Demo Data from VLDB 2026 / reviewers in the wild / expert
Koki Aizawa
dblp:277/7008
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Motion planning and robot control › robot control
trajectory tracking |
0.9 | 1 | 2025 | 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 |
0.3 | 1 | 2025 | 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 |
0.3 | 1 | 2025 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Lyapunov-Certified Trajectory Tracking for Mobile Robot With a Tail Wheel: Differential-Flatness and Adaptive Backstepping DesignabstractThis 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 |
ICRA | 3 |