Ján Zelenka

Production planning and scheduling by means of artificial immune systems and particle swarm optimisation algorithms

The main task of the scheduling optimisation process in production systems is to minimise production cost, overall production time and to ensure optimal utilisation of the resources. Application of stochastic search techniques to find a feasible schedule that minimise cost and satisfy all constraints jointed with all products can bring a particular solution of the complexity problem. On the other hand, the cost and the time of an optimisation process have to reciprocate with the found schedule; otherwise the optimisation loses its meaning. The article presents two stochastic methods, based on biologically inspired techniques, applied on a scheduling optimisation process. The first one is based on the mechanism inspired by biological evolution and the one method applies the swarm intelligence. The application of methods is illustrated on a real world example of a production line.

Insect Pheromone Strategy for the Robots Coordination

Insect colony inspires scientists for years to create similar behavior in the robotic application. The main goal of our work was to develop simple and powerful algorithm which will accept dynamically changes in the size of a robot swarm due the mission. This algorithm is suitable for situations where unpredictable conditions may lead to robot fault in multi-robotics system and mission completion is endangered. In this article we would like to investigate properties of a simple pheromone based algorithm. The algorithm operates as cellular automata and partially uses an insect pheromone strategy for the robots coordination. Our abstract model is a decentralized adaptive system with a shared memory which represents the environment.

Insect pheromone strategy for the robots coordination — Reaction on loss communication

This paper represents biologically inspired control strategy for a group of robots. A descripted method was developed to solve exploration and monitoring tasks. The main goal of our work was to develop a simple and powerful algorithm which will accept dynamical changes in the number of robot swarm in line with its mission. This algorithm is suitable for situations where unpredictable conditions may lead to failures in multi-robotics system and the completion of the mission is endangered. In this article we would like to focus on the investigation of the actual characteristics of a simple pheromone based algorithm in a situation where there is a sudden failure in communication.

Application of particle swarm optimization in job-shop scheduling problem in the recycling process

This article refers to possibility of utilization of particle swarm optimization (PSO) by job-shop scheduling problem (JSSP) in real manufacturing system, where the operation processing time depends on the state of machine after processing previous operation.

Control and Coordination System Supported by Biologically Inspired Method for 3D Space “Proof of Concept”

This paper presents a control strategy for a swarm of mobile/flying robots operated in 3D space. The described biologically inspired method was developed to solve exploration or monitoring tasks. We discuss a possibility to extend an algorithm [1] to 3D space in this paper. The presented model is a decentralized adaptive system with shared memory representing the environment.

Distributed Multi-agent System for Area Coverage Tasks: Architecture and Development

The article aims to present a new concept in mobile robotics – swarm robotics. It gives an overview of related research in coordination of mobile robots in a group and presents recent research activities in swarm robotics at the Institute of Informatics SAS. The architecture and development of a distributed multi-agent system for area coverage tasks are introduced.

Outdoor UAV control and coordination system supported by biological inspired method

This paper presents control strategy for a group of multi-rotor unmanned aerial vehicles (UAV). Descripted biologically inspired method was developed to solve exploration or monitoring tasks. The proposed solution was tested in real environment on a quad-rotor UAV platform. Our abstract model is a decentralized adaptive system with a shared memory which represents the environment.

SkyBat: A Swarm Robotic Model Inspired by Fission-Fusion Behaviour of Bats

An agent based model - SkyBat, based on long-term observation of bats behaviour under fission-fusion dynamics, is presented in this paper. The agents cooperate while searching for specific targets of interest in an unknown area. Although the agents are autonomous, they have an ability to move from one location to another without a group leader and to react to changes in environment.

A Self-adapting Method for 3D Environment Exploration Inspired by Swarm Behaviour

A problem of finding an optimal size of a swarm of robots in a way of effective cooperation is not an easy task to solve. There are many factors, which influence the optimal size of the robotic swarm. Among major factors that have to be considered, belong communication, structure of environment and behavior of agents in the swarm. This paper presents a method for creating a decentralized self-adapting swarm of robots. The task is to set an optimal size of the swarm in a role of space exploration. Communication among robots is restricted to communication through the environment. The only way how agents communicate, is through artificial pheromone marks. This fact gives us an ability to create a decentralized algorithm for controlling and coordination of a robotic swarm, which is robust and efective.

Cooperation Particle Swarm Optimization with Discrete Events Systems by Dynamical Rescheduling

Currently, materials flow optimization and creating of optimal schedule are one of the main tasks of all companies for increase of competitiveness. A schedule problem in a manufacturing company is characterized as jobs sequence and allocation to machines during a time period. A variety of approaches have been developed to solve the problem of scheduling. However, many of these approaches are often impractical in dynamic real-world environments where there are complex constraints and a variety of unexpected disruptions. In this chapter cooperation of one meta-heuristic optimization algorithm with manufacturing model by the dynamical rescheduling is described. Particle Swarm Optimization algorithm solved scheduling problem of real manufacturing system. Model of the manufacturing system is represented as discrete event system created by SimEvents toolbox of MATLAB programing enviroment.