The attractive researches in the field of mobile robotics is behavior-based mobile robots. Behavior-based approach should have an ideal controller to generate perfect behavior action and able to handle conflicting ones that are seemingly irreconcilable. Schemas to overcome these problems based on fuzzy logic controller (FLC) are provided, known as fuzzy behavior-based robot. This paper presents a novel approach that the fuzzy membership functions and fuzzy rule bases are tuned automatically by particle swarm optimization (PSO), named as particle swarm fuzzy controller (PSFC). PSO is an optimization method that uses a principle of social behavior of a group of particles. The behaviors are controlled by PSFC to generate individual command action. Later, a context dependent blending (CDB) based on meta fuzzy rules coordinates the commands to produce final control action. The algorithm is validated using parameters of MagellanPro mobile robot and tested by simulation using MATLAB/SIMULINK. Simulation results show that the proposed model offers hopeful advantages and has improved performance

Multi-agent system is one of the most popular research interests in robotics nowadays. How would collections of simple robots aid us in human endeavours? Let us think about an ant colony. Ants have little capability, but when many of them are working together, they can do incredible tasks. By examining the major control architectures available in robotics area, decentralized-based reactive control architecture gives the tools required. Priority-based scheme is used in arbitration level to select which behaviour to run. It does not need to gather all information from various sensors to plan its action like deliberative control, which is also known as sensors fusion that clearly required a lot of computational time. Besides that, the mapping that the deliberate control architecture produces might not be valid at the time when changes occur between the time it gathers the information and the time it plans its actions. Instead, intelligence will arise when these simple agents work together to perform some complex task cooperatively. Our multi-agent system consists of three mobile robots that are required to transfer a large object through a restricted area. We are using a leader follower strategy to accomplish this task. Intelligence in multi-agent system in our context is that any agents have a possibility to be a leader or a follower depending on current situation of the environment. This means that all agents are homogeneously equal in hardware and software. By having this capability, it will ensure the burden of the programming task will be decreased because there will be only one program needed for the whole system. In this paper, decentralized reactive control architecture is used to develop an intelligent multi-agent cooperative strategy to perform complex task like carrying a load and navigation.

In this paper an integration of product modeling with robot control is discussed. The authors propose a method to enhance applications of geometric and other product and production equipment related information in robot control. Research of the related methodology was motivated by a growing significance of robots in highly flexible manufacturing systems. Difficulties of reproduction of modeled product geometry at its applications resulted a growing importance of online real time communications between a centralized product modeling and several model applications. The reported research concentrates on the creation and application of model data information understandable by robot controls, on communication of model data with robot environments through the Internet, and finally on some related telerobotics issues. It covers one of the actual problem complexes in the integration of engineering modeling with its shop floor level applications. Systems integration, data model, data communication and model processing have been investigated. In the paper an introduction of the robot related activities of engineers and the modeling environment are first given, taking into account the robot control aspect. Following this, key issues regarding robot tasks and their shape model based control are explained. Then an approach to interactive application of Internet-based telerobotics for product model driven control of robots is outlined. Finally, possibilities for an integration of remote robot control applications with product modeling environments are discussed.