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Inácio Fonseca

dblp:132/5060 · DBLP profile ↗
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3ranked-venue papers
1as first author
0since 2021 · last 1998
0000-0003-0167-7489ORCID · corroborated

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

Artificial intelligence and machine learning · 2 · 1 first-authorSystems, architecture and hardware · 2 · 1 first-authorApplied, interdisciplinary, general and emerging computing · 1

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
2 papers
Robot navigation and mapping · 63% Motion planning and robot control · 37%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot navigation and mapping › target tracking
visual pursuit
0.021998
Simulating pursuit with machine experiments with robots and artificial vision · IEEE Trans. Robotics Autom. 1998
Simulating Pursuit with Machines Experiments with Robots and Artificial Vision · ICRA 1995
Robotics › Motion planning and robot control › robot control › sensor-based control
visual servoing
0.021998
Simulating pursuit with machine experiments with robots and artificial vision · IEEE Trans. Robotics Autom. 1998
Simulating Pursuit with Machines Experiments with Robots and Artificial Vision · ICRA 1995
Robotics › Robot navigation and mapping
visual navigation
0.011998
Simulating pursuit with machine experiments with robots and artificial vision · IEEE Trans. Robotics Autom. 1998
Robotics › Robot navigation and mapping
active vision
0.011995
Simulating Pursuit with Machines Experiments with Robots and Artificial Vision · ICRA 1995

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

visual feedback control · 0.0visual gaze holding · 0.0active vision · 0.0trajectory control · 0.0gaze holding · 0.0
YearPublicationVenuePosition
1998 Exploring spherical image properties for robot navigation
abstract
Addresses the problem of performing navigation with a mobile robot using active vision exploring the image sphere properties in a stereo divergent configuration. The navigational process is supported by the control of the robot's steering and forward movements just using visual information as feedback. The steering control solution is based on the difference between signals of visual motion flow computed in images on different positions of a virtual image sphere. The majority of solutions based on motion flow and proposed until now, were usually very unstable because they normally compute other parameters from the motion flow. In our case the control is based directly on the different between motion flow signals on different images. Those multiple images are obtained by small mirrors, that simulate cameras positioned in different positions on the image sphere. The control algorithm described in this work is based on a discrete-event approach to generate a control feedback signal for navigation of an autonomous robot with an active vision system.
Inácio Fonseca, Jorge Dias 0001
IROS1
1998 Simulating pursuit with machine experiments with robots and artificial vision
abstract
This article describes one solution for the problem of pursuit of objects moving on a plane by using a mobile robot and an active vision system. The solution deals with the interaction of different control systems using visual feedback and it is accomplished by the implementation of a visual gaze holding process interacting cooperatively with the control of the trajectory of a mobile robot. These two systems are integrated to follow a moving object at constant distance and orientation with respect to the mobile robot. The orientation and the position of the active vision system running a gaze holding process give the feedback signals to the control used to pursuit the target in real-time. The paper addresses the problems of visual fixation, visual smooth pursuit, navigation using visual feedback and compensation for system's movements. The algorithms for visual processing and control are described in the article. The mechanisms of cooperation between the different control and visual algorithms are also described. The final solution is a system able to operate at approximately human walking rates as the experimental results show in the paper.
Jorge Dias 0001, Carlos Paredes, Inácio Fonseca, Helder Araújo, Jorge P. Batista, Aníbal T. de Almeida
IEEE Trans. Robotics Autom.3
1995 Simulating Pursuit with Machines Experiments with Robots and Artificial Vision
abstract
This article describes one solution for the problem of pursuit by using a mobile robot and an active vision system. The solution deals with the interaction of different control systems using visual feedback and it is accomplished by the implementation of a visual gaze holding process interacting cooperatively with the control of the trajectory of a mobile robot. These two systems are integrated to follow a moving object at constant distance and orientation with respect to the mobile robot. The orientation and the position of the active vision system running a gaze holding process give the feedback signals to the control used to pursuit the target in real-time. The mechanisms of cooperation between the different control and visual algorithms are described. The final solution is a system able to operate at approximately human walking rates.
Jorge Dias 0001, Carlos Paredes, Inácio Fonseca, Helder Araújo, Jorge P. Batista, Aníbal T. de Almeida
ICRA3