M. Kanaya

Papers

Total Citations

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About

M. Kanaya is a pioneering researcher in bio-inspired robotics and neuromorphic computing, whose work bridges analog neural networks and autonomous navigation. Kanaya’s primary contributions lie in developing path planning and shortest-path search algorithms for robots using analog resistive networks and cellular neural networks (CNNs). In their most-cited work, “Shortest path searching for robot walking using an analog resistive network” (2002, 20 citations), Kanaya introduced a local current comparison method inspired by retinal information processing, enabling efficient real-time pathfinding for walking robots through analog dynamics. This approach demonstrated how biological principles could be harnessed for robotic control. Earlier, in “Path planning method for multi-robots using a cellular neural network” (1998, 6 citations), Kanaya proposed a novel CNN-based system combining a resistive grid, a competitive network for maximum detection, and a digital network for path searches, allowing multiple autonomous robots to coordinate navigation. Though citation counts are modest, Kanaya’s work is notable for its early integration of analog computing and neural architectures into robotics, predating later advances in neuromorphic engineering. This research remains relevant for students exploring energy-efficient, biologically plausible approaches to multi-agent systems and real-time robot navigation.