An Immunological Approach to Mobile Robot Navigation

Luh, Guan-Chun; Liu, Weiwen

doi:10.5772/6008

Cited by 3 publications

(3 citation statements)

References 34 publications

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“…Robotic applications tend to be based on controlling simulated robots around small, artificial environments, generally addressing the problem of behaviour arbitration and autonomous navigation (Lau et al 2007;Luh and Liu 2006), although work by (Hart et al 2003) attempts to lay a foundation for using an AIS to provide the basis of an architecture for a robot to acquire new, more complex skills throughout its lifetime. The small topics of image processing And web-mining are self-evident, however, an increasing amount of literature reporting successful applications of immune algorithms in these domains is becoming apparent.…”

Section: Other Minor Application Areasmentioning

confidence: 99%

A Survey of artificial immune applications

2010

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The artificial immune system (AIS) community has been vibrant and active for a number of years now, producing a prolific amount of research ranging from modeling the natural immune system, to tackling real world applications, using an equally diverse set of immune inspired algorithms. We review the current immune applications of the AIS approach, and propose a number of suggestions to the AIS community that can be undertaken to help move the area forward. Despite many successes of AIS techniques, there remain some open issues which have to be addressed in order to make the AIS a real-world problem solving technique.

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Section: Other Minor Application Areasmentioning

confidence: 99%

A Survey of artificial immune applications

2010

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“…After the cloning procedure has created the antibodies, the affinity should be measured as explained in Eq. (4). If the affinity criteria are not met the antibodies will be discarded and deleted.…”

Section: Codamentioning

confidence: 99%

“…Machine learning is the response to this paradigm, several algorithms have arisen having as main objective; making computers learn as can be seen in [2], even they may have different particular objectives according to their taks. Machine learning algorithms have already demonstrated their competence at learning in different engineering and science problems [3][4][5]. However, there is still no algorithm that is more versatile and generalised, i.e., a "do-it-all" algorithm that can be used in several situations no matter the nature of the task itself.…”

Section: Introductionmentioning

confidence: 99%

CODA Algorithm: An Immune Algorithm for Reinforcement Learning Tasks

Rebollo¹,

Cruz²,

Molina³

2016

Recent Advances in Robotic Systems

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This document presents the design of an algorithm that takes on its basis: reinforcement learning, learning from demonstration and most importantly Artificial Immune Systems. The main advantage of this algorithm named CODA (Cognition from Data). Is; it can learn from limited data samples-that is given a single example and the algorithm will create its own knowledge. The algorithm imitates from the Natural Immune System the clonal procedure for obtaining a repertoire of antibodies from a single antigen. It also uses the self-organised memory in order to reduce searching time in the whole action-state space by searching in specific clusters. CODA algorithm is presented and explained in detail in order to understand how these three principles are used. The algorithm is explained with pseudocode, flowcharts and block diagrams. The clonal/mutation results are presented with a simple example. It can be seen graphically how new data that has a completely new probability distribution. Finally, the first application where CODA is used, a humanoid hand is presented. In this application the algorithm created affordable grasping postures from limited examples, creates its own knowledge and stores data in memory data in memory in order to recognise whether it has been on a similar situation.

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