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Marcos P. Souza

dblp:274/9619 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none

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

Artificial intelligence and machine learning · 1Systems, architecture and hardware · 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
1 paper
Reinforcement learning · 50% Motion planning and robot control · 50%

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

TopicWeightPapersLastEvidence papers
Machine learning › Reinforcement learning
exploration		 0.412020
Exploration of 3D terrains using potential fields with elevation-based local distortions · ICRA 2020
Robotics › Motion planning and robot control
motion planning		 0.412020
Exploration of 3D terrains using potential fields with elevation-based local distortions · ICRA 2020
Robotics › Motion planning and robot control › path planning
potential field planning		 0.412020
Exploration of 3D terrains using potential fields with elevation-based local distortions · ICRA 2020
Machine learning › Reinforcement learning › exploration › autonomous exploration › mobile robot exploration
terrain exploration		 0.412020
Exploration of 3D terrains using potential fields with elevation-based local distortions · ICRA 2020

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

potential fields with local distortions · 0.4
YearPublicationVenuePosition
2020 Exploration of 3D terrains using potential fields with elevation-based local distortions
abstract
Mobile robots can be used in numerous outdoor tasks such as patrolling, delivery and military applications. In order to deploy mobile robots in this kind of environment, where there are different challenges like slopes, elevations, or even holes, they should be able to detect such challenges and determine the best path to accomplish their tasks. In this paper, we are proposing an exploration approach based on potential fields with local distortions, in which we define preferences in uneven terrains to avoid high declivity regions without compromising the best path. The approach was implemented and tested in simulated environments, considering a ground robot embedded with two 2D LIDAR sensors, and the experiments demonstrated the efficiency of our method.
Renan Maffei, Marcos P. Souza, Mathias Mantelli, Diego Pittol, Mariana Luderitz Kolberg, Vitor Augusto Machado Jorge
 ICRA2