Demo Data from EDBT 2026 / reviewers in the wild / expert
Justin Page
dblp:416/6820
· 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 |
Robot manipulation · 50% Motion planning and robot control · 50% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Motion planning and robot control › robot control
force control |
0.9 | 1 | 2025 | A Tugging Controller that Maximizes Lateral Resistive Force by Mounding Sandy Terrain · ICRA 2025 |
Methods — techniques the papers use, named apart from their topics
gradient tracking · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | A Tugging Controller that Maximizes Lateral Resistive Force by Mounding Sandy TerrainabstractSandy environments present challenges for robotic space rovers and systems due to reduced traction, limiting mobility and tugging force. This paper presents an anchoring method that utilizes a winching system to create a sand mound in front of a mobile agent dragged through the media. The proposed controller is designed to consistently achieve realtime capture of close-to-maximal lateral sand mound resistive force, even when applied to varied uneven terrains, like holes or waves. Notably, tugging is non-reversible, so suitable peaks should be captured before breakdown and without necessarily knowing the global optimum a priori. The controller logic tracks both tugging force and agent pitch gradients to detect terrain conditions and peak force trends. Results show that the controller captures an average 92 % of the maximum forces, within the previously winched workspace tested, across three different granular media with four varying structured terrain features. The controller achieves higher resistive force peaks on terrains with geometric features, as opposed to flat sand. We conclude that sand mounding through tugging is a viable means to generate robotic resistive forces for unknown sandy terrains, a simple yet effective anchoring mechanism. Deaho Moon, Chris Huang, Justin Page, Hannah Stuart |
ICRA | 3 |