Artificial Immune Systems - Models, algorithms and applications

Copyright © 2010 Academic Research Publishing Agency.This article has been made available through the Brunel Open Access Publishing Fund.Artificial Immune Systems (AIS) are computational paradigms that belong to the computational intelligence family and are inspired by the biological immune system. During the past decade, they have attracted a lot of interest from researchers aiming to develop immune-based models and techniques to solve complex computational or engineering problems. This work presents a survey of existing AIS models and algorithms with a focus on the last five years.This article is available through the Brunel Open Access Publishing Fun

Multi-robot system control using artificial immune system

For the successful deployment of task-achieving multi-robot systems (MRS), the interactions must be coordinated among the robots within the MRS and between the robots and the task environment. There have been a number of impressive experimentally demonstrated coordinated MRS. However it is still of a premature stage for real world applications. This dissertation presents an MRS control scheme using Artificial Immune Systems (AIS). This methodology is firmly grounded in the biological sciences and provides robust performance for the intertwined entities involved in any task-achieving MRS. Based on its formal foundation, it provides a platform to characterize interesting relationships and dependencies among MRS task requirements, individual robot control, capabilities, and the resulting task performance. The work presented in this dissertation is a first of its kind wherein the principles of AIS have been used to model and organize the group behavior of the MRS. This has been presented in the form of a novel algorithm. In addition to the above, generic environments for computer simulation and real experiment have been realized to demonstrate the working of an MRS. These could potentially be used as a test bed to implement other algorithms onto the MRS. The experiment in this research is a bomb disposal task which involves a team of three heterogeneous robots with different sensors and actuators. And the algorithm has been tested practically through computer simulations.Mechanical Engineerin

Immune systems inspired multi-robot cooperative shepherding

Certain tasks require multiple robots to cooperate in order to solve them. The main problem with multi-robot systems is that they are inherently complex and usually situated in a dynamic environment. Now, biological immune systems possess a natural distributed control and exhibit real-time adaptivity, properties that are required to solve problems in multi-robot systems. In this thesis, biological immune systems and their response to external elements to maintain an organism's health state are researched. The objective of this research is to propose immune-inspired approaches to cooperation, to establish an adaptive cooperation algorithm, and to determine the refinements that can be applied in relation to cooperation. Two immune-inspired models that are based on the immune network theory are proposed, namely the Immune Network T-cell-regulated---with Memory (INT-M) and the Immune Network T-cell-regulated---Cross-Reactive (INT-X) models. The INT-M model is further studied where the results have suggested that the model is feasible and suitable to be used, especially in the multi-robot cooperative shepherding domain. The Collecting task in the RoboShepherd scenario and the application of the INT-M algorithm for multi-robot cooperation are discussed. This scenario provides a highly dynamic and complex situation that has wide applicability in real-world problems. The underlying 'mechanism of cooperation' in the immune inspired model (INT-M) is verified to be adaptive in this chosen scenario. Several multi-robot cooperative shepherding factors are studied and refinements proposed, notably methods used for Shepherds' Approach, Shepherds' Formation and Steering Points' Distance. This study also recognises the importance of flock identification in relation to cooperative shepherding, and the Connected Components Labelling method to overcome the related problem is presented. Further work is suggested on the proposed INT-X model that was not implemented in this study, since it builds on top of the INT-M algorithm and its refinements. This study can also be extended to include other shepherding behaviours, further investigation of other useful features of biological immune systems, and the application of the proposed models to other cooperative tasks

Artificial immune systems

The human immune system has numerous properties that make it ripe for exploitation in the computational domain, such as robustness and fault tolerance, and many different algorithms, collectively termed Artificial Immune Systems (AIS), have been inspired by it. Two generations of AIS are currently in use, with the first generation relying on simplified immune models and the second generation utilising interdisciplinary collaboration to develop a deeper understanding of the immune system and hence produce more complex models. Both generations of algorithms have been successfully applied to a variety of problems, including anomaly detection, pattern recognition, optimisation and robotics. In this chapter an overview of AIS is presented, its evolution is discussed, and it is shown that the diversification of the field is linked to the diversity of the immune system itself, leading to a number of algorithms as opposed to one archetypal system. Two case studies are also presented to help provide insight into the mechanisms of AIS; these are the idiotypic network approach and the Dendritic Cell Algorithm

Evolution of collective behaviors for a real swarm of aquatic surface robots

Swarm robotics is a promising approach for the coordination of large numbers of robots. While previous studies have shown that evolutionary robotics techniques can be applied to obtain robust and efficient self-organized behaviors for robot swarms, most studies have been conducted in simulation, and the few that have been conducted on real robots have been confined to laboratory environments. In this paper, we demonstrate for the first time a swarm robotics system with evolved control successfully operating in a real and uncontrolled environment. We evolve neural network-based controllers in simulation for canonical swarm robotics tasks, namely homing, dispersion, clustering, and monitoring. We then assess the performance of the controllers on a real swarm of up to ten aquatic surface robots. Our results show that the evolved controllers transfer successfully to real robots and achieve a performance similar to the performance obtained in simulation. We validate that the evolved controllers display key properties of swarm intelligence-based control, namely scalability, flexibility, and robustness on the real swarm. We conclude with a proof-of-concept experiment in which the swarm performs a complete environmental monitoring task by combining multiple evolved controllers.info:eu-repo/semantics/publishedVersio

Intelligent Control and Path Planning of Multiple Mobile Robots Using Hybrid Ai Techniques

This work reports the problem of intelligent control and path planning of multiple mobile robots. Soft computing methods, based on three main approaches i.e. 1) Bacterial Foraging Optimization Algorithm, 2) Radial Basis Function Network and 3) Bees Algorithm are presented. Initially, Bacterial foraging Optimization Algorithm (BFOA) with constant step size is analyzed for the navigation of mobile robots. Then the step size has been made adaptive to develop an Adaptive Bacterial Foraging Optimization (ABFO) controller. Further, another controller using radial basis function neural network has been developed for the mobile robot navigation. Number of training patterns are intended to train the RBFN controller for different conditions arises during the navigation. Moreover, Bees Algorithm has been used for the path planning of the mobile robots in unknown environments. A new fitness function has been used to perform the essential navigational tasks effectively and efficiently. In addition to the selected standalone approaches, hybrid models are also proposed to improve the ability of independent navigation. Five hybrid models have been presented and analyzed for navigation of one, two and four mobile robots in various scenarios. Comparisons have been made for the distance travelled and time taken by the robots in simulation and real time. Further, all the proposed approaches are found capable of solving the basic issues of path planning for mobile robots while doing navigation. The controllers have been designed, developed and analyzed for various situations analogous to possible applications of the robots in indoor environments. Computer simulations are presented for all cases with single and multiple mobile robots in different environments to show the effectiveness of the proposed controllers. Furthermore, various exercises have been performed, analyzed and compared in physical environments to exhibit the effectiveness of the developed controllers