Behaviour-based Robot Research Articles

Learning of fuzzy-behaviours using Particle Swarm Optimisation in behaviour-based mobile robot

Behaviour-based mobile robots should have an ideal controller to generate perfect behaviour action. A schema to overcome these problems is provided, known as Fuzzy Behaviour-based robot. However, tuning fuzzy parameters is not a simple effort. This paper presents a technique to tune automatically fuzzy Rule Bases and fuzzy Membership Functions (MF) by Particle Swarm Optimisation (PSO), named as Particle Swarm Fuzzy Controller (PSFC). The behaviours 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. A Sigmoid Decreasing Inertia Weight has been proposed for a good balancing of global and local searches for obtaining good convergence speed and best accuracy of PSO algorithm. 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.

Advances in intelligent information processing

Advances in intelligent information processing

Teaching robots to plan through Q-learning

This paper presents a Q-learning approach to state-based planning of behaviour-based walking robots. The learning process consists of a teaching stage and an autonomous learning stage. During the teaching stage, the robot is instructed to operate in some interesting areas of the solution space to accumulate some prior knowledge. Then, the learning is switched to the autonomous learning stage to let the robot explore the solution space based on its prior knowledge. Experiments are conducted in the RoboCup domain and results show a good performance of the proposed method.

From behaviour-based robots to motivation-based robots

The appearance on the market of entertainment robots for children and families has ipso facto created the new category of motivation-based robots. A taxonomy of the architectures of different robot categories is proposed. The architecture of motivation-based robots is phylogenetic and ontogenetic. A tentative architecture for a specific experimental setup is described. The results of the experiment show that a new motivation arises from the interaction between the robot and the environment. Motivation-based robots equipped with ontogenetic architecture might provide the foundation for a new generation of robots capable of ontogenetic development.

Behaviour-based robot program invariance

Despite the numerous technical improvements, robot programming is still today a very tedious activity requiring considerable efforts for fine tuning programs written off-line. Moreover, such programs are usually not particularly portable from one robot to another, sometimes even when the robots are from the same maker.The issue addressed in the work reported here is how to overcome these shortcomings. In this respect, the argument is to develop programs within the behaviour-based assembly paradigm which views tasks in terms of goal-achieving entities (behaviours). The focus on the description of a task more than its implementation is the key to generality and portability. As shown with the aid of two similar, but not identical, assembly benchmarks, behavioural programs are independent of any specificity of each assembly kit and can, indeed, be constructed using the very same code.

Connectionist Learning in Behaviour-Based Mobile Robots: A Survey

This paper is a survey of some recent connectionist approaches to the design and development of behaviour-based mobile robots. The research is analysed in terms of principal connectionist learning methods and neurological modeling trends. Possible advantages over conventionally programmed methods are considered and the connectionist achievements to date are assessed. A realistic view is taken of the prospects for medium term progress and some observations are made concerning the direction this might profitably take.

A behaviour-based architecture with attention control

In this paper we present a behaviour-based mobile robot system for task execution. The behaviour model of this system consists of a number of motion behaviours, including reflexes and voluntary motion behaviours, and knowledge acquisition modules providing supporting information. Execution of a task is regarded as a problem-solving process. A blackboard model is introduced to overcome some shortcomings of the behaviour-based architectures, especially concerning modularity and task execution capability. The concept of attention is introduced in the behaviour control, which is more advantageous than the behaviour control mechanisms presented in the literature. Its introduction results in situation-dependent behaviour coordination. For efficient task execution, environment knowledge is maintained in a memory. Task-achieving behaviour is designed to make use of the memory when available.

Evolving of a Fitness Based Operation Strategy for a Robot Society

An evolving fitness based foraging and exploration strategy for a robot society formed by cooperative autonomous mobile robots in a spatially and temporally dynamic environment is presented. The operation of multiple behaviour-based robots is controlled by an outside operator through modifications in members’ fitness functions. With a certain type of function formulation a collective society behavior will emerge and improve the society’s operation. The suggested concept is verified with simulations and some near future physical realizations are also presented.