Demo Data from EDBT 2026 / reviewers in the wild / expert
Adam J. Rutkowski
dblp:95/3314
· DBLP profile ↗
4ranked-venue papers
3as first author
0since 2021 · last 2007
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 4 · 3 first-authorSystems, architecture and hardware · 4 · 3 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
4 papers |
Robot navigation and mapping · 70% Legged, aerial and field robots · 22% Motion planning and robot control · 8% |
Topics — the 6 heaviest of 7, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Robot navigation and mapping › target tracking
plume tracking |
0.2 | 3 | 2006 | Simulated Odor Tracking in a Plane Normal to the Wind Direction · ICRA 2006 Moth-Inspired Plume Tracking Strategies In Three-Dimensions · ICRA 2005 A Robotic Platform for Testing Moth-inspired Plume Tracking Strategies · ICRA 2004 |
Robotics › Robot navigation and mapping › source localization
odor source localization |
0.1 | 2 | 2007 | A Sensor Fusion Approach to Odor Source Localization Inspired by the Pheromone Tracking Behavior of Moths · ICRA 2007 Moth-Inspired Plume Tracking Strategies In Three-Dimensions · ICRA 2005 |
Robotics › Legged, aerial and field robots › bio-inspired robot
bio-inspired robot control |
0.1 | 1 | 2005 | Moth-Inspired Plume Tracking Strategies In Three-Dimensions · ICRA 2005 |
Robotics › Legged, aerial and field robots
aerial robots |
0.0 | 1 | 2007 | A Sensor Fusion Approach to Odor Source Localization Inspired by the Pheromone Tracking Behavior of Moths · ICRA 2007 |
Robotics › Motion planning and robot control
robot control |
0.0 | 1 | 2005 | Moth-Inspired Plume Tracking Strategies In Three-Dimensions · ICRA 2005 |
Robotics › Legged, aerial and field robots
field robotics |
0.0 | 1 | 2004 | A Robotic Platform for Testing Moth-inspired Plume Tracking Strategies · ICRA 2004 |
Methods — techniques the papers use, named apart from their topics
visual sensor · 0.1sensor fusion · 0.1odour sensor · 0.1airspeed sensor · 0.1simulation · 0.1inter-turn timer · 0.1modified counter-turner algorithm · 0.1counter-turner algorithm · 0.1moth-inspired strategy · 0.0ionized air plume · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2007 | A Sensor Fusion Approach to Odor Source Localization Inspired by the Pheromone Tracking Behavior of MothsabstractAn approach to odor source localization with an aerial vehicle using the fusion of odor sensors, visual sensors, and airspeed sensors is presented. The motion of the tracking vehicle is decomposed into two components - a component normal to the wind direction and a component tangential to the wind direction. The tangential component is controlled with a strategy that moves upwind when odor is detected and moves gradually downwind when odor is lost. The normal component of velocity is controlled by two different algorithms. The first algorithm controls the rate of turning in the plane normal to the wind direction as a function of concentration. The second algorithm controls the rate of turning and the magnitude of the normal component of velocity as a function of the time derivative of concentration. Both algorithms display the potential to declare the location of an odor source in a three-dimensional space. Adam J. Rutkowski, Roger D. Quinn, Mark A. Willis |
ICRA | 1 |
| 2006 | Simulated Odor Tracking in a Plane Normal to the Wind DirectionabstractA new strategy is developed for tracking an odor plume in an environment where wind is present. The strategy is inspired by the mechanisms that animals use to orient to stimuli. A male moth counter-turns horizontally and vertically across the wind while tracking a pheromone plume. The inter-turn duration, or the amount of time between turns, is about 500 milliseconds on average in both directions. This observation has lead to the development of odor tracking algorithms that use inter-turn timers to control when to turn in both directions. The algorithm presented in this work takes a different approach. The new algorithm controls the rate of turning of the tracking vehicle in a plane normal to the wind direction. Simulation results demonstrate that counter-turning can be achieved in both directions without the use of inter-turn timers. Also, the estimated odor source location approaches the true source location as time progresses Adam J. Rutkowski, Mark A. Willis, Roger D. Quinn |
ICRA | 1 |
| 2005 | Moth-Inspired Plume Tracking Strategies In Three-DimensionsabstractTwo simple three-dimensional moth inspired odor tracking algorithms, Counter-turner and Modified counter-turner, were tested on a robotic platform. The Counter-turner uses the plume edge to modify the timing of the crosswind movements, while the Modified counter-turner uses the plume centerline. Both algorithms shows some success in tracking the plume to it’s source. In addition, flight tracks show promise in mimicking the flight tracks observed in biological experiments with the moth Manduca. Sexta. Shaun Edwards, Adam J. Rutkowski, Roger D. Quinn, Mark A. Willis |
ICRA | 2 |
| 2004 | A Robotic Platform for Testing Moth-inspired Plume Tracking StrategiesabstractA mobile autonomous robot capable of tracking an odor plume to its source can be used to locate hazardous material spills or leaks. To test plume tracking strategies in a laboratory environment, a robotic platform consisting of a linear Cartesian robotic gantry mounted inside a wind tunnel with a mobile floor has been designed. A plume of ionized air is created by an ion detector installed in the wind tunnel. A two-dimensional plume tracking strategy based on the behavior of the tobacco hornworm moth Manduca sexta is implemented and tested. It is discovered that a plume tracking algorithm can be implemented with the robotic platform almost as easily as in simulation. Adam J. Rutkowski, Shaun Edwards, Mark A. Willis, Roger D. Quinn, Greg C. Causey |
ICRA | 1 |