9 research outputs found
Genetic algorithm seeding of idiotypic networks for mobile-robot navigation
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
An Idiotypic Immune Network as a Short Term Learning Architecture for Mobile Robots
A combined Short-Term Learning (STL) and Long-Term Learning (LTL) approach to
solving mobile robot navigation problems is presented and tested in both real
and simulated environments. The LTL consists of rapid simulations that use a
Genetic Algorithm to derive diverse sets of behaviours. These sets are then
transferred to an idiotypic Artificial Immune System (AIS), which forms the STL
phase, and the system is said to be seeded. The combined LTL-STL approach is
compared with using STL only, and with using a handdesigned controller. In
addition, the STL phase is tested when the idiotypic mechanism is turned off.
The results provide substantial evidence that the best option is the seeded
idiotypic system, i.e. the architecture that merges LTL with an idiotypic AIS
for the STL. They also show that structurally different environments can be
used for the two phases without compromising transferabilityComment: 13 pages, 5 tables, 4 figures, 7th International Conference on
Artificial Immune Systems (ICARIS2008), Phuket, Thailan
An idiotypic immune network as a short-term learning architecture for mobile robots
A combined Short-Term Learning (STL) and Long-Term Learning (LTL) approach to solving mobile robot navigation problems is presented and tested in both real and simulated environments. The LTL consists of rapid simulations
that use a Genetic Algorithm to derive diverse sets of behaviours. These sets are then transferred to an idiotypic Artificial Immune System (AIS), which forms the STL phase, and the system is said to be seeded. The combined
LTL-STL approach is compared with using STL only, and with using a handdesigned controller. In addition, the STL phase is tested when the idiotypic mechanism is turned off. The results provide substantial evidence that the best option is the seeded idiotypic system, i.e. the architecture that merges LTL with an idiotypic AIS for the STL. They also show that structurally different environments can be used for the two phases without compromising transferability
Two-Timescale Learning Using Idiotypic Behaviour Mediation For A Navigating Mobile Robot
A combined Short-Term Learning (STL) and Long-Term Learning (LTL) approach to
solving mobile-robot navigation problems is presented and tested in both the
real and virtual domains. The LTL phase consists of rapid simulations that use
a Genetic Algorithm to derive diverse sets of behaviours, encoded as variable
sets of attributes, and the STL phase is an idiotypic Artificial Immune System.
Results from the LTL phase show that sets of behaviours develop very rapidly,
and significantly greater diversity is obtained when multiple autonomous
populations are used, rather than a single one. The architecture is assessed
under various scenarios, including removal of the LTL phase and switching off
the idiotypic mechanism in the STL phase. The comparisons provide substantial
evidence that the best option is the inclusion of both the LTL phase and the
idiotypic system. In addition, this paper shows that structurally different
environments can be used for the two phases without compromising
transferability.Comment: 40 pages, 12 tables, Journal of Applied Soft Computin
Mobile robot localization failure recovery
Mobile robot localization is one of the most important problems in robotics. Localization is the process of a robot finding out its location given a map of its environment. A number of successful localization solutions have been proposed, among them the well-known and popular Monte Carlo localization method, which is based on particle filters. This thesis proposes a localization approach based on particle filters, using a different way of initializing and resampling of the particles, that reduces the cost of localization. Ultrasonic and light sensors are used in order to perform the experiments. Monte Carlo Localization may fail to localize the robot properly because of the premature convergence of the particles. Using more number of particles increases the computational cost of localization process. Experimental results show that, applying the proposed method robot can successfully localize itself using less number of particles; therefore the cost of localization is decreased
Genetic algorithm seeding of idiotypic networks for mobile-robot navigation
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
GENETIC-ALGORITHM SEEDING OF IDIOTYPIC NETWORKS FOR MOBILE-ROBOT NAVIGATION
Keywords: Mobile-robot navigation, genetic algorithm, artificial immune system, idiotypic network. 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.