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Koki Aizawa

dblp:277/7008 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Systems, architecture and hardware · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Motion planning and robot control · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control › robot control
trajectory tracking
0.912025
Lyapunov-Certified Trajectory Tracking for Mobile Robot With a Tail Wheel: Differential-Flatness and Adaptive Backstepping Design · ICRA 2025
Robotics › Motion planning and robot control › robot control › adaptive control
adaptive backstepping control
0.312025
Lyapunov-Certified Trajectory Tracking for Mobile Robot With a Tail Wheel: Differential-Flatness and Adaptive Backstepping Design · ICRA 2025
Robotics › Motion planning and robot control › robot control
adaptive control
0.312025
Lyapunov-Certified Trajectory Tracking for Mobile Robot With a Tail Wheel: Differential-Flatness and Adaptive Backstepping Design · ICRA 2025

Methods — techniques the papers use, named apart from their topics

system identification · 0.9lyapunov stability · 0.9differential flatness · 0.9backstepping · 0.9
YearPublicationVenuePosition
2025 Lyapunov-Certified Trajectory Tracking for Mobile Robot With a Tail Wheel: Differential-Flatness and Adaptive Backstepping Design
abstract
This paper proposes a trajectory tracking control law for a mobile robot with two front differential wheels and a tail wheel. The dynamics is given by mimicking Ackerman steering model for the dynamics of position and orientation, associated with the actuator dynamics of the tail wheel's angle modeled by a first-order response with respect to the robot's angular velocity. First we develop a nominal trajectory tracking control law to track a given desired trajectory by applying differential-flatness property of the unicycle model and backstepping approach to handle the actuator dynamics. The effectiveness of the trajectory tracking is demonstrated by conducting hardware robot experiment after performing system identification, which illustrates the superior performance over a benchmark method. The design is also extended to an adaptive tracking control under parameter uncertainty in the tail wheel dynamics through introducing the adaptation law of the parameters, and the performance is demonstrated in numerical simulation.
Yuta Nishizawa, Shumon Koga, Koki Aizawa, Yuji Yasui
ICRA3