Institution profile

Aichi Institute of Technology

Academic institutionasia · jp
Official website
Research library15linked papers
Opportunities0open roles
Selected work

Representative Papers

Multifunctional Locomotion Control of Multi-Jointed BURs with Swimming and gait Capabilities

Sep 29, 2026

This study addresses the limitations of traditional rule-based control in acquiring novel behaviors and the difficulty of achieving multimodal locomotion with a single mechanical structure. To overcome these challenges, this work proposes a nonlinear control method based on potential functions. By integrating multi-joint mechanisms, potential function control algorithms, and multisensory technologies, the approach enables a four-legged fin structure to autonomously switch between swimming and walking gaits. This framework eliminates reliance on predefined rules, facilitating the emergence and nonlinear transition of multimodal behaviors within a unified mechanical architecture. Both simulation and physical experiments demonstrate that the system successfully achieves smooth transitions between multiple locomotion modes, significantly enhancing environmental adaptability for underwater exploration.

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Path Following Control System of Line-of-Sight Guidance for Robotic Dolphin with Multi-Link Mechanism in Underwater Simulator

May 24, 2026

This study addresses the challenge of adapting existing path-following methods to the complex dynamics of multi-link biomimetic underwater robots, which has been hindered by insufficient simulation validation. Focusing on a biomimetic dolphin robot, this work proposes the first line-of-sight (LOS) guidance-based path-following framework specifically designed for multi-link architectures. By integrating an accurate dynamic model with a high-fidelity underwater simulation environment, the approach enables efficient tuning of control parameters and thorough validation of the control strategy. The proposed method not only fills a critical gap in dedicated path-following control for multi-link biomimetic underwater vehicles but also demonstrates its effectiveness and feasibility through comprehensive simulations in a realistic virtual setting.

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Efficient event-driven retrieval in high-capacity kernel Hopfield networks

May 07, 2026

This work addresses the challenge of deploying high-capacity kernel Hopfield networks on event-driven neuromorphic hardware, which is hindered by their reliance on synchronous updates. The study investigates the asynchronous retrieval dynamics of kernel logistic regression–based Hopfield networks and demonstrates that, through careful tuning of kernel parameters, asynchronous sequential updates become statistically equivalent to their synchronous counterparts while preserving high recall accuracy. For the first time, it is shown that asynchronous dynamics can operate stably at storage capacities approaching \( P/N \approx 30 \), surpassing the classical Hopfield limit, and exhibit a smooth energy landscape amenable to event-driven computation. The number of state transitions required for convergence scales approximately with the initial Hamming distance, without significant spurious oscillations, enabling efficient and low-power associative memory retrieval.

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Recent publications

Latest Papers

Multifunctional Locomotion Control of Multi-Jointed BURs with Swimming and gait Capabilities

Sep 29, 2026

This study addresses the limitations of traditional rule-based control in acquiring novel behaviors and the difficulty of achieving multimodal locomotion with a single mechanical structure. To overcome these challenges, this work proposes a nonlinear control method based on potential functions. By integrating multi-joint mechanisms, potential function control algorithms, and multisensory technologies, the approach enables a four-legged fin structure to autonomously switch between swimming and walking gaits. This framework eliminates reliance on predefined rules, facilitating the emergence and nonlinear transition of multimodal behaviors within a unified mechanical architecture. Both simulation and physical experiments demonstrate that the system successfully achieves smooth transitions between multiple locomotion modes, significantly enhancing environmental adaptability for underwater exploration.

0 citationsRead paper

Path Following Control System of Line-of-Sight Guidance for Robotic Dolphin with Multi-Link Mechanism in Underwater Simulator

May 24, 2026

This study addresses the challenge of adapting existing path-following methods to the complex dynamics of multi-link biomimetic underwater robots, which has been hindered by insufficient simulation validation. Focusing on a biomimetic dolphin robot, this work proposes the first line-of-sight (LOS) guidance-based path-following framework specifically designed for multi-link architectures. By integrating an accurate dynamic model with a high-fidelity underwater simulation environment, the approach enables efficient tuning of control parameters and thorough validation of the control strategy. The proposed method not only fills a critical gap in dedicated path-following control for multi-link biomimetic underwater vehicles but also demonstrates its effectiveness and feasibility through comprehensive simulations in a realistic virtual setting.

0 citationsRead paper

Efficient event-driven retrieval in high-capacity kernel Hopfield networks

May 07, 2026

This work addresses the challenge of deploying high-capacity kernel Hopfield networks on event-driven neuromorphic hardware, which is hindered by their reliance on synchronous updates. The study investigates the asynchronous retrieval dynamics of kernel logistic regression–based Hopfield networks and demonstrates that, through careful tuning of kernel parameters, asynchronous sequential updates become statistically equivalent to their synchronous counterparts while preserving high recall accuracy. For the first time, it is shown that asynchronous dynamics can operate stably at storage capacities approaching \( P/N \approx 30 \), surpassing the classical Hopfield limit, and exhibit a smooth energy landscape amenable to event-driven computation. The number of state transitions required for convergence scales approximately with the initial Hamming distance, without significant spurious oscillations, enabling efficient and low-power associative memory retrieval.

0 citationsRead paper