EDBT 2026 Demo / reviewers in the wild / expert
A. Martinengo
dblp:93/5157
· DBLP profile ↗
2ranked-venue papers
2as first author
0since 2021 · last 1994
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 2 first-authorSystems, architecture and hardware · 2 · 2 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 · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Motion planning and robot control › robot control
hierarchical control |
0.0 | 1 | 1994 | Complex Tasks and Control Strategies of Robots · ICRA 1994 |
Robotics › Motion planning and robot control › path planning
maze navigation |
0.0 | 1 | 1994 | Complex Tasks and Control Strategies of Robots · ICRA 1994 |
Robotics › Motion planning and robot control
motion planning |
0.0 | 1 | 1994 | Complex Tasks and Control Strategies of Robots · ICRA 1994 |
Robotics › Motion planning and robot control
robot control architecture |
0.0 | 1 | 1994 | Complex Tasks and Control Strategies of Robots · ICRA 1994 |
Methods — techniques the papers use, named apart from their topics
finite state automata · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1994 | Complex Tasks and Control Strategies of RobotsabstractThe robot moves in a simplified world and performs tasks of increasing complexity, such as the exploration of a maze, the recovery of its structure and the planning of some nearly optimal trajectories through the maze. There are three layers of robot motion control: the lowest layer is essentially an optomotor reflex; the second layer has a short term memory and operates when the visual information is not complete; the third layer has a long term memory and reasoning capabilities. The vision and motor systems are assumed to be finite state automata and the control architecture is based on transitions between states of these automata. The intelligent behavior of the robot is primarily caused by the memory and the capability to create simple and useful representations of the world.> A. Martinengo, Marco Campani, Vincent Torre |
ICRA | 1 |
| 1994 | Artificial systems and complex behavioursabstractThis paper describes some experiments on control strategies of a mobile robot. The robot has a simple vision system and moves in a simplified world, but the controlling architecture performs tasks of increasing complexity, such as the exploration of a maze, the recovery of its structure and the planning of some nearly optimal trajectories through the maze. There are three layers of control of robot motion: the lowest layer analyses images, controls the speed of robot wheels and is essentially an optomotor reflex; the second layer has a short term memory and operates when the visual information is not complete or when the robot has lost its way; the third layer has a long term memory and reasoning capabilities, monitors the achievement of robot tasks and schedules the timing of completion of the different tasks. The vision and motor systems and other supervising units are assumed to be finite state automata and the control architecture is based on transitions between states of these automata. The control system is flexible and new capabilities are easily acquired by simply introducing new states. The intelligent behaviour of this artificial system is primarily caused by the memory and the capability to create simple and useful representations of the world.> A. Martinengo, Marco Campani, Vincent Torre |
IROS | 1 |