ABSTRACT Low-level tasks for the navigation of autonomous vehicles can be accomplished efficientl... more ABSTRACT Low-level tasks for the navigation of autonomous vehicles can be accomplished efficiently by behavioral-based approaches mapping sensory data onto control commands in a reactive way, without using internal representations. Such a direct mapping can be usefully realized using fuzzy logic. All the choices involved in fuzzy controller design, impacting the final result in terms of complexity and performance, are generally made intuitively and/or by trial-and-error processes. Automatic learning and self-tuning of rules are required for adaptive controllers operating in real dynamic environments instead. This paper presents a method for minimizing the number of rules in a fuzzy controller without affecting its overall performance. The experiments have been made reducing the number of rules of a fuzzy controller developed for driving a TRC Labmate based vehicle along the wall on its right-hand side mapping ultrasonic sensor readings onto steering velocity values. Fuzzy rules have been learned automatically from training data collected during operator-driven runs of the vehicle. Experimental results, comparing the original and the optimized versions of the controller successfully driving the vehicle along arbitrarily shaped walls in real unknown environments, are provided
Parallel Genetic Algorithms (PGAs), implemented on the APE100/Quadrics SIMD architecture, were ap... more Parallel Genetic Algorithms (PGAs), implemented on the APE100/Quadrics SIMD architecture, were applied to automatic design of membership functions and fuzzy rules for robotic control. They run multiple simultaneous searches, differently balancing exploration of the solution space and fine tune of the best solutions available at each generation. Migration spreads the best individuals of each population in local neighborhoods. The approach
Applications and Science of Computational Intelligence, 1998
Designing a fuzzy system involves defining membership functions and constructing rules. Carrying ... more Designing a fuzzy system involves defining membership functions and constructing rules. Carrying out these two steps manually often results in a poorly performing system. Genetic Algorithms (GAs) has proved to be a useful tool for designing optimal fuzzy controller. In order to increase the efficiency and effectiveness of their application, parallel GAs (PAGs), evolving synchronously several populations with different balances between exploration and exploitation, have been implemented using a SIMD machine (APE100/Quadrics). The parameters to be identified are coded in such a way that the algorithm implicitly provides a compact fuzzy controller, by finding only necessary rules and removing useless inputs from them. Early results, working on a fuzzy controller implementing the wall-following task for a real vehicle as a test case, provided better fitness values in less generations with respect to previous experiments made using a sequential implementation of GAs.
Proceedings of Conference on Intelligent Vehicles, 1996
ABSTRACT Low-level tasks for the navigation of autonomous vehicles can be accomplished efficientl... more ABSTRACT Low-level tasks for the navigation of autonomous vehicles can be accomplished efficiently by behavioral-based approaches mapping sensory data onto control commands in a reactive way, without using internal representations. Such a direct mapping can be usefully realized using fuzzy logic. All the choices involved in fuzzy controller design, impacting the final result in terms of complexity and performance, are generally made intuitively and/or by trial-and-error processes. Automatic learning and self-tuning of rules are required for adaptive controllers operating in real dynamic environments instead. This paper presents a method for minimizing the number of rules in a fuzzy controller without affecting its overall performance. The experiments have been made reducing the number of rules of a fuzzy controller developed for driving a TRC Labmate based vehicle along the wall on its right-hand side mapping ultrasonic sensor readings onto steering velocity values. Fuzzy rules have been learned automatically from training data collected during operator-driven runs of the vehicle. Experimental results, comparing the original and the optimized versions of the controller successfully driving the vehicle along arbitrarily shaped walls in real unknown environments, are provided
ABSTRACT Low-level tasks for the navigation of autonomous vehicles can be accomplished efficientl... more ABSTRACT Low-level tasks for the navigation of autonomous vehicles can be accomplished efficiently by behavioral-based approaches mapping sensory data onto control commands in a reactive way, without using internal representations. Such a direct mapping can be usefully realized using fuzzy logic. All the choices involved in fuzzy controller design, impacting the final result in terms of complexity and performance, are generally made intuitively and/or by trial-and-error processes. Automatic learning and self-tuning of rules are required for adaptive controllers operating in real dynamic environments instead. This paper presents a method for minimizing the number of rules in a fuzzy controller without affecting its overall performance. The experiments have been made reducing the number of rules of a fuzzy controller developed for driving a TRC Labmate based vehicle along the wall on its right-hand side mapping ultrasonic sensor readings onto steering velocity values. Fuzzy rules have been learned automatically from training data collected during operator-driven runs of the vehicle. Experimental results, comparing the original and the optimized versions of the controller successfully driving the vehicle along arbitrarily shaped walls in real unknown environments, are provided
Parallel Genetic Algorithms (PGAs), implemented on the APE100/Quadrics SIMD architecture, were ap... more Parallel Genetic Algorithms (PGAs), implemented on the APE100/Quadrics SIMD architecture, were applied to automatic design of membership functions and fuzzy rules for robotic control. They run multiple simultaneous searches, differently balancing exploration of the solution space and fine tune of the best solutions available at each generation. Migration spreads the best individuals of each population in local neighborhoods. The approach
Applications and Science of Computational Intelligence, 1998
Designing a fuzzy system involves defining membership functions and constructing rules. Carrying ... more Designing a fuzzy system involves defining membership functions and constructing rules. Carrying out these two steps manually often results in a poorly performing system. Genetic Algorithms (GAs) has proved to be a useful tool for designing optimal fuzzy controller. In order to increase the efficiency and effectiveness of their application, parallel GAs (PAGs), evolving synchronously several populations with different balances between exploration and exploitation, have been implemented using a SIMD machine (APE100/Quadrics). The parameters to be identified are coded in such a way that the algorithm implicitly provides a compact fuzzy controller, by finding only necessary rules and removing useless inputs from them. Early results, working on a fuzzy controller implementing the wall-following task for a real vehicle as a test case, provided better fitness values in less generations with respect to previous experiments made using a sequential implementation of GAs.
Proceedings of Conference on Intelligent Vehicles, 1996
ABSTRACT Low-level tasks for the navigation of autonomous vehicles can be accomplished efficientl... more ABSTRACT Low-level tasks for the navigation of autonomous vehicles can be accomplished efficiently by behavioral-based approaches mapping sensory data onto control commands in a reactive way, without using internal representations. Such a direct mapping can be usefully realized using fuzzy logic. All the choices involved in fuzzy controller design, impacting the final result in terms of complexity and performance, are generally made intuitively and/or by trial-and-error processes. Automatic learning and self-tuning of rules are required for adaptive controllers operating in real dynamic environments instead. This paper presents a method for minimizing the number of rules in a fuzzy controller without affecting its overall performance. The experiments have been made reducing the number of rules of a fuzzy controller developed for driving a TRC Labmate based vehicle along the wall on its right-hand side mapping ultrasonic sensor readings onto steering velocity values. Fuzzy rules have been learned automatically from training data collected during operator-driven runs of the vehicle. Experimental results, comparing the original and the optimized versions of the controller successfully driving the vehicle along arbitrarily shaped walls in real unknown environments, are provided
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Papers by G. Mondelli