Graphical abstractDisplay Omitted HighlightsAn algorithm which is able to learn controllers with embedded preprocessing for mobile robotics is presented.Quantified Fuzzy Propositions, a model able to summarize the low-level input data, are used.The algorithm was tested with the wall-following behavior both in simulated and real environments.Results show a better and statistically significant performance of our proposal.The approach was also successfully tested in three real world behaviors. The automatic design of controllers for mobile robots usually requires two stages. In the first stage, sensorial data are preprocessed or transformed into high level and meaningful values of variables which are usually defined from expert knowledge. In the second stage, a machine learning technique is applied to obtain a controller that maps these high level variables to the control commands that are actually sent to the robot. This paper describes an algorithm that is able to embed the preprocessing stage into the learning stage in order to get controllers directly starting from sensorial raw data with no expert knowledge involved. Due to the high dimensionality of the sensorial data, this approach uses Quantified Fuzzy Rules (QFRs), that are able to transform low-level input variables into high-level input variables, reducing the dimensionality through summarization. The proposed learning algorithm, called Iterative Quantified Fuzzy Rule Learning (IQFRL), is based on genetic programming. IQFRL is able to learn rules with different structures, and can manage linguistic variables with multiple granularities. The algorithm has been tested with the implementation of the wall-following behavior both in several realistic simulated environments with different complexity and on a Pioneer 3-AT robot in two real environments. Results have been compared with several well-known learning algorithms combined with different data preprocessing techniques, showing that IQFRL exhibits a better and statistically significant performance. Moreover, three real world applications for which IQFRL plays a central role are also presented: path and object tracking with static and moving obstacles avoidance.
In @cite_43 a Genetic Cooperative-Competitive Learning (GCCL) approach was presented. The proposal learns knowledge bases without preprocessing raw data, but the rules involved approximative labels while the IQFRL proposal uses unconstrained multiple granularity. Moreover, in this approach it is difficult to adjust the balance between cooperation and competition, which is typical when learning rules in GCCL. As a result, the obtained rules where quite specific and the performance of the behavior was not comparable to other proposals based on expert knowledge.
@cite_43
1594098193 2113834117 2096145798 2607119937
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