for distributed coordination, as observed in nature in many different animal species, has caused an increased interest in collective behaviour research in recent years because of its applicability to a broad spectrum of technical systems requiring robust multi-agent control. One of the main problems is understanding the mechanisms driving the emergence of collective behaviour of social animals. With the aim of deepening the knowledge in this field, we have designed a multi-robot system capable of interacting with honeybees within an experimental arena. The final product, stationary autonomous robot units, designed by specificaly considering the physical, sensorimotor and behavioral characteristics of the honeybees (lat. Apis mallifera), are equipped with sensing, actuating, computation, and communication capabilities that enable the measurement of relevant environmental states, such as honeybee presence, and adequate response to the measurements by generating heat, vibration and airflow. The coordination among robots in the developed system is established using distributed controllers. The cooperation between the two different types of collective systems is realized by means of a consensus algorithm, enabling the honeybees and the robots to achieve a common objective. Presented results, obtained within ASSISIbf project, show successful cooperation indicating its potential for future applications.
Rapid changes in electricity power markets have increased the production costs of coal-fired power plants and pushed their production to the limits of profitability. For power plants currently in operation, a possible approach to cope with this issue is to introduce novel methods that increase the plant’s reliability and availability. Coal mills are a subsystem that should ensure a plant’s availability without unexpected breakdowns. Remediation-based maintenance is defined as a set of actions performed after fault detection that do not require instant shutdown due to safety reasons. The aim of this paper was to provide a scientific confirmation that by implementing a novel remediation-based maintenance strategy, electricity production breakdowns can be significantly reduced. First, the performance of the proposed maintenance method was proved in simulation where coal mills were modeled by generalized stochastic Petri nets. The maintenance strategy was then experimentally verified in a 220 MW coal-fired power plant located in Croatia, where the plant’s availability, reliability and efficiency were increased.
This paper addresses a distributed connectivity control problem in networked multi-agent systems. The system communication topology is controlled through the algebraic connectivity measure, the second smallest eigenvalue of the communication graph Laplacian. The algebraic connectivity is estimated locally in a decentralized manner through a trustbased consensus algorithm, in which the agents communicate the perceived quality of the communication links in the system with their set of neighbors. In the presented approach, link qualities represent the weights of the communication graph from which the adjacency matrix is estimated. The Laplacian matrix and its eigenvalues, including the algebraic connectivity, are then calculated from this local estimate of the global adjacency matrix. A method for network topology control is proposed, which creates and deletes communication links based on the Albert-Barabási probabilistic model, depending on the estimated and referenced connectivity level. The proposed algebraic connectivity estimation and connectivity maintenance strategy have been validated both in simulation and on a physical robot swarm, demonstrating the method performance under varying initial topology of the communication graph, different multi-agent system sizes, in various deployment scenarios, and in the case of agent failure.
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