AIS Based Robot Navigation in a Rescue Scenario

Krautmacher, Michael; Dilger, Werner

doi:10.1007/978-3-540-30220-9_9

Cited by 27 publications

(20 citation statements)

References 3 publications

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“…RL is used both on the selected antibody and on those connected to it in the network. Krautmacher and Dilger [13] apply the idiotypic technique to navigation in a simulated maze world using the same basic approach as Watanabe et al [5], but their antigens are variable, being composed of an object type and an object position, and they do not implement metadynamics, i.e., antibodies are not replaced. Luh and Liu [9] use an idiotypic system to overcome local-minima problems, modeling their antibodies as steering directions and their antigens as a fusion data set consisting of orientation of goal, distance between obstacles and sensors, and positions of sensors.…”

Section: Incorporation Of the Network Theory Into Mobile Roboticsmentioning

confidence: 99%

Idiotypic Immune Networks in Mobile-Robot Control

Whitbrook

Aickelin

Garibaldi

2007

IEEE Trans. Syst., Man, Cybern. B

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Abstract-Jerne's idiotypic-network theory postulates that the immune response involves interantibody stimulation and suppression, as well as matching to antigens. The theory has proved the most popular artificial immune system (AIS) model for incorporation into behavior-based robotics, but guidelines for implementing idiotypic selection are scarce. Furthermore, the direct effects of employing the technique have not been demonstrated in the form of a comparison with nonidiotypic systems. This paper aims to address these issues. A method for integrating an idiotypic AIS network with a reinforcement-learning (RL)-based control system is described, and the mechanisms underlying antibody stimulation and suppression are explained in detail. Some hypotheses that account for the network advantage are put forward and tested using three systems with increasing idiotypic complexity. The basic RL, a simplified hybrid AIS-RL that implements idiotypic selection independently of derived concentration levels, and a full hybrid AIS-RL scheme are examined. The test bed takes the form of a simulated Pioneer robot that is required to navigate through maze worlds detecting and tracking door markers.Index Terms-Artificial immune system (AIS), behavior arbitration mechanism, idiotypic-network theory, reinforcement learning (RL).

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Section: Incorporation Of the Network Theory Into Mobile Roboticsmentioning

confidence: 99%

Idiotypic Immune Networks in Mobile-Robot Control

Whitbrook

Aickelin

Garibaldi

2007

IEEE Trans. Syst., Man, Cybern. B

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“…For instance, in the Artificial Biochemical Neuron, concentrations of reactants form weighted links between reactions and their dynamics are modelled using Ordinary Differential Equations (ODEs) (Eikelder et al 2009). Other related chemically inspired approaches can be found in the literature, for instance, the Gene Regulatory Network algorithm (Guo et al 2009), the Digital Hormone System (Shen et al 2004), the Artificial Homeostatic Hormone System (Hamann et al 2010) and idiotypic Farmer based Artificial Immune Systems (Krautmacher and Dilger 2004). Like these models, the ARN represents molecular species as continuous concentrations where dynamics are modelled using ODEs.…”

Section: The Arn Belongs To the Branch Of Artificial Life Known As Armentioning

confidence: 99%

Exploring aspects of cell intelligence with artificial reaction networks

et al. 2013

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“…Michelan and Von Zuben (2002) and Vargas et al (2003) also use GAs to evolve the antibodies, but again only the idiotypic-network connections are derived. Krautmacher and Dilger (2004) apply the idiotypic method to robot navigation, but their emphasis is on the use of a variable set of antigens; they do not change or develop the initial set of handcrafted antibodies, as only the network links are evolved. Luh and Liu (2004) address target-finding using an idiotypic system, modelling their antibodies as steering directions.…”

Section: Background and Motivationmentioning

confidence: 99%

Genetic Algorithm Seeding of Idiotypic Networks for Mobile-Robot Navigation

2008

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Abstract:Robot-control designers have begun to exploit the properties of the human immune system in order to produce dynamic systems that can adapt to complex, varying, real-world tasks. Jerne's idiotypic-network theory has proved the most popular artificial-immune-system (AIS) method for incorporation into behaviour-based robotics, since idiotypic selection produces highly adaptive responses. However, previous efforts have mostly focused on evolving the network connections and have often worked with a single, preengineered set of behaviours, limiting variability. This paper describes a method for encoding behaviours as a variable set of attributes, and shows that when the encoding is used with a genetic algorithm (GA), multiple sets of diverse behaviours can develop naturally and rapidly, providing much greater scope for flexible behaviour-selection. The algorithm is tested extensively with a simulated e-puck robot that navigates around a maze by tracking colour. Results show that highly successful behaviour sets can be generated within about 25 minutes, and that much greater diversity can be obtained when multiple autonomous populations are used, rather than a single one.

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AIS Based Robot Navigation in a Rescue Scenario

Cited by 27 publications

References 3 publications

Idiotypic Immune Networks in Mobile-Robot Control

Idiotypic Immune Networks in Mobile-Robot Control

Exploring aspects of cell intelligence with artificial reaction networks

Genetic Algorithm Seeding of Idiotypic Networks for Mobile-Robot Navigation

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