Stephane Ngnepiepaye Wembe

dblp:401/8599 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2025
—ORCID · unresolved

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

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Systems, architecture and hardware · 1 · 1 first-author · 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 · 62% Legged, aerial and field robots · 38%

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

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control
path following
0.912025
A Novel Control Strategy for Offset Points Tracking in the Context of Agricultural Robotics · ICRA 2025
Robotics › Legged, aerial and field robots › field robotics
agricultural robotics
0.312025
A Novel Control Strategy for Offset Points Tracking in the Context of Agricultural Robotics · ICRA 2025
Robotics › Legged, aerial and field robots
field robotics
0.312025
A Novel Control Strategy for Offset Points Tracking in the Context of Agricultural Robotics · ICRA 2025

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

backstepping control · 0.9
YearPublicationVenuePosition
2025 A Novel Control Strategy for Offset Points Tracking in the Context of Agricultural Robotics
abstract
In this paper, we present a novel method to control a rigidly connected location on the vehicle, such as a point on the implement in case of agricultural tasks. Agricultural robots are transforming modern farming by enabling precise and efficient operations, replacing humans in arduous tasks while reducing the use of chemicals. Traditionally, path-following algorithms are designed to guide the vehicle's center along a predefined trajectory. However, since the actual agronomic task is performed by the implement, it is essential to control a specific point on the implement itself rather than the vehicle's center. As such, we present in this paper two approaches for achieving the control of an offset point on the robot. The first approach adapts existing control laws, initially intended for the rear axle's midpoint, to manage the desired lateral deviation. The second approach employs backstepping control techniques to create a control law that directly targets the implement. We conduct realworld experiments, highlighting the limitations of traditional approaches for offset point control, and demonstrating the strengths and weaknesses of the proposed methods.
Stephane Ngnepiepaye Wembe, Vincent Rousseau, Johann Laconte, Roland Lenain
ICRA1