Demo Data from EDBT 2026 / reviewers in the wild / expert
Toshiya Maki
dblp:272/5418
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2020
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-authorSystems, architecture and hardware · 1 · 1 first-author
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 · 83% Legged, aerial and field robots · 17% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Motion planning and robot control › robot control
adaptive control |
0.4 | 1 | 2020 | Model Reference Adaptive Control of Multirotor for Missions with Dynamic Change of Payloads During Flight · ICRA 2020 |
Robotics › Motion planning and robot control › robot control › adaptive control
model reference adaptive control |
0.4 | 1 | 2020 | Model Reference Adaptive Control of Multirotor for Missions with Dynamic Change of Payloads During Flight · ICRA 2020 |
Robotics › Motion planning and robot control
robot control |
0.4 | 1 | 2020 | Model Reference Adaptive Control of Multirotor for Missions with Dynamic Change of Payloads During Flight · ICRA 2020 |
Robotics › Legged, aerial and field robots
aerial robots |
0.1 | 1 | 2020 | Model Reference Adaptive Control of Multirotor for Missions with Dynamic Change of Payloads During Flight · ICRA 2020 |
Robotics › Legged, aerial and field robots › aerial robots › rotorcraft
multirotor |
0.1 | 1 | 2020 | Model Reference Adaptive Control of Multirotor for Missions with Dynamic Change of Payloads During Flight · ICRA 2020 |
Methods — techniques the papers use, named apart from their topics
model reference adaptive control · 0.4MIMO control · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | Model Reference Adaptive Control of Multirotor for Missions with Dynamic Change of Payloads During FlightabstractCarrying payloads in air is a major mission for multirotor aerial robot. However, the presence of payloads on multirotor aerial robot has a risk of degrading the performance of the flight controller. This concern becomes obvious especially when carrying objects not securely attached to the body or performing aerial manipulation. Therefore, controller with the ability to adapt itself to the effects of payloads on flight stability is needed. This paper proposes a novel nonlinear multiple-input and multiple-output (MIMO) model reference adaptive control (MRAC) system for attitude control of multirotor aerial robots which can dynamically compensate change in the position of center of gravity and inertia caused by payloads. Stability and robustness of the controller are experimentally confirmed in quadrotor and transformable multirotor, and experiments modeling practical applications are conducted for each aerial robot system, proving the utility of the controller. Toshiya Maki, Moju Zhao, Fan Shi 0002, Kei Okada, Masayuki Inaba |
ICRA | 1 |