| Summary: | The aim of this paper is to improve the dynamic window approach algorithm for mobile robots by implementing a prediction window with a fuzzy inference system to adapt to fixed parameters, depending on the surrounding conditions. The first implementation shows the advantage of the prediction step in terms of optimizing the path selection. The second improvement uses fuzzy inference to optimize each of the fixed parameters’ values to increase the algorithm performance. Nevertheless, a simple fuzzy inference system (FIS) was not used for this particular study; instead, an artificial neuro-fuzzy inference system (ANFIS) was used, thus making it possible to develop a FIS system with a back-propagation technique. Each parameter would have a particular ANFIS, in order to modify the <inline-formula> <math display="inline"> <semantics> <mrow> <msub> <mi mathvariant="sans-serif">α</mi> <mi mathvariant="normal">D</mi> </msub> </mrow> </semantics> </math> </inline-formula>, <inline-formula> <math display="inline"> <semantics> <mrow> <msub> <mi mathvariant="sans-serif">β</mi> <mi mathvariant="normal">D</mi> </msub> </mrow> </semantics> </math> </inline-formula>, and <inline-formula> <math display="inline"> <semantics> <mrow> <msub> <mi mathvariant="sans-serif">γ</mi> <mi mathvariant="normal">D</mi> </msub> </mrow> </semantics> </math> </inline-formula> parameters individually. At the end of the article, different scenarios are analyzed to determine whether the developments in this article have improved the DWA behavior. The results show that the prediction step and ANFIS adapt DWA performance by optimizing the path resolution.
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