Demo Data from EDBT 2026 / reviewers in the wild / expert
Daniil Kirsanov
dblp:285/2948
· DBLP profile ↗
2ranked-venue papers
0as first author
1since 2021 · last 2021
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 1 since 2021Systems, architecture and hardware · 2 · 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 › trajectory planning
time-optimal trajectory planning |
0.5 | 1 | 2021 | On Smooth Time-Optimal Trajectory Planning in Twisted String Actuators · ICRA 2021 |
Robotics › Motion planning and robot control
trajectory optimization |
0.5 | 1 | 2021 | On Smooth Time-Optimal Trajectory Planning in Twisted String Actuators · ICRA 2021 |
Robotics › Motion planning and robot control
robot control |
0.1 | 1 | 2021 | On Smooth Time-Optimal Trajectory Planning in Twisted String Actuators · ICRA 2021 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | On Smooth Time-Optimal Trajectory Planning in Twisted String Actuators
Simeon Nedelchev, Daniil Kirsanov, Igor Gaponov, HyeonSeok Seong, Jee-Hwan Ryu |
ICRA | 2 |
| 2020 | IMU-based Parameter Identification and Position Estimation in Twisted String ActuatorsabstractThis study proposes a technique to estimate the output state of twisted string actuators (TSAs) based on payload's acceleration measurements. We outline differential kinematics relationships of the actuator, re-formulate these into a nonlinear parameter identification problem and then apply linearization techniques to efficiently solve it as a quadratic program. Using accurate estimates of string parameters obtained with the proposed method, we can predict TSA position with sub-millimeter accuracy via conventional kinematic relationships. In addition, the proposed method supports accurate estimation under varying operating conditions, unpredictable perturbations, and poorly-excited trajectories. This technique can be employed to improve the accuracy of trajectory tracking when the use of direct position measurements is challenging, with the list of potential applications including flexible and soft robots, long-span cable robots, multi-DOF joints and others. Simeon Nedelchev, Daniil Kirsanov, Igor Gaponov |
IROS | 2 |