Robot motion classification from the standpoint of learning control

A case study for learning behaviors in mobile robotics by evolutionary fuzzy systems

“…And, what kind of fuzzy controller is the most adequate? The most popular groups of learning methodologies for fuzzy controllers in robotics are evolutionary algorithms (Dahl & Giraud-Carrier, 2004;Gu, Hu, Reynolds, & Tsang, 2003;Hagras, Callaghan, & Collin, 2004;Izumi, Watanabe, & Jin, 1999;Katagami & Yamada, 2000;Mucientes, Moreno, Bugarín, & Barro, 2006;Mucientes, Moreno, Bugarín, & Barro, 2007;Oh & Barlow, 2004;Yamada, 2005), neural networks (Hui, Mahendar, & Pratihar, 2006;Shiah & Young, 2004) and reinforcement learning (Beom & Cho, 1995;Bonarini, 1997;Gu, Hu, & Spacek, 2003;Kalmár, Szepesvári, & Lörincz, 1998;Lin, 2003;Takahashi & Asada, 2003;Thongchai, 2002;Wang, Huber, Papudesi, & Cook, 2003;Zhou, 2002). Also, combinations of them, like neural networks and evolutionary algorithms (Berlanga, Sanchis, Isasi, & Molina, 2000;Chen & Chiang, 2004;Floreano & Mondada, 1998;Lee & Zhang, 2000;Miglino, Lund, & Nolfi, 1995;Nelson, Grant, Barlow, & White, 2003;Tuci, Quinn, & Harvey, 2003), have been successfully applied.…”